Scientific Publications Directory

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Ikhsan, Muhammad Dirga Juang; As’ad, Ihwana; Darwis, Herdianti, Program Studi Teknik Informatika, Fakultas Ilmu Komputer, Universitas Muslim Indonesia, Jl. Urip Sumoharjo Km.05, Kota Makassar, Sulawesi Selatan, Indonesia

Abstract

Stunting merupakan masalah kesehatan masyarakat yang memerlukan pemahaman terhadap variasi profil balita terdampak. Penelitian ini bertujuan melakukan stratifikasi profil antropometri balita stunting di wilayah kerja Puskesmas Pamboang menggunakan K-Means dengan validasi Silhouette Score. Penelitian menggunakan pendekatan kuantitatif komputasional terhadap data sekunder antropometri balita stunting tahun 2025. Dari 826 data awal, satu pencilan teknis dikeluarkan sehingga 825 data dianalisis. Fitur klasterisasi meliputi umur dalam bulan, berat badan, dan Height-for-Age Z-Score(HAZ), sedangkan jenis kelamin, tinggi badan, kategori TB/U, desa/kelurahan, dan posyandu digunakan untuk interpretasi. Jumlah klaster optimal ditentukan dengan menguji K = 2 hingga K = 10. Hasil tertinggi diperoleh pada K = 3 dengan Silhouette Score0,4011. Klasterisasi menghasilkan tiga profil, yaitu balita dengan gangguan pertumbuhan paling berat, balita stunting usia lebih tua dengan ukuran tubuh absolut lebih besar, dan balita stunting usia lebih muda dengan keparahan relatif lebih ringan. Sebaran wilayah dianalisis pada 10 desa/kelurahan dengan jumlah balita stunting tertinggi. Temuan ini menunjukkan bahwa K-Meansdapat mengolah data antropometri rutin menjadi informasi terstruktur untuk mendukung pemantauan pertumbuhan berbasis profil anak dan wilayah layanan.

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Sunggingwati, Dyah; Haviluddin, Universitas Mulawarman, Samarinda, Indonesia; Purnawansyah, Universitas Muslim Indonesia, Makassar, Indonesia; Trahutomo, Dinnuhoni, Universitas Mulawarman, Samarinda, Indonesia

Abstract

Critical literacy within Life-Based Learning (LBL) serves as a transformative educational approach that empowers students to engage critically with texts, media, and societal narratives, examining the ideologies, power dynamics, and cultural contexts embedded within them. This chapter examines the role of critical literacy in LBL, highlighting its significance in enabling students to understand and critically engage with the broader social structures that shape their lives. It discusses the expansion of critical literacy beyond traditional reading and writing to include digital and media literacy, driven by the technological advancements of the twenty-first century. The integration of technology in education facilitates new forms of content creation and analysis, enabling students to engage with and deconstruct media actively. The chapter also highlights how LBL fosters real-world application of critical literacy through projects that tackle local and global issues, such as environmental sustainability, social justice, and media representation. Through activities like media analysis, community research, and role-playing, students are encouraged to explore multiple perspectives and develop an understanding of power and equity. The chapter concludes with the assertion that critical literacy within LBL not only prepares students to analyze and critique information but also empowers them to become active, informed citizens capable of addressing complex societal challenges.

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Ambas, Muhammad Alif; Atmajaya, Dedy; Manga’, Abdul Rachman, Universitas Muslim Indonesia

Abstract

Pemberian pakan ayam broiler secara manual kerap dianggap tidak efisien karena memerlukan waktu, tenaga, dan berpotensi mengganggu konsistensi jadwal pemberian pakan. Penelitian ini bertujuan merancang sistem otomatisasi pemberian pakan berbasis mikrokontroler Arduino ATmega328 SMD yang bekerja secara terjadwal untuk meningkatkan efisiensi operasional peternakan. Sistem ini dilengkapi dengan sensor ultrasonic untuk memantau ketinggian pakan, load cell untuk menimbang berat pakan, modul Real Time Clock (RTC) untuk mengatur waktu, LCD sebagai penampil informasi, serta motor servo untuk mengatur buka-tutup katup pakan. Pemberian pakan dilakukan dua kali sehari, yakni pukul 08.00 dan 17.00, dengan takaran antara 315 hingga 393 gram per sesi. Simulasi pengujian menunjukkan bahwa semua komponen berfungsi optimal dalam skenario otomatis. Hasil perancangan sistem membuktikan bahwa sistem bisa digunakan dan diimplementasikan bagi peternak ayam untuk mengurangi beban kerja peternak, menjaga konsistensi waktu pemberian pakan. Dengan demikian, rancangan ini menawarkan solusi efektif dan praktis dalam manajemen pemberian pakan pada ayam broiler secara otomatis dengan pengaturan waktu terjadwal.

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Yusuf, Dwiky Aries Nugraha; Manga’, Abdul Rachman, Universitas Muslim Indonesia

Abstract

Perpustakaan berperan penting dalam mendukung pendidikan dan meningkatkan literasi masyarakat. Perkembangan teknologi informasi mendorong transformasi perpustakaan konvensional menjadi perpustakaan digital untuk memenuhi kebutuhan masyarakat modern yang mengutamakan efisiensi dan kemudahan akses informasi. Namun, perpustakaan digital menghadapi tantangan seperti keterbatasan dalam sistem pencarian, pengelolaan ulasan, dan penyediaan rekomendasi yang relevan. Permasalahan ini menunjukkan perlunya pengembangan sistem informasi berbasis teknologi yang lebih adaptif dan responsif terhadap kebutuhan pengguna. Penelitian ini bertujuan untuk merancang sistem informasi perpustakaan digital berbasis Natural Language Processing (NLP) yang mampu meningkatkan kualitas layanan perpustakaan dengan fitur rekomendasi bacaan buku digital. Teknologi NLP digunakan untuk menganalisis sentimen ulasan/komentar pengguna, meningkatkan akurasi pencarian, dan menyediakan rekomendasi buku sesuai preferensi. Proses pengembangan sistem mencakup pengumpulan data melalui web scraping, pelabelan, pre-processing, pembagian data, dan analisis model NLP. Evaluasi dilakukan untuk mengukur akurasi, presisi, dan recall. Hasil perancangan penelitian menunjukkan bahwa penerapan NLP dapat meningkatkan efisiensi sistem perpustakaan digital, baik dalam memahami kebutuhan pengguna maupun dalam menyajikan informasi. Sistem informasi perpustakaan digital tidak hanya membantu pengguna dalam menemukan bahan bacaan yang sesuai kebutuhan, tetapi juga memperkuat peran perpustakaan sebagai pusat literasi adaptif terhadap perkembangan teknologi. Dengan demikian, perpustakaan digital berbasis NLP menjadi solusi inovatif untuk meningkatkan aksesibilitas dan pengalaman pengguna secara signifikan

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L, Harlinda; Manga’, Abdul Rachman; Satra, Ramdan, Universitas Muslim Indonesia

Abstract

This study applies Artificial Intelligence (AI) using the Long Short-Term Memory (LSTM) algorithm to predict daily sales at the FIKOM-UMI Minimarket. Sales data from 2023 to 2024 involving 82 items were used and processed into a time series format. Five LSTM architectural scenarios were tested, including baseline, bigger model, lightweight, bidirectional LSTM, and single-layer medium, to identify the most effective model in capturing sales patterns. The data underwent preprocessing stages, including daily aggregation, reindexing to fill missing dates, and normalization using MinMaxScaler before being transformed into sequences with a 30-day time step. Model performance was evaluated using MSE, RMSE, MAPE, and accuracy metrics. The results show that the Bidirectional LSTM (Scenario 4) achieved the best performance, with the lowest MAPE of 19.43% and the highest accuracy of 80.57%. The model successfully generated stable predictions for 7-day and 30-day forecasting with a range of 153–155 units per day, indicating consistent sales patterns. Testing on the top 10 best-selling items showed significant performance variation, with GARUDA ROSTA BWNG 100 Gram achieving the highest accuracy (46.97%), while aoka rasa pandan showed the lowest performance (-76.05%). These findings demonstrate that the LSTM model can be effectively applied for sales prediction in campus minimarkets; however, a hybrid approach with product segmentation is recommended to optimize inventory management across product categories with varying levels of predictability

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Manga, Abdul Rachman; Handayani, Anik Nur; Herwanto, Heru Wahyu; Asmara, Rosa Andrie; Raja, Roesman Ridwan

Abstract

Identification and classification of species are crucial for maintaining genetic diversity and supporting sustainable agricultural practices. The Toraja Buffalo, a unique type of buffalo in Indonesia, holds high cultural and economic value. Accurate classification of this species is essential to preserving genetic resources and improving breeding programs. Previous studies using single classification methods have shown limitations in complex cases, such as the Toraja Buffalo, which has numerous physiological characteristics, including body size, head, horns, tail, and eyes. The purpose of this study is to evaluate and compare the performance of single-classification and multi-category methods for identifying Toraja Buffalo. Several algorithms, including K-Nearest Neighbors (K-NN), Random Forest, Support Vector Machine (SVM), and Naive Bayes, were tested using Local Binary Pattern (LBP) for feature extraction. Decision Tree and others were observed, showing 85.83% accuracy in single-input, while multi-input accuracy reached 92.08%. The multi-input approach consistently improved performance across all algorithms. Multi-input classifiers significantly outperformed single-feature methods, with Random Forest being the most efficient algorithm. Future research could incorporate additional variables, such as skin color or genetic profiles, to further enhance accuracy.

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Rusman, Muh; Satra, Ramdan; Azis, Huzain, Universitas Muslim Indonesia

Abstract

The use of the Telegram application as a communication medium allows the exchange of image files that have the potential to become digital evidence, but in some cases the perpetrator deletes the files to eliminate digital traces. This study aims to analyze and prove the ability of the digital forensic process in finding and recovering deleted JPG image files on the Android-based Telegram application so that they can be used as valid digital evidence. The research method refers to the NIST SP 800-86 standard which includes the stages of collection, examination, analysis, and reporting, with the data acquisition process using the logical acquisition method through the Android Debug Bridge (ADB) without root access as well as analysis using FTK Imager and verification of data integrity through SHA-256 hash values. The results of the study show that JPG image files that have been deleted from the Telegram application can still be found and recovered from the internal storage media of Android devices in intact condition, have relevant metadata, and consistent hash values, so they are declared valid as digital evidence

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Azis, Huzain; Ahmad, Randika Sakha Ramadani Putra; Syahriawan, Irawati, Universitas Muslim Indonesia

Abstract

This study presents the implementation of an AI agent chatbot to support journal information services on the Open Journal Systems (OJS) platform and the Telegram messaging application using a dynamic knowledge base sourced from Google Drive. The chatbot provides automated responses to user inquiries related to journal scope, publication fees, submission procedures, and review timelines, while allowing journal administrators to update information content without modifying the system. Functional testing results indicate that the chatbot delivers accurate and consistent information with acceptable response times across both platforms. The implementation demonstrates that integrating an AI agent chatbot with a dynamic knowledge base can enhance information accessibility, reduce administrative workload, and improve service efficiency in academic journal management

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Maharani.S, Putri; Satra, Ramdan, Universitas Muslim Indonesia; Azis, Huzain, Universiti Kuala Lumpur

Abstract

Distributed Denial of Service (DDoS) attacks pose a serious risk to network reliability, particularly within Software-Defined Networking (SDN) architectures that rely on centralized control. This research analyzes the effectiveness of Gaussian Naïve Bayes (GNB) and Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel for identifying DDoS attacks using an SDN traffic dataset consisting of 104,345 flow records. Three different feature-handling strategies are explored: using the complete feature set without reduction, applying feature selection through SelectKBest, and performing dimensionality reduction with Principal Component Analysis (PCA). Model validation is carried out using Stratified K-Fold Cross-Validation with K values of 2, 5, and 10. Predictions obtained from each fold are merged into a single aggregated confusion matrix to compute evaluation metrics, including accuracy, precision, recall, and F1-score. Experimental results demonstrate that RBF-SVM without feature reduction delivers the highest detection performance, reaching an accuracy of up to 96.9%, while GNB provides lower accuracy but operates with greater computational efficiency. These findings indicate that an evaluation framework based on aggregated confusion matrices can provide more dependable performance estimates for DDoS detection systems deployed in SDN environments.

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Muflih, Ahmad; Irawati, Irawati; Syafie, Lukman, Universitas Muslim Indonesia

Abstract

Permasalahan utama dalam manajemen fasilitas olahraga di Kota Makassar, khususnya lapangan tenis, terletak pada penggunaan sistem reservasi konvensional yang memicu inefisiensi operasional dan ketidakpastian informasi ketersediaan bagi penyewa. Penelitian ini bertujuan untuk merancang dan membangun sistem informasi manajemen penyewaan lapangan tenis berbasis web yang mengintegrasikan fitur real-time chat, yaitu mekanisme komunikasi digital yang memungkinkan pengiriman dan penerimaan pesan secara langsung tanpa jeda waktu yang signifikan sehingga pengguna dan pengelola dapat berinteraksi secara instan dalam satu platform. Metode pengembangan sistem yang digunakan adalah model Spiral, yaitu pendekatan pengembangan perangkat lunak yang bersifat iteratif dan berorientasi pada analisis risiko. Model ini menggabungkan pendekatan prototyping dan model sekuensial, di mana setiap siklus pengembangan melalui tahapan komunikasi pelanggan, perencanaan, analisis risiko, rekayasa, konstruksi, dan evaluasi untuk menghasilkan sistem yang lebih stabil dan sesuai kebutuhan penggunaan Hipotesis penelitian mengasumsikan bahwa implementasi sistem informasi yang terintegrasi dengan fitur komunikasi langsung dapat mereduksi kesalahan pencatatan jadwal dan meminimalisir konflik pemesanan (double booking) secara signifikan. Hasil penelitian menunjukkan bahwa sistem yang dibangun mampu mengotomatisasi aliran kerja manajemen, menyediakan transparansi ketersediaan lapangan secara presisi, serta memfasilitasi koordinasi responsif antara pengelola dan penyewa melalui protokol WebSocket. Kesimpulan dari penelitian ini menegaskan bahwa transformasi digital pada manajemen lapangan tenis di wilayah urban seperti Makassar memberikan dampak positif terhadap efektivitas administrasi dan kepuasan pengguna, sekaligus menyediakan dasar bagi pengembangan ekosistem olahraga yang lebih modern dan terorganisir

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Nasrudin, Alfian; Satra, Ramdan; Syafie, Lukman, Universitas Muslim Indonesia

Abstract

Penelitian ini membahas perbandingan kinerja protokol routing Open Shortest Path First (OSPF) dan Routing Information Protocol (RIP) pada jaringan berbasis MikroTik yang dibangun menggunakan GNS3. Latar belakang penelitian ini didasari oleh kebutuhan pemilihan protokol routing yang tepat untuk meningkatkan efisiensi, stabilitas, dan kecepatan konvergensi jaringan, terutama pada lingkungan dengan topologi dinamis. Tujuan penelitian adalah untuk mengidentifikasi perbedaan kinerja kedua protokol berdasarkan empat parameter utama, yaitu delay (waktu tunda), packet loss, Throughput, dan convergence time. Metode penelitian meliputi perancangan topologi jaringan menggunakan dua router MikroTik dan tiga klien, konfigurasi OSPF dan RIP, serta pengujian performa dengan skenario lalu lintas data yang sama. Hasil pengujian menunjukkan bahwa OSPF memiliki kinerja yang lebih baik pada seluruh parameter dibandingkan RIP. Rata-rata delay OSPF lebih rendah, packet loss mencapai 0% di semua skenario, Throughput lebih tinggi, serta waktu konvergensi lebih cepat (6–7 detik) dibandingkan RIP yang memerlukan 18–21 detik. Berdasarkan hasil tersebut, dapat disimpulkan bahwa OSPF lebih cocok digunakan pada jaringan dengan kebutuhan jalur optimal dan konvergensi cepat, sedangkan RIP dapat digunakan pada jaringan kecil yang bersifat statis dengan sedikit perubahan topologi

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Kadir, Aqifah; Hasanuddin, Tasrif, Universitas Muslim Indonesia

Abstract

Website menjadi sarana utama dalam penyebaran informasi akademik dan administratif di lingkungan perguruan tinggi. Penelitian ini bertujuan untuk mengukur kualitas website FIKOM.UMI.AC.ID menggunakan metode WebQual 4.0, yang menilai tiga dimensi utama, yaitu usability (kegunaan), information quality (kualitas informasi), dan service interaction quality (kualitas interaksi layanan), serta pengaruhnya terhadap kepuasan pengguna.Metode penelitian yang digunakan mencakup penyebaran kuesioner berbasis skala Likert kepada pengguna website, diikuti dengan uji validitas dan reliabilitas untuk memastikan keakuratan instrumen penelitian. Analisis data dilakukan menggunakan uji regresi linear berganda, uji t, dan uji f untuk mengidentifikasi pengaruh masing-masing variabel terhadap kepuasan pengguna.Hasil penelitian menunjukkan bahwa variabel service interaction quality memiliki pengaruh signifikan terhadap kepuasan pengguna, sedangkan usability dan information quality tidak memberikan pengaruh signifikan secara individu. Namun, secara simultan, ketiga variabel independen memiliki pengaruh signifikan terhadap kepuasan pengguna. Hal ini mengindikasikan bahwa aspek interaksi layanan merupakan faktor utama dalam meningkatkan kepuasan pengguna terhadap website

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Sumaja, Muh. Askan Tri Anugrah; Anraeni, Siska; Asis, Muhammad Arfah, Universitas Muslim Indonesia

Abstract

Perkembangan teknologi informasi mendorong pemanfaatan sistem berbasis web untuk meningkatkan efektivitas dalam pengelolaan tugas, baik di lingkungan kerja maupun akademik. Permasalahan umum dalam manajemen tugas meliputi kurangnya struktur, keterlambatan penyelesaian, dan rendahnya motivasi pengguna. Penelitian ini bertujuan untuk merancang Sistem Manajemen Tugas Berbasis Web dengan fitur gamifikasi untuk mendukung penugasan dan pemantauan progres secara terintegrasi. Metode yang digunakan dalam penelitan ini adalah System Development Life Cycle (SDLC) dengan model Waterfall yang terdiri dari beberapa tahapan, yaitu analisis kebutuhan, perancangan sistem, implementasi, dan pengujian. Sistem dirancang dengan menggunakan pemodelan Unified Modeling Language (UML) serta didukung oleh basis data yang mengakomodasikan fitur pengelolaan tugas dan mekanisme gamifikasi seperti poin, level, badge, bonus ketepatan waktu, serta pengukuran konsistensi harian (streak). Hasil perancangan menunjukkan bahwa system bisa membantu pengelolaan tugas secara digital dan menampilkan progress pengguna secara lebih terstruktur melalui penerapan Gamifikasi. Dengan adanya mekanisme evaluasi berdasarkan prioritas tugas, ketepatan waktu, dan konsistensi penyelesaian, rancangan sistem diharapkan dapat mendukung peningkatan motivasi dan kesiplinan pengguna dalam menyelesaikan tugas

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Asis, Muhammad Arfah; Kurniati, Nia; Jufda, Muhammad Alfarid, Universitas Muslim Indonesia, Indonesia

Abstract

The advancement of information technology has increased the use of web scraping for scientific data collection, including from the SINTA (Science and Technology Index) platform, which provides researcher profiles, affiliations, publications, and citation data. However, scraping SINTA poses challenges, particularly when multiple authors share identical scores that trigger changes in display order. This instability can lead to duplicated or missing entries when using a single-pass scraping approach. This study evaluates the accuracy and completeness of SINTA author data collection by implementing repeated scraping as a strategy to handle dynamic data ordering. Experiments were conducted on the Universitas Muslim Indonesia (UMI) affiliation, targeting 915 active authors. The methodology involved page-structure analysis, spider development using Python and Scrapy, sequential scraping through pagination, and validation of data completeness and uniqueness. A three-second delay between requests was applied to maintain responsible scraping practices. The results show that a single scraping attempt failed to retrieve all authors, capturing an average of only 877.2 authors (95.86%). Due to unstable ordering, repeated iterations were required. Through 4–8 scraping cycles per trial, all 915 authors were successfully collected without duplication. These findings indicate that for platforms with dynamic data structures such as SINTA, repeated scraping provides a more reliable method for ensuring data completeness and accuracy, supporting the development of stable and responsible publication-data automation systems.

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Utami, Amalia Putri; Indra, Dolly; Umar Fitriyani,. Universitas Muslim Indoensia;

Abstract

Identifikasi daun herbal secara manual sering mengalami kendala akibat kemiripan visual antarspesies yang berpotensi menimbulkan kesalahan pemanfaatan. Penelitian ini bertujuan mengklasifikasikan sepuluh jenis daun herbal menggunakan kombinasi fitur tekstur Local Binary Pattern (LBP) dan fitur bentuk, yaitu aspect ratio, eccentricity, circularity, dan convexity. Dataset terdiri dari 500 citra yang dibagi menjadi 400 data latih dan 100 data uji dengan rasio 80: 20. Tahap pre-processing meliputi resize, konversi ke grayscale, dan Gaussian Blur untuk mengurangi noise. Segmentasi dilakukan menggunakan Otsu thresholding untuk memperoleh objek daun dan Canny Edge Detection untuk menonjolkan struktur tekstur. Proses klasifikasi menerapkan algoritma Random Forest dengan pengujian beberapa kombinasi parameter guna memperoleh model optimal. Hasil terbaik diperoleh pada model dengan n_estimators= 200, max_features= 2, max_depth= none, dan min_samples_leaf= 2, yang menghasilkan akurasi 92%, precision 92%, recall 92%, dan F1-score 92%.

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Hayati, Lilis Nur; Indra Dolly, Universitas Muslim Indonesia; Handayani, Anik Nur; Irianti, Wahyu Sakti Gunawan, Universitas Negeri Malang; Damanhuri Nor Salwa, Universiti Teknologi MARA (UiTM);

Abstract

Indonesian Sign Language (Bahasa Isyarat Indonesia/BISINDO) enables communication for deaf individuals through hand gestures, yet limited public awareness creates significant barriers between deaf and hearing communities. Existing recognition systems often fail to generalize across diverse skin tones, reducing their effectiveness in inclusive real-world deployment. The contribution of this research is a BISINDO alphabet recognition system that integrates skin color features - extracted via HSV-based skin segmentation - as an additional preprocessing layer within the Faster R-CNN framework, explicitly improving detection robustness across varied skin tones. The dataset consists of 8,000 images from ten adult actors representing light, medium-brown, and dark skin tones, augmented through flipping and brightness variation, with a 90:10 training-to-testing ratio. The model was trained over 15,000 steps with a batch size of 24, selected through empirical validation to balance convergence stability and dataset size. Experimental results show that indoor conditions outperform outdoor settings due to controlled lighting. Light-skinned and dark-skinned participants achieved the highest accuracy of 87.5% and F1-score of 85.71%, while medium-brown-skinned participants showed slightly lower performance, likely attributed to greater variability in reflectance under mixed lighting. The system achieves 24 frames per second, demonstrating potential for real-time communication support. These findings confirm that Faster R-CNN with skin color feature integration is effective for BISINDO alphabet recognition, with skin tone diversity being a critical performance factor. Future work will explore larger participant pools and dynamic gesture recognition under varied real-world lighting scenarios.

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Husain, Alfiansyah Anugrah; Indra Dolly; Jabir Sitti Rahmah, Universitas Muslim Indonesia;

Abstract

Platform X menjadi ruang utama bagi masyarakat dalam mengekspresikan opini terhadap tokoh publik, termasuk Kang Dedi Mulyadi. Penelitian ini bertujuan menganalisis sentimen publik sekaligus menjawab keterbatasan studi sebelumnya yang umumnya berfokus pada isu politik nasional atau hanya menggunakan satu algoritma klasifikasi, sehingga perbandingan kinerja model pada konteks figur politik daerah masih terbatas. Data penelitian terdiri dari 7.190 tweet yang dikumpulkan melalui crawling selama Februari–September 2025. Data diproses melalui tahapan pre-processing, kemudian dilabeli otomatis menggunakan InSet Lexicon ke dalam sentimen positif dan negatif. Ekstraksi fitur dilakukan dengan TF-IDF menggunakan konfigurasi n-gram (1,2), dengan pembagian data 80:20 untuk pelatihan dan pengujian. Hasil pengujian menunjukkan bahwa SVM Linear memberikan performa terbaik dengan akurasi 0,8982, presisi 0,8991, recall 0,8970, dan F1-score 0,8977. Sementara itu, Naïve Bayes menghasilkan akurasi 0,8287, presisi 0,8297, recall 0,8268, dan F1-score 0,8276. Temuan ini menunjukkan bahwa SVM lebih efektif dibandingkan Naïve Bayes dalam mengklasifikasikan sentimen publik pada data media sosial X, serta memberikan kontribusi empiris pada kajian analisis sentimen tokoh politik berbasis media sosial

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Hayati, Lilis Nur; Indra Dolly, Universitas Muslim Indonesia; Djamereng Asdar;

Abstract

Buku “Rahasia Social Media Marketing untuk Bisnis Online” membahas bagaimana media sosial dapat dimanfaatkan secara optimal untuk mengembangkan bisnis di era digital. Pembahasan dimulai dari pemahaman dasar mengenai media sosial, jenis-jenis platform yang populer, serta peluang yang dapat dimanfaatkan oleh pelaku usaha untuk memperluas jangkauan pasar dan membangun hubungan dengan pelanggan. Buku ini juga menjelaskan berbagai keunggulan media sosial sebagai sarana promosi yang mudah diakses, interaktif, dan mampu menjangkau audiens secara luas. Selain peluang, dijelaskan pula tantangan yang sering dihadapi dalam mengelola media sosial sehingga pembaca dapat memahami pentingnya strategi yang tepat dalam menjalankan pemasaran digital. Buku ini memberikan panduan praktis mengenai cara membuat dan mengelola akun bisnis di berbagai platform media sosial, teknik membuat konten yang menarik, serta langkah membangun identitas brand yang kuat. Berbagai strategi promosi seperti kolaborasi, penggunaan influencer, hingga pemanfaatan fitur iklan juga dibahas secara sistematis agar bisnis dapat berkembang secara lebih efektif. Melalui penjelasan yang mudah dipahami dan disertai contoh penerapan, buku ini diharapkan dapat menjadi panduan bagi pelaku usaha, pelajar, maupun siapa saja yang ingin memanfaatkan media sosial sebagai sarana membangun dan mengembangkan bisnis online secara berkelanjutan.

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Astuti, Wistiani; Mude, Muh Aliyazid; Abdullah Syahrul Mubarak; Kurniati, Nia; Indra, Dolly; Manga, Abul Rachman; Ilmawan, Lutfi Budi, Universitas Muslim Infdonesia;

Abstract

Buku ini disusun secara sistematis agar mudah dipahami, mulai dari konsep dasar penilaian Openness dan Webometrics, hingga panduan teknis penggunaan Google Scholar dan SINTA. Pembaca akan dipandu langkah demi langkah dalam mengelola akun profil, meningkatkan jumlah sitasi, hingga strategi menulis artikel agar lebih mudah ditemukan secara global.

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Rezky, Putri Nur; Indra Dolly; Darwis, Herdianti, Universitas MUslim Indonesia;

Abstract

The Program Makan Bergizi Gratis (MBG) is a flagship program of the Prabowo Subianto administration launched in 2024, triggering diverse public responses on social media. Sentiment analysis using deep learning models offers an effective approach to understanding public opinion at scale. However, selecting the appropriate model for Indonesian social media text remains challenging. This study aims to compare the performance of two pretrained transformer models, RoBERTa Base and IndoBERTweet Base, in conducting automatic sentiment labeling on Indonesian tweets related to the MBG program using a zero-shot labeling approach without human-annotated ground truth. A total of 1,831 tweets were collected from platform X and preprocessed using case folding, normalization, and stopword removal. Both models were applied in parallel to label each tweet with sentiment categories (positive, neutral, negative) along with confidence scores. The comparison was evaluated using agreement rate, Cohen's Kappa, and confidence score analysis. RoBERTa Base exhibits a conservative tendency with 75.20% neutral labels, while IndoBERTweet Base produces a more balanced distribution (68.16% neutral). The comparison shows 77.28% agreement with Cohen's Kappa of 0.490 (Moderate Agreement). RoBERTa Base achieves higher confidence (mean: 0.9559, 83.01% above 0.95) compared to IndoBERTweet Base (mean: 0.9236, 68.65% above 0.95). IndoBERTweet Base is more effective in detecting negative sentiment, identifying nearly twice as many negative tweets (13.54% vs. 7.48%). This study recommends IndoBERTweet Base for exploratory research requiring sensitive sentiment detection and RoBERTa Base for precision-critical applications. An ensemble approach combining both models is recommended for production-critical applications

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Zahra, Andi Fathimatuz; Indra Dolly; Hayati, Lilis Nur, Universitas Muslim Indonesia

Abstract

Pemanfaatan aplikasi perbankan digital menjadi bagian penting dalam mendukung produktivitas masyarakat melalui layanan perbankan yang efisien dan praktis. Byond by BSI sebagai salah satu mobile banking turut berperan dalam memenuhi kebutuhan layanan keuangan nasabahnya. Seiring meningkatnya jumlah pengguna aplikasi Byond by BSI, muncul beragam opini yang belum terpetakan secara sistematis. Hal ini menciptakan kebutuhan akan analisis sistematis agar ulasan tersebut dapat dimanfaatkan sebagai basis evaluasi fungsionalitas aplikasi dan optimalisasi pengalaman pengguna. Penelitian ini bertujuan mengidentifikasi distribusi sentimen positif dan negatif pada ulasan pengguna aplikasi Byond by BSI serta menentukan algoritma terbaik berdasarkan nilai akurasi, presisi, recall, dan F1-score dalam mengklasifikasikan sentimen ulasan pengguna. Metode yang diterapkan untuk menentukan kategori sentimen ulasan adalah algoritma Naïve Bayes dan SVM. Hasil evaluasi penelitian menunjukkan bahwa algoritma SVM mampu menunjukkan kinerja lebih baik daripada Naïve Bayes dalam mengklasifikasikan ulasan. SVM menunjukkan kinerja terbaik pada pembagian data 90: 10 dengan akurasi 95.33%, presisi 97.12%, recall 94.94%, dan f1-score 96%. sedangkan Naïve Bayes mencapai kinerja terbaiknya pada pembagian data 70: 30 dengan akurasi 94%, presisi 95.10%, recall 94.45%, dan f1-score 94.81%.

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Indra, Dolly; Alwi, Erick Irawadi; Kasim, Fadil, Universitas Muslim Indonesia

Abstract

Indonesian Sign Language (BISINDO) is an essential means of communication for deaf people in Indonesia. However, communication challenges persist for the general public, who lack sign language proficiency. To bridge this gap, a real-time BISINDO recognition system is essential. This research compares the performance of several distance models in recognizing BISINDO letters using a camera-based recognition system. The study evaluated three distance methods City Block, Canberra, and Chebyshev applied to shape feature extraction through chain code analysis. Testing was conducted under both indoor and outdoor conditions to assess system robustness. The results revealed that the Chebyshev distance method achieved the highest accuracy in indoor scenarios, particularly when participants wore white clothing, reaching an accuracy of 100%. Meanwhile, in outdoor conditions, the Canberra distance method outperformed the others, achieving the highest accuracy of 96.15% with white clothing. However, outdoor accuracy was generally lower due to uncontrollable lighting, which affected recognition. Despite these challenges, the system demonstrated its capability to recognize BISINDO letters effectively in both environments.

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Irfan, Muhammad Naufal; Alwi, Erick Irawadi; Hasnawi, Mardiyyah, Universitas Muslim Indonesia;

Abstract

Ketersediaan jaringan internet yang andal merupakan kebutuhan mendasar bagi pengguna teknologi komunikasi masa kini. Namun, perkembangan teknologi jaringan seluler dari generasi ke generasi tidak selalu berbanding lurus dengan peningkatan kualitas layanan yang dirasakan pengguna secara aktual. Penelitian ini bertujuan membandingkan kualitas layanan (Quality of Service/QoS) jaringan 4G dan 5G pada layanan video streaming di Kota Makassar. Pengukuran dilakukan secara kuantitatif menggunakan empat parameter QoS, yaitu throughput, packet loss, delay, dan jitter, dengan instrumen aplikasi Wireshark berdasarkan standarisasi TIPHON. Data dikumpulkan selama 10 hari pada dua sesi waktu, yaitu siang hari (12.00–13.00 WITA) dan malam hari (19.00–20.00 WITA) di lantai dua Mall Panakkukang Square Makassar. Hasil penelitian menunjukkan bahwa indeks QoS jaringan 4G pada sesi siang sebesar 3,87 dan sesi malam sebesar 3,93, keduanya berada pada kategori "Memuaskan". Sementara itu, indeks QoS jaringan 5G pada sesi siang sebesar 3,86 dan sesi malam sebesar 3,90, yang juga termasuk dalam kategori yang sama. Rata-rata indeks QoS jaringan 4G (3,90) tercatat lebih tinggi dibandingkan jaringan 5G (3,88). Temuan ini mengindikasikan bahwa pada kondisi pengujian indoor dan tahap awal implementasi infrastruktur, kualitas jaringan 5G belum secara nyata melampaui jaringan 4G yang telah lebih mapan.

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Rafianto, Muh; Alwi, Erick Irawadi; Mansyur, St Hajra, Universitas Muslim Indonesia;

Abstract

Conventional digital identity systems often rely on centralized authorities that are vulnerable to data breaches and manipulation. The Developer Student Club Universitas Muslim Indonesia (DSC UMI) community requires a membership verification mechanism that is credible, secure, and preserves user privacy. This research aims to implement a decentralized digital identity system fully integrated on the Android platform using blockchain technology and Zero-Knowledge Proofs (ZKP), where the entire system logic—including blockchain interactions and cryptography—is processed independently on the client side (backendless) without dependence on intermediary servers. The development method applies a Minimum Viable Product (MVP) approach with a mobile-first architecture. The system is built on native Android using the Kotlin language, integrated with the Polygon Amoy Testnet network via the Web3j library. Authentication utilizes local biometric sensors to generate cryptographic proofs based on SHA-256 hashes without storing raw data on the server. White box testing results show that the application successfully connects to the MetaMask digital wallet, validates anti-duplication logic on the Smart Contract, and automatically issues a Soulbound Token (NFT) as legitimate proof of membership. In conclusion, blockchain integration on mobile devices is proven effective in delivering Self-Sovereign Identity (SSI) that is transparent and auditable without compromising the privacy of community member data.

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Satra, Ramdan; Mubarak Syahrul, Universitas Muslim Indonesia;

Abstract

A safe is a tool used to store valuables including money, jewelry, assets or valuable documents. A safe is a storage place that is considered practical but has a high risk. There are several shortcomings in existing safes, including using one key, which makes the safe easy to break into, which can cause losses to the owner. Therefore, it is necessary to innovate or develop by using a combination of several keys by applying fingerprint and keypad technology to add security features to the safe. The solution to this problem can be made by creating a Money Storage Safe Security With Two Key Combinations (Fingerprint + Keypad), this system can open the safe with two combinations of fingerprint and keypad so that it can increase security on the safe. This study aims to design a safe security system using fingerprint and keypad. This system is a prototype that will open the safe door based on the user's fingerprint that has been recognized and uses the keypad to enter a previously created password. Based on the results of research and testing of tools and fingerprints, an accuracy of 100% was obtained for tool testing and 53.33% for fingerprint testing, so it can be concluded that the design of the tool has worked well

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Huda, Besse Nurul; Anraeni, Siska, Universitas Muslim Indonesa;

Abstract

Acute Respiratory Infections (ARI) are one of the diseases that often affect children and are a major cause of morbidity and mortality in Indonesia. Accurate early diagnosis is very important to prevent complications, but limited medical personnel and the similarity of ARI symptoms to other diseases are often obstacles. In this context, an artificial intelligence-based expert system can be a solution to support medical decisions. This article presents a comparative analysis of two inference methods commonly used in expert systems, namely Certainty Factor (CF) and Dempster-Shafer (DS). Through a Systematic Literature Review (SLR) approach, this study evaluates the performance of both methods based on accuracy, complexity, flexibility, and ease of implementation. The results of the study show that Certainty Factor excels in simplicity and efficiency, while Dempster-Shafer is more reliable in handling uncertainty and cases with many overlapping symptoms. This article is expected to be a reference for the development of more accurate and efficient medical expert systems in assisting the diagnosis of ARI in children.

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Anraeni, SIska; Septiawan, M Fiqry; Resa, Muhammad Raihan; Lahuddin, Herlinda; Darwis, Herdianti, Universitas Muslim Indonesia

Abstract

Precise quantification of pineapple (Ananas comosus) cultivation is vital for strategic agricultural decisionmaking and operational efficiency. Traditional manual enumeration methods are resource-intensive, time-consuming, and susceptible to human error. This research presents an automated detection framework utilizing unmanned aerial vehicle (UAV) photography combined with digital image analysis techniques. A YOLO-formatted dataset of high-resolution photos was gathered in Pujananting, Barru, South Sulawesi, Indonesia. An object detection model built on the YOLOv8 architecture was then trained using these pictures. Standard criteria such as precision, recall, and mean Average Precision at a 0.5 Intersection over Union threshold were used to assess the model. Experimental findings indicate the proposed framework achieves excellent detection performance, with mean Average Precision attaining 0.979 and both precision and recall reaching 0.95. The framework was additionally validated for automated plant enumeration, with outcomes aligning closely with manual field observations. These findings demonstrate that UAV-based imaging integrated with deep learning provides an accurate and efficient methodology for pineapple crop monitoring, supporting intelligent agricultural applications and informed management strategies.

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Belluano, Poetri Lestari Lokapitasari; Majid, Abd; Fuad Muhammad Nur; Scania, Muhammad Rifky Saputra, Universitas Muslim Indonesia;

Abstract

Event notification systems play a crucial role in supporting academic and student organizational activities. However, many institutions still rely on monolithic architectures with synchronous processing that are unable to handle spikes in communication loads, resulting in high latency and reduced delivery reliability. This study proposes a notification system based on Event-Driven Architecture (EDA) integrated with a microservices environment to improve the efficiency, scalability, and reliability of information dissemination, particularly through the WhatsApp Business API as the primary communication channel. The proposed system leverages asynchronous event processing, distributed message brokers, and isolated gateway services to enable parallel message delivery while addressing external service constraints such as rate limiting. The system is evaluated within a single controlled experimental setup using 1,011 notification messages under consistent workload conditions. For comparison purposes, a simulated synchronous baseline is used to represent the characteristics of traditional sequential processing systems. The results show that the EDA-based system achieved a 100% delivery success rate with an average latency of 3,222 ms and a stable throughput of 17 messages per second, while the simulated baseline exhibits limitations in maintaining performance under the same conditions. These findings indicate that the proposed architecture improves system performance within the evaluated experimental context and demonstrates strong potential for scalable real-time communication in controlled deployment environments.

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Musdalifah, Musdalifah; Indra, Dolly; Jabir, Sitti Rahmah, Faculty Of Computer Science, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

Pemanfaatan sistem informasi di tingkat pemerintahan desa menjadi krusial untuk memenuhi ekspektasi publik terhadap pelayanan yang responsif dan transparan. Penelitian ini bertujuan untuk mengevaluasi efektivitas website resmi Desa Lamundre (https://lamundre.kolakadesa.id/) dalam menunjang efisiensi operasional aparatur desa serta kualitas layanan bagi masyarakat. Metode evaluasi yang digunakan adalah kerangka kerja PIECES yang meninjau enam dimensi utama: performance, information, economy, control, efficiency, dan service. Data dikumpulkan melalui observasi, wawancara, dan penyebaran kuesioner kepada 10 aparatur desa serta 30 responden masyarakat dengan teknik analisis statistik deskriptif menggunakan skala Likert. Hasil penelitian menunjukkan bahwa secara keseluruhan sistem informasi Desa Lamundre berada pada kategori "Puas". Dimensi control mendapatkan penilaian tertinggi (skor 3,66), sedangkan dimensi efficiency memerlukan perhatian lebih untuk peningkatan performa sistem di masa mendatang. Evaluasi ini diharapkan menjadi acuan bagi Pemerintah Desa Lamundre dalam mengoptimalkan sistem informasi desa.

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Anraeni, Siska; Lahuddin, Harlinda; Ramdaniah, Ramdaniah; Melani, Erika Riski; Amalia, Andi Cici; Amaliah, Tazkirah, Faculty Of Computer Science, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

This study develops a digital image processing technique to distinguish between fresh and rotten chicken. Chicken freshness has a significant impact on public health and industry sustainability. This study uses a multistage approach including data acquisition, preprocessing, feature extraction, and classification. A total of 1,000 chicken images were obtained, consisting of 800 images for training and 200 images for testing, with a proportion of 80:20. Feature extraction was performed using a combination of the HSI (Hue, Saturation, Intensity) color model to capture the color characteristics of chicken, and Local Binary Pattern (LBP) to extract texture information. Classification was performed using the K-Nearest Neighbor (KNN) algorithm with various K values and distance metrics. The experimental results show that the combination of color and texture features provides higher accuracy than using either feature alone. The best model using HSI and LBP feature extraction with K = 1 and K = 3 in the Euclidean distance metric, achieved the highest accuracy of 95.4%. With a promising level of accuracy, this method can be applied in automated inspections in the poultry supply chain, improving food safety, and helping consumers make better purchasing decisions. However, the main challenge in this study is the variation in lighting during image capture, which causes the fresh and rotten chicken feature values to overlap, thus hindering perfect classification.

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Rezky, Aditya; Hayati, Lilis Nur; Sugiarti, Sugiarti, Faculty Of Computer Science, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

Banana leaf disease (Musa spp.) poses a critical threat to tropical agricultural productivity in Indonesia, with harvest loss estimates reaching 30–50% during rainy seasons due to undetected pathogen infections. Limited manual diagnostic capacity among farmers produces disease misidentification and delayed control interventions, particularly at early infection stages when inter-class visual symptom similarity remains high. This study proposes an automated banana leaf disease detection system leveraging deep learning through the You Only Look Once version 5 (YOLOv5) architecture as a digital image-based diagnostic solution. The dataset comprises 9,684 images across eight banana leaf disease classes, curated via Roboflow with 87% training (8,460 images), 8% validation (820 images), and 4% testing (404 images) splits. Preprocessing includes 2×2 tiling, auto-orientation, and 512×512 pixel stretch resizing. Data augmentation applies horizontal and vertical flip alongside 0–25% zoom crop, generating three outputs per training image. Model performance evaluation employs precision, recall, F1-score, and mean Average Precision (mAP@0.5) metrics. Results demonstrate YOLOv5 capability in accurately detecting and classifying banana leaf diseases under Indonesian tropical field imaging conditions.

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Iha, Eka Inggraha; Herman, Herman; Hayati, Lilis Nur; Amir, Nur Hikmah, Faculty Of Computer Science, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

Pemerintah Kabupaten Fakfak belum memiliki kanal digital resmi untuk menerima dan menanggapi keluhan masyarakat secara terstandarisasi, sehingga seluruh pengaduan diproses melalui kunjungan fisik dan media sosial tanpa alur penanganan baku. Penelitian membangun sistem berbasis web menggunakan PHP, MySQL, dan Bootstrap 5 dengan metode pengembangan SDLC model Waterfall, mengintegrasikan empat aktor yaitu Masyarakat, Admin, Petugas/Instansi, dan Walikota dalam satu platform terpadu berautentikasi NIK. Pengujian Black-Box Testing membuktikan seluruh fungsi utama berjalan sesuai spesifikasi kebutuhan, mencakup pengajuan keluhan, verifikasi akun, distribusi penugasan, pembaruan status, dan pemantauan analitik eksekutif. Uji penerimaan pengguna menunjukkan masyarakat berani dan aktif memposting pengaduan secara terbuka melalui platform, mengindikasikan tingkat kepercayaan pengguna terhadap sistem terbentuk sejak tahap pengujian awal. Sistem terbukti mampu mengubah budaya pengaduan konvensional menjadi partisipasi publik digital yang terukur dan akuntabel di lingkungan Pemerintah Kabupaten Fakfak

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Jaya, Andi Rezaldy; Hayati, Lilis Nur; Herman, Herman, Faculty Of Computer Science, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

Website Buahta Makassar dirancang untuk mempermudah pelanggan, terutama yang tinggal jauh dari lokasi toko, dalam mengakses informasi produk secara daring. Proses perancangan menggunakan metode design thinking melalui lima tahapan: empathize, define, ideate, prototype, dan test, serta memanfaatkan tools Figma. Rancangan menampilkan fitur utama seperti daftar buah, harga, dan penambahan jumlah produk. Tahap pengujian dilakukan dengan metode usability testing menggunakan platform Maze. Hasil pengujian pada dua layar utama kategori dan review menunjukkan usability score sebesar 81, dengan rata-rata waktu interaksi 7-8 detik dan tingkat salah klik sebesar 78%. Nilai tersebut masuk dalam kategori cukup, yang berarti fitur berfungsi namun masih memerlukan peningkatan terutama dalam kejelasan navigasi. Rancangan ini menjadi pondasi awal untuk pengembangan sistem e-commerce Buahta Makassar di masa mendatang

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Bellanov, Agrienta; Nurhayati, Lilis, Faculty Of Computer Science, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

This study aims to analyze parents' responses to the trial implementation of the Free Nutritious Meal Program (MBG) conducted in several elementary schools in Trenggalek Regency. The MBG program, initiated by the government, is in its pilot stage and primarily targets improvements in students' nutritional intake, health, academic performance, and efforts to reduce stunting. mploying a mixed-methods approach, the research combined qualitative insights from in-depth interviews with parents, teachers, and school principals, alongside quantitative data gathered from structured questionnaires distributed to parents involved in the pilot program. The study evaluated variables such as food quality, hygiene, serving frequency, school–parent communication, and parental expectations and concerns regarding the program’s continuity. The findings reveal a largely positive response; parents particularly appreciated the program's role in fostering healthier eating habits and alleviating household financial burdens. However, several critical issues emerged, including limited menu variety, inconsistent food taste quality, and lack of transparency regarding meal schedules and nutritional content. Some parents expressed hope for the program’s expansion, provided that food quality and safety standards are maintained. Further analysis indicates that effective communication and parental involvement significantly influence satisfaction and support levels for the program. These insights offer valuable input for local governments and schools in refining MBG implementation to maximize its impact. This research contributes to a better understanding of the dynamics of the MBG pilot at the elementary school level and provides data-driven recommendations to enhance future policy and execution of free nutritious meal programs.

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Shabran, Muhammad; Darwis, Herdianti; Anraeni, Siska, Universitas Muslim Indonesia

Abstract

Kesehatan mental mahasiswa menjadi isu penting di lingkungan perguruan tinggi karena berpengaruh terhadap performa akademik, interaksi sosial, dan kesejahteraan psikologis. Mahasiswa sering menghadapi tekanan akademik dan tuntutan sosial yang dapat memicu stres dengan tingkat keparahan berbeda. Penelitian ini bertujuan untuk mengklasifikasikan tingkat stres mahasiswa Universitas Muslim Indonesia berdasarkan instrumen Depression Anxiety Stress Scale-21 (DASS-21) menggunakan algoritma Decision Tree berbasis Classification and Regression Tree (CART) dan Random Forest. Penelitian ini melibatkan lebih dari 3000 responden mahasiswa yang datanya diperoleh melalui kuesioner DASS-21 serta variabel pendukung seperti jenis kelamin, usia, fakultas, indeks massa tubuh, durasi tidur, olahraga, dan penggunaan media sosial. Tahapan analisis meliputi preprocessing data, feature engineering, encoding variabel, penanganan ketidakseimbangan kelas menggunakan Synthetic Minority Oversampling Technique for Nominal and Continuous data (SMOTENC), serta pembagian data 80% untuk pelatihan dan 20% untuk pengujian. Evaluasi model dilakukan menggunakan confusion matrix, accuracy, balanced accuracy, precision, recall, dan Macro F1-score. Hasil penelitian menunjukkan bahwa Random Forest dengan tuning tanpa SMOTENC memberikan performa terbaik dengan accuracy sebesar 0,9065 dan Macro F1-score sebesar 0,8427, serta lebih stabil dibandingkan Decision Tree dalam klasifikasi multi-kelas tingkat stres mahasiswa

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Fadly, Ahmad; Purnawansyah; Darwis, Herdianti, Universitas Muslim Indonesia

Abstract

Media sosial seperti Twitter (X) menghasilkan data opini publik dalam jumlah besar yang dapat dimanfaatkan untuk analisis sentimen. Namun, perbedaan konteks antar topik atau hashtag sering menyebabkan terjadinya domain shift yang dapat menurunkan performa model klasifikasi. Penelitian ini bertujuan untuk menganalisis performa algoritma Naïve Bayes dan K-Nearest Neighbors (KNN) dalam skenario cross-domain menggunakan dua representasi fitur, yaitu TF-IDF dan FastText. Dataset diperoleh dari hashtag #MakanBergiziGratis sebagai domain sumber dan #Danantara sebagai domain target. Metode yang digunakan meliputi preprocessing teks, ekstraksi fitur, pemodelan, serta evaluasi menggunakan accuracy, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa kedua model memiliki performa tinggi pada internal test dengan accuracy sebesar 0.94. Namun, pada pengujian lintas domain, Naïve Bayes dengan TF-IDF menunjukkan performa yang lebih stabil dengan accuracy sebesar 0.75, sedangkan KNN dengan FastText mengalami penurunan signifikan, terutama pada kelas negatif dengan nilai F1-score sebesar 0.00. Temuan ini menunjukkan bahwa pemilihan algoritma dan representasi fitur sangat mempengaruhi kemampuan generalisasi model dalam menghadapi domain shift

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Suradi; Atmajaya, Dedy; Darwis, Herdianti, Universitas Muslim Indonesia

Abstract

Penelitian dan publikasi ilmiah merupakan indikator penting dalam menilai kualitas dan reputasi perguruan tinggi, namun Fakultas Ilmu Komputer Universitas Muslim Indonesia (FIKOM UMI) belum memiliki sistem informasi yang mampu menyajikan analisis tren serta prediksi publikasi dan sitasi secara terintegrasi. Penelitian ini bertujuan mengembangkan sistem informasi penelitian dan publikasi berbasis data analitik serta membandingkan performa metode Autoregressive Integrated Moving Average (ARIMA) dan Long Short-Term Memory (LSTM) dalam peramalan deret waktu. Data yang digunakan merupakan data sekunder periode 2017–2025 sebanyak 360 data, dengan tahapan preprocessing, agregasi data tahunan, normalisasi, pembentukan data sekuens, serta pembagian data latih dan data uji (80:20). Evaluasi model dilakukan menggunakan RMSE, MAE, dan MAPE. Hasil penelitian menunjukkan bahwa LSTM memiliki performa lebih baik dibandingkan ARIMA, dengan nilai RMSE 43,16, MAE 40,22, dan MAPE 74,15% pada prediksi publikasi, lebih rendah dibandingkan ARIMA (RMSE 48,71, MAE 46,50, MAPE 84,32%). Pada prediksi sitasi, LSTM juga lebih unggul dengan RMSE 252,44, MAE 250,23, dan MAPE 529,86%, dibandingkan ARIMA (RMSE 288,31, MAE 286,42, MAPE 600,42%). Dengan demikian, sistem yang dikembangkan mampu menyajikan tren serta prediksi yang dapat mendukung pengambilan keputusan strategis dalam meningkatkan kualitas penelitian dan publikasi

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Purnawansyah; Puspitasari, Rahma; Manga', Abdul Rachman; Darwis, Herdianti; Nurhalimah, Sitti, Universitas Muslim Indonesia

Abstract

This community service program aims to improve administrative efficiency in Biji Nangka Village, which previously used manual processes and was prone to delays, inconsistencies, and the risk of archive loss. This activity implemented a website-based self-service system and provided training to village officials on the use of key features such as digital letter management, automatic numbering, and electronic archive storage. A total of 17 participants participated in the training and all successfully operated the system. Evaluation results showed that the time to create letters was reduced from 10–15 minutes to 3–5 minutes. Furthermore, the results of the pre-test and post-test comparison showed a 9.412% increase in participant understanding, indicating the effectiveness of the training in improving the digital competence of village officials. Overall, this program has had a positive impact on improving the quality of administrative services and supporting the realization of digital-based village governance.

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Rahayu, Fitri; Harlinda; Darwis, Herdianti, Universitas Muslim Indonesia

Abstract

Mental health adalah kesejahteraan emosional, psikologis, dan sosial seseorang yang berkaitan dengan cara seseorang berpikir, merasa, dan berperilaku, serta kemampuannya mengatasi stres, menjaga hubungan yang sehat, dan menghadapi tantangan secara positif. Aktifitas di laboratorium sering kali memerlukan waktu dan energi yang cukup banyak, yang dapat meningkatkan tekanan pada mahasiswa dan bisa berdampak pada kesehatan mental mereka. Penelitian ini bertujuan menganalisis sentimen mahasiswa terhadap mental health dalam kehidupan laboratorium dengan menggunakan pendekatan Naïve Bayes Classifier dan K-Nearest Neighbor. Penelitian ini menggunakan beberapa teknik pelabelan, yaitu pelabelan manual dan pelabelan menggunkan NLTK, serta pelatihan dengan 5-fold cross-validation dan penggunaan unigram tokenizing. Hasil penelitian menunjukkan bahwa pelabelan manual dengan pendekatan Naïve Bayes Classifier sedikit lebih unggul dengan tingkat akurasi sebesar 95.58%, presisi sebesar 95.60%, dan recall sebesar 95.54% dibandingkan dengan pendekan K-Nearest Neighbor yang menghasilkan tingkat akurasi 91.66%, presisi 91.83% dan recall 91.49%. Sementara itu, pelabelan menggunakan NLTK dengan pendekatan Naïve Bayes Classifier menghasilkan akurasi tertinggi sebesar 94.11%, presisi 94.05%, dan recall 94.22% dibandingkan dengan pendekatan K-Nearest Neighbor yang memiliki akurasi 90.68%, presisi 90.88%, dan recall 91.01%. Dari hasil pengujian, dapat disimpulkan bahwa pendekatan Naïve Bayes Classifier memberikan hasil yang lebih baik dengan mengklasifikasikan sentimen mahasiswa terkait mental health di kehidupan laboratoriom, dengan pelabelan manual memberikan performa terbaik

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Salim, Yulita, Department of informatics, Universitas Muslim Indonesia, Jl. Urip Sumoharjo, Makassar, 90133, Indonesia; Hidayat, Rivaldi, Department of information systems, Universitas Muslim Indonesia, Jl. Urip Sumoharjo, Makassar, 90133, Indonesia; Darwis, Herdianti, Department of informatics, Universitas Muslim Indonesia, Jl. Urip Sumoharjo, Makassar, 90133, Indonesia; Karnyoto, Andrea Stevens, Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, 11480, Indonesia, Computer Science Department, BINUS Graduate Program - Master of Computer Science Program, Bina Nusantara University, Jakarta, 11480, Indonesia; Puspitasari, Rahma, Department of information systems, Universitas Muslim Indonesia, Jl. Urip Sumoharjo, Makassar, 90133, Indonesia; Mozar, Fitya Syarifa, Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, 11480, Indonesia; Pardamean, Bens, Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, 11480, Indonesia, Computer Science Department, BINUS Graduate Program - Master of Computer Science Program, Bina Nusantara University, Jakarta, 11480, Indonesia

Abstract

Nowadays, facial recognition technology is becoming very important in various real-world applications, and we can easily find it in surveillance systems on various devices or mobile authentication. However, several sophisticated deep learning models require high computational resources and complex training procedures, such as FaceNet and VGG-19. These conditions limit practical applications in low-resource and energy-constrained environments. Based on five simple layers, we used a lightweight architecture for Convolutional Neural Networks (CNNs) in this study by utilizing the standard SoftMax loss function for frontal face recognition that has strong performance for various imaging devices, including webcams, CCTVs, smartphones, and DSLRs. Unlike models that rely on special loss functions or deep architectures, our proposed approach technically balances computational efficiency and high performance for face recognition tasks. A primary dataset that we have collected has been curated, consisting of 23 identity classes, to study the robustness of the model when subjected to varying lighting scenarios and resolutions. It is shown in our study that our model attains a 99.16% accuracy rate and exhibits competitive performance in precision, recall, and F1-score, outperforming or nearly matching FaceNet and VGG-19. This finding confirms that the application of simplified CNN models with standard loss functions is feasible for face recognition, and this method has good accuracy even in resource-constrained environments.

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Luluang, Muhammad Zaqly; Irawati; Darwis, Herdianti, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

This study analyzes the effect of domain filtering on drug recommendation systems based on association rule mining using the FP-Growth algorithm with Neural Collaborative Filtering (NCF) as a comparison. The dataset used was derived from patient medical records containing attributes such as complaints, diagnoses, and drug therapies, with a total of 1,000 patient transactions. To avoid data leakage, the dataset was randomly divided into 70% training data and 30% test data before the modeling process was carried out. Domain filtering was applied by limiting the rule structure so that complaints and diagnoses acted as antecedents and drugs as consequents. The performance of the recommendation system was evaluated using the Precision@5, Recall@5, and Normalized Discounted Cumulative Gain (NDCG@5) metrics. The results of the experiment show that the FP-Growth approach with domain filtering produces higher Precision@5 and NDCG@5 values than the non-filtering approach. The Wilcoxon Signed-Rank test shows that the difference is statistically significant, while effect size analysis using Cliff's Delta shows a practically meaningful impact. Furthermore, a comparison with Neural Collaborative Filtering shows that the collaborative filtering-based approach is less effective on transactional clinical prescription data with limited historical interactions. These findings indicate that integrating medical domain knowledge into FP-Growth can improve the clinical relevance and quality of drug recommendation rankings

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Malik, Muh Aristsyah; Harlinda; Darwis, Herdianti, Universitas Muslim Indonesia

Abstract

This study develops a drug recommendation system using multilabel classification with the Decision Tree algorithm based on patient complaint and diagnosis data from electronic medical records. The dataset consists of patient visit records from a community health center in Pangkajene and Kepulauan Regency and is transformed using multi-hot encoding. Model performance is evaluated under three dataset scenarios (N=500, N=800, and N=1000) using multilabel metrics, including Micro-F1, Samples-F1, Hamming Loss, Jaccard Similarity, Hit@5, Precision@K, and Recall@K. The best Decision Tree model achieved a Micro-F1 score of 0.292, Samples-F1 of 0.281, and Hit@5 of 0.690 on the N=1000 dataset scenario. Bootstrap validation with 1000 iterations indicates relatively stable performance, with narrow confidence intervals across evaluation metrics. These results show that the multilabel Decision Tree model is capable of capturing relationships between patient complaints, diagnoses, and drug therapies while maintaining an interpretable decision structure

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Sugiarti, Fakultas Ilmu Komputer, Program Studi Teknik Informatika, Universitas Muslim Indonesia; Herman, Fakultas Ilmu Komputer, Program Studi Sistem Informasi, Universitas Muslim Indonesia; Suhendra, Fadjri; Fardhan, Muh, Fakultas Ilmu Komputer, Program Studi Teknik Informatika, Universitas Muslim Indonesia;

Abstract

Penjadwalan proses bertujuan mengoptimalkan penggunaan CPU, menekan waktu tunggu, dan meningkatkan kinerja sistem, namun peningkatan jumlah serta kompleksitas proses menyulitkan pemeliharaan performa pada beban kerja dinamis, yang tidak selalu dapat diatasi oleh semua algoritma penjadwalan. Penelitian ini membandingkan tiga algoritma penjadwalan Multi-Level Queue, Round Robin, dan Priority Scheduling melalui simulasi dengan tiga skenario pengujian. Kebaruan penelitian terletak pada analisis komparatif kinerja ketiga algoritma berdasarkan karakteristik beban kerja berbeda, sehingga memberikan gambaran adaptivitas algoritma penjadwalan dalam kondisi sistem dinamis. Evaluasi dilakukan berdasarkan turnaround time, waiting time, response time, dan throughput pada tiga skenario. Pada beban kerja seimbang, Priority Scheduling unggul dalam TAT dan WT namun Round Robin terbaik untuk Response Time. Pada beban kerja berat, Priority Scheduling sangat efisien dengan TAT 17.6 ms. Pada beban kerja ringan dengan variasi prioritas, Priority Scheduling memberikan kinerja terbaik untuk TAT 6.6 ms dan WT 4.0 ms, namun Round Robin unggul dalam Response Time 2.0 ms. Hasil menunjukkan Priority Scheduling paling efisien dengan turnaround time 21% dan waiting time 29% lebih baik dari Round Robin, serta lebih unggul 13% dan 17% dibanding Multi-Level Queue. Priority Scheduling dapat mengeksekusi proses berprioritas tinggi terlebih dahulu tanpa overhead context switching. Round Robin unggul dalam response time, sedangkan Multi-Level Queue menunjukkan kinerja seimbang.

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Irawati; Sugiarti; Hayati, Lilis Nur; Tawetubun, Siti Safira; Ahmad, Nur Asy Syams Sam, Universitas Muslim Indonesia

Abstract

Diversitas mahasiswa di perguruan tinggi Indonesia menuntut mekanisme seleksi mahasiswa berprestasi yang objektif dan transparan, sementara praktik seleksi manual masih rentan terhadap subjektivitas dan inkonsistensi penilaian. Penelitian ini mengembangkan Decision Support System (DSS) berbasis Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) dengan kebaruan metodologis berupa integrasi delapan kriteria multidimensi yang mengombinasikan aspek akademik dan non-akademik dalam satu kerangka pengambilan keputusan terstruktur. Hasil implementasi menunjukkan bahwa sistem mampu meningkatkan konsistensi keputusan seleksi hingga 87% serta mereduksi waktu proses seleksi sekitar 60% dibandingkan metode konvensional. Temuan ini menegaskan bahwa DSS berbasis TOPSIS tidak hanya meningkatkan objektivitas penilaian, tetapi juga memberikan efisiensi operasional yang signifikan, sehingga berpotensi menjadi model pendukung keputusan adaptif dan aplikatif untuk standardisasi seleksi mahasiswa berprestasi di perguruan tinggi.

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Hidayah, Syarif; Irawati; Ramdaniah, Universitas Muslim Indonesia

Abstract

Desa Baolan Kabupaten Toli-Toli merupakan salah satu desa yang ada di Sulawesi Tengah. Proses pengaduan yang dilakukan oleh masyarakat desa masih secara manual yang harus datang secara langsung ketempatnya, dalam hal ini di kantor desa. Masyarakat desa jika ingin melaporkan pengaduan harus datang ke kantor desa. Hal ini menimbulkan keluhan masyarakat yang menumpuk di desa Baolan yang dianggap tidak efisien dari segi waktu, tenaga, dan biaya. Metode yang diterapkan pada perancangan aplikasi adalah metode agile, dan teknik pengumpulan data yang digunakan adalah observasi awal, interview dan pengumpulan literatur. Tahapan metode agile yang di gunakan yaitu perencanaan, implementasi, tes perangkat lunak, dokumentasi, development, dan pemeliharaan. Adapun hasil penelitian ini yaitu adanya desain aplikasi berbasis mobile, yang dapat digunakan pada tahap pembuatan aplikasi, desain user interface yang dirancang bersifat user friendly, sehingga memberikan kemudahan bagi pengguna aplikasi

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Raflisyah, Aliefian; Irawati; Manga', Abdul Rachman, Universitas Muslim Indonesia

Abstract

Kecamatan Mariso merupakan salah satu Kecamatan yang ada di Kota Makassar, Sulawesi Selatan. Proses pengaduan yang dilakukan oleh masyarakat Mariso masih secara manual yang harus dating secara langsung ketempatnya, dalam hal ini di kantor kecamatan. Masyarakat mariso jika ingin melaporkan pengaduan harus datang ke kantor kecamatan. Hal ini menimbulkan keluhan masyarakat menumpuk dan proses yang tidak efisien dari segi waktu, tenaga dan biaya. Metode yang diterapkan pada perancangan aplikasi adalah metode Waterfall, dan Teknik pengumpulan data yang digunakan adalah observasi awal, interview, dan pengumpulan literatur. Tahapan metode agile yang digunakan yaitu perencanaan, implementasi, tes perangkat lunak, dokumentasi, development, dan pemeliharaan. Tujuan dari penelitian ini adalah terbentuknya sebuah aplikasi berbasis mobile yang dapat digunakan masyarakat kecamatan Mariso Ketika ingin melakukan pengaduan. Adapun hasil penelitian ini yaitu adanya aplikasi berbasis mobile yang dapat digunakan pada tahap pembuatan aplikasi, desain user interface yang dirancang bersifat user friendly, sehingga memberikan kemudahan bagi pengguna aplikasi

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Hayati, Lilis Nur, Sistem Informasi, Fakultas Ilmu Komputer, Universitas Muslim Indonesia, Makassar, Indonesia; Irawati, Teknik informatika, Fakultas Ilmu Komputer, Universitas Muslim Indonesia, Makassar, Indonesia; Rahbiah, Sitti, Agribisnis, Fakultas Pertanian, Universitas Muslim Indonesia, Makassar, Indonesia; Mubaral, Mush'ab Al; Bahar, Rayhana, Teknik informatika, Fakultas Ilmu Komputer, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

Penyakit pada tanaman Vanilla planifolia menimbulkan kerugian ekonomi yang signifikan akibat rendahnya akurasi diagnosis dini di tingkat lapangan. Pendekatan konvensional berbasis pengamatan visual hanya mencapai akurasi sekitar 55–65% dan sangat bergantung pada pengalaman subjektif petani. Penelitian ini bertujuan mengembangkan sistem klasifikasi penyakit vanili berbasis Support Vector Machine (SVM) untuk meningkatkan akurasi dan konsistensi diagnosis pada kondisi lapangan. Dataset penelitian terdiri atas 1.616 sampel berlabel yang dikumpulkan melalui observasi lapangan di Desa Gattareng, Kabupaten Bulukumba, mencakup lima kelas kondisi tanaman, yaitu sehat (412), bercak daun (338), layu (301), busuk batang (279), dan akar busuk (286). Metodologi penelitian meliputi prapemrosesan data dengan deteksi outlier berbasis IQR, normalisasi Min–Max, penyeimbangan kelas, serta optimasi hiperparameter menggunakan grid search dengan 5-fold stratified crossvalidation. Hasil evaluasi menunjukkan bahwa model SVM dengan kernel polinomial derajat tiga mencapai kinerja klasifikasi yang stabil dengan akurasi 92,1% dan performa recall yang konsisten pada seluruh kelas penyakit. Berbeda dengan penelitian sebelumnya yang umumnya mengandalkan CNN pada dataset besar, penelitian ini mengusulkan pipeline SVM yang ringan dan replikatif, efektif pada dataset berukuran menengah dengan karakteristik data lapangan yang heterogen. Temuan ini menunjukkan potensi SVM sebagai pendekatan diagnosis penyakit vanili yang lebih realistis untuk konteks kebun, dengan peluang pengembangan lebih lanjut melalui perluasan dataset multi-lokasi dan peningkatan interpretabilitas model.

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Ariani, Chindy; Belluano, Poetri Lestari Lokapitasari; Astuti, Wistiani, Universitas Muslim Indonesia

Abstract

Setiap cafe ingin memiliki karyawan yang memenuhi kriteria dalam jumlah tertentu melalui decision maker atau pihak cafe. Hal tersebut juga dapat mempengaruhi kepuasan pelanggan cafe yang mendapatkan pelayanan terbaik dari karyawan cafe. Penelitian ini dibuat untuk membangun sebuah sistem pendukung keputusan calon karyawan di Cafe Dapur Ide Makassar secara objektif menggunakan metode Simple Additive Weighting (SAW). Dalam hal ini diharapkan penentuan calon karyawan menggunakan metode SAW dapat membantu dalam menentukan calon karyawan tepat sesuai dengan kriteria yang telah ditentukan oleh pihak cafe dengan melihat ranking atau total skor tertinggi agar karyawan yang akan diterima dapat memberikan pelayanan yang terbaik dan layak diterima

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Azis, Huzain; Salim, Yulita, Department of Informatics Engineering, Universitas Muslim Indonesia, Jl. Urip Sumoharjo No.km.5, Makassar, 90231, Indonesia; Puspitasari, Yustisia, Orthodontic Department, Dentistry Faculty, Universitas Muslim Indonesia, Jl. Urip Sumoharjo No.km.5, Makassar, 90231, Indonesia

Abstract

In this work, we explore the use of deep learning models for soft tissue profile classification in cephalometric images, an essential task in orthodontic treatment planning. To increase classification accuracy and to preprocess the data and remedy class imbalance, four deep learning architectures were used: Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNN), and Generative Adversarial Networks (GAN). RNN performed best, with an accuracy of 59.76%, followed by LSTM, with 51.22%. CNN showed signs of overfitting, with a validation accuracy of 95.61% but a testing accuracy of 47.56%. Although the GAN model was used to augment the data and reduce class imbalance, it did not improve classification performance. We used evaluation metrics like precision, recall, and F1 Score to assess how well the models performed. Future work will involve larger datasets and advanced regularization techniques to improve generalizability and clinical relevance. Future directions should focus on expanding the dataset to enhance model accuracy and robustness. Additionally, exploring advanced techniques is essential to prevent overfitting. Furthermore, implementing more sophisticated data augmentation methods and continuously optimizing architectures are recommended to achieve improved classification outcomes and support clinical applications for orthodontic tasks.

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Ayyub, Muhammad Algifari; Salim, Yulita; Mude, Muh. Aliyazid, Universitas Muslim Indonesia

Abstract

Penelitian ini bertujuan untuk membandingkan kinerja antara Chatbot Meta AI dan Grok AI dalam merespons pola perintah yang serupa. Metode yang digunakan meliputi Metode Black Box dan algoritma K-Means Clustering. Hasil pengujian menunjukkan bahwa Grok AI unggul dalam memberikan penjelasan mendalam dan menyajikan jurnal penelitian terkini. Keunggulan ini ditemukan berkat kemampuan pemrosesan bahasa alami yang handal serta integrasi teknologi real-time melalui platform X (Twitter). Di sisi lain, Meta AI menunjukkan keunggulan dalam efisiensi penggunaan, kecepatan respons yang mencapai 11,11 detik, serta kreativitas dalam output, yang didukung oleh optimasi model bahasa Llama 3. 2. Proses clustering dengan K=2 berhasil mengelompokkan data menjadi dua cluster: Cluster 0, di mana Chatbot Grok AI unggul dalam kedalaman informasi yang terkini; dan Cluster 1, di mana Chatbot Meta AI lebih cepat, efisien, serta menawarkan solusi inovatif. Analisis faktor mengungkapkan bahwa perbedaan kinerja ini dipengaruhi oleh desain arsitektur model, strategi integrasi data, serta prioritas pengembangan masing-masing platform

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Syafie, Lukman; Awangga, Narendra; Salim, Yulita, Universitas Muslim Indonesia

Abstract

Daily rainfall data plays an important role in hydrological and climatological analysis, especially in tropical regions characterised by high rainfall variability and sharp seasonal changes. However, observational data often has gaps, which can reduce model accuracy and obscure relevant climatological signals. This study addresses these issues by applying the Trend-Based Adaptive Window Imputation (TBaWI) method, an adaptive imputation approach that considers local temporal trends and seasonal dynamics in estimating missing rainfall values. This method was tested using CHIRPS data for the Makassar region for the period 2014–2023 with synthetic data loss scenarios of 10%, 15%, 20%, and 25%. The results show that TBaWI consistently provides a lower Mean Absolute Error (MAE) value, namely 6.14–7.65 mm, compared to linear interpolation, which produces 6.46–7.75 mm. The SMAPE value of TBaWI is also lower, for example 33.16% in the 15% data loss scenario, compared to interpolation at 35.06%. In addition, this method showed an improvement in the ability to identify dry days through the Zero Hit Rate (ZHR), which reached 60.08% in the 20% data loss scenario, higher than the interpolation of 58.32%, while the Rainy Hit Rate (RHR) remained in a stable range of 79–88%. These findings indicate that TBaWI is more effective in maintaining climatological consistency and numerical accuracy of tropical rainfall data. Further research is expected to integrate spatial aspects and optimise machine learning-based parameters to improve the generalisation of the method under various climatic conditions.

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Yunus, Sattar, Department of Environmental Engineering, Faculty of Engineering, Universitas Muslim Indonesia, Indonesia; Marzuki, Ismail, Department of Chemical Engineering, Faculty of Engineering, Universitas Fajar, Indonesia; Hasnawi, Mardiyyah, Department of Computer Science, Faculty of Computer Science, Universitas Muslim Indonesia, Indonesia; Anggamulia, Muh Ilham, Department of Environmental Engineering, Faculty of Engineering, Universitas Muslim Indonesia, Indonesia

Abstract

Atmospheric particulate matter poses significant risks to the environment and human health, depending on its mass concentration and chemical composition. This study aims to quantify the mass concentrations of total suspended particles (TSP) and particulate matter with an aerodynamic diameter of less than 10 µm (PM10), as well as to determine their elemental composition. Particulate samples were collected from three ambient air monitoring sites within the City of Makassar, the capital of South Sulawesi Province, during the peak dry season (May to September 2025). The measured concentrations of PM10 and TSP were evaluated against the Primary Impact Zone (PIA) standards of the Indonesian Air Quality (IAQ) guidelines. A high-volume air sampler (HVAS) was employed to collect particulate samples, and gravimetric analysis was conducted to determine their mass concentrations. The elemental composition of the particles was analyzed using scanning electron microscopy (SEM) coupled with energy-dispersive X-ray spectroscopy (EDS). The mean concentrations of PM10 and TSP were 34.20 µg/m3 and 73.22 µg/m3, respectively. The findings indicate that the mean mass concentrations of both TSP and PM10 were below the Indonesian regulatory standards, although temporal and spatial variations were observed. The identified elemental constituents included Al, Ca, K, Na, Rb, and Sr, suggesting contributions from crustal sources. These results provide valuable insights into the characteristics of particulate matter in residential areas of Makassar and support the development of improved, source-oriented air quality management strategies.

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Hasnawi, Mursyid, Department of Civil Engineering, Faculty of Engineering, Universitas Muslim Indonesia, Makassar, Indonesia; Hasnawi, Mardiyyah, Department of Computer Science and Information Technology, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

Reservoir sedimentation critically threatens the long-term sustainability of water infrastructure in tropical monsoon basins. This study presents an integrated assessment of sediment accumulation, morphodynamic evolution, and storage capacity loss in the Bili-Bili Reservoir, Indonesia, based on multi-year bathymetric surveys conducted in 1997 (baseline) to 2024. Multitemporal bathymetric datasets were combined with three-dimensional morphometric reconstruction and exponential predictive modeling (R² = 0.94) to quantify spatiotemporal sediment distribution and forecast future storage decline. The results indicate a 35% reduction in total storage from 375 × 10⁶ m³ at commissioning to 243.85 × 10⁶ m³ in 2024 with projections suggesting a 50% loss by 2048 under current management practices. The 2004 Mount Bawakaraeng landslide contributed an unprecedented sediment inflow of approximately 3.8 × 10⁶ m³ yr⁻¹, while post-2011 sediment-control structures retained approximately 57% of total sediment inflow. Bathymetric evidence further demonstrates progressive upstream deltaic aggradation, reducing hydraulic efficiency and effective storage capacity. Unlike previous short-term or empirical studies, this research introduces a multi-decadal, data-driven framework that explicitly links sedimentation processes with infrastructure performance and management outcomes. The findings provide a quantitative benchmark for reservoir sustainability and inform adaptive sediment management strategies consistent with Indonesia’s IWRM framework. This approach offers a transferable methodology for sediment-prone reservoirs across tropical regions under increasing hydroclimatic variability.

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Mude, Muh Aliyazid; Umar, Fitriyani, Universitas Muslim Indonesia

Abstract

Masalah efisiensi pada identifikasi kendaraan secara manual telah diidentifikasi sebagai kendala utama dalam sistem keamanan. Sebuah perancangan sistem deteksi pemilik kendaraan berbasis Automatic Number Plate Recognition dan Internet of Things yang mengacu pada standar International Organization for Standardization / International Electrotechnical Commission 30141 telah dikembangkan dalam penelitian ini. Fokus utama riset dilakukan terbatas pada tahap perancangan prototipe menggunakan emulator fritzing guna memvalidasi arsitektur sistem secara virtual. Hasil perancangan menunjukkan bahwa berhasil divisualisasikan dengan tingkat akurasi logika pada 5 domain pada ISO/EIC 130141 yakni device layer, gateway & network, data management, application layer dan domain business layer Disimpulkan bahwa model perancangan pada emulator fritzing ini layak dan dijadikan acuan awal dalam pengembangan sistem keamanan kendaraan sebelum dilakukan implementasi pada perangkat fisik sehingga untuk pengembangan selanjutnya disarankan menggunakan elumator lainya agar ada gambaran yang jelas penggunaan tools berbasis IoT

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Akbar, Muh Ilham Nur Hidayat; Fattah, Farniwati; Gaffar, Andi Widya Mufila, Universitas Muslim Indonesia

Abstract

This study investigates the classification of Kodály hand signs using hand landmark features extracted from MediaPipe Hands. A landmark-based approach was chosen because it represents hand gestures as structured numerical features that are more efficient than raw image data. The objective of this study is to compare the performance of three tree-based machine learning algorithms, namely Decision Tree, Random Forest, and XGBoost, for classifying eight Kodály hand sign classes (Do–Do’). A dataset consisting of 8,000 samples was collected with a balanced distribution of 1,000 samples per class from 2 participants using the right hand only. Each sample is represented by 63 features derived from 21 hand landmarks with (x, y, z) coordinates. The data were divided using an 80% training and 20% testing holdout scheme and further validated using Stratified 5-fold cross-validation. Model performance was evaluated using accuracy, macro-precision, macro-recall, macro F1-score, and confusion matrix analysis. Experimental results show that XGBoost achieves the best performance with an accuracy of 0.9712 and macro F1-score of 0.9714 in the holdout evaluation and remains stable in cross-validation with an accuracy of 0.9768±0.0046. Random Forest achieved slightly lower performance, while Decision Tree produced the lowest accuracy. Confusion matrix analysis indicates that most misclassifications occur between classes with similar landmark patterns, particularly La and Mi. Therefore, XGBoost is recommended as the most effective model for landmark-based Kodály hand sign classification.

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Anjasmara, Edo Ranov; Fattah, Farniwati; Gaffar, Andi Widya Mufila, Universitas Muslim Indonesia

Abstract

Waste management, particularly in sorting plastic bottle and non-bottle waste, remains a challenge in supporting effective recycling systems. This study aims to implement the MobileNetV2 model on the ESP32-CAM device using the Edge Impulse platform and to evaluate the model’s performance in real-time object classification. The dataset consists of 200 images containing bottle and non-bottle objects with variations in lighting conditions, shooting angles, and backgrounds. The model was trained using Edge Impulse and then converted into TensorFlow Lite format for deployment on the ESP32-CAM device.The training results show that the model achieved high performance with an accuracy of 92.50%, supported by an AUC of 0.97, precision of 0.98, recall of 0.97, and F1-score of 0.97. Based on the simplified confusion matrix with visual verification, the model achieved 100% accuracy in detecting bottle objects and 95% accuracy for non-bottle objects, with a 5% misclassification rate. However, during real-world implementation on the ESP32-CAM device, the model’s performance decreased to 65.7% accuracy due to differences between training and real-world conditions as well as hardware limitations.Despite this, the system successfully performed real-time image classification on an embedded device. This study demonstrates that the edge artificial intelligence approach using MobileNetV2 and Edge Impulse can be effectively applied to resource-constrained devices, although improvements are still needed in terms of model generalization and dataset diversity.

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Fattah, Farniwati; Gaffar, Andi Widya Mufila, Universitas Muslim Indonesia

Abstract

Vehicle detection in low-resolution images remains a significant challenge in computer vision, particularly for embedded devices such as ESP32-CAM with limited computational resources and simple image resolution. This study evaluates the performance of YOLOv8 on low-resolution QVGA (320 × 240 pixels) images for vehicle detection and classification. The dataset was independently collected in a controlled laboratory environment using miniature vehicles, covering four vehicle classes (motorcycle, car, bus, and truck) with a total of 4,000 images and a 70:20:10 data split. A pretrained YOLOv8 model was fine tuned for 100 epochs and tested on an ESP32-CAM prototype. The evaluation results demonstrate excellent performance, achieving precision of 0.999, recall of 1.000, mAP@0.5 of 0.995, and mAP@0.5-0.95 of 0.995 on the validation data, as well as real-time detection accuracy of 97% for motorcycles and cars, and 99% for buses and trucks. These findings indicate that YOLOv8 can deliver reliable vehicle detection performance on low-resolution images and is suitable for implementation in embedded device-based systems

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Zulfikar, Achmad ; Fattah, Farniwati; Gaffar, Andi Widya Mufila, Universitas Muslim Indonesia

Abstract

Introduction: Angklung is a traditional Indonesian musical instrument that continues to evolve through digital technology. However, computer vision–based gesture recognition for controlling physical angklung instruments remains limited. This study investigates landmark-based recognition of Kodály hand signs and evaluates its application for real-time angklung interaction. Method: Hand landmarks were extracted using MediaPipe Hands from RGB camera input. Each gesture was represented by 63 normalized numerical features derived from 21 landmarks. The dataset consists of 8,000 images representing eight Kodály gesture classes (Do–Do'). Gesture classification was performed using the Extreme Gradient Boosting (XGBoost) algorithm. Model evaluation applied a subject-independent two-fold scheme using accuracy, precision, recall, F1-score, and confusion matrix analysis. Real-time system trials were conducted under different lighting conditions and capture distances, and TCP communication with an ESP32 controller was evaluated. Results: The model achieved 96.63% accuracy in Fold 1 and 96.40% in Fold 2. Misclassifications were mainly observed between visually similar gestures, particularly La and Mi. Separate real-time system trials showed consistent recognition under bright lighting, while accuracy decreased under dim lighting, especially for Do (90%) and Mi (86.7%). Gesture recognition remained reliable up to approximately 1.5 m. TCP testing over 200 command events recorded 0% failed acknowledgments with a mean round-trip time of 87.36 ms. Conclusion: These indicate that landmark-based Kodály gesture classification using MediaPipe Hands and XGBoost can support real-time angklung interaction under controlled conditions, although improvements are needed for low-light environments and visually similar gestures

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Hasanuddin, Tasrif; R, Muhammad Alif Maulana; Darwis, Herdianti; Gaffar, A. Widya Mufila; Satra, Ramdan; Fattah, Farniwati, Universitas Muslim Indonesia

Abstract

The threat of Distributed Denial of Service (DDoS) attacks is a serious challenge to network stability and security, especially in centralized Software Defined Networking (SDN) architectures. This study aims to evaluate and compare the performance of three classification algorithms in detecting DDoS attacks, namely Fully Connected Neural Network (FCNN), Extreme Gradient Boosting (XGBoost), and Random Forest. The dataset used is public SDN network traffic data that has gone through a normalization and feature selection process to improve model efficiency and accuracy. The evaluation was conducted on four scenarios of training and test data sharing ratios (90:10,80:20,70:30, and 60:40) using accuracy, precision, recall, and F 1-score metrics. The test results show that Random Forest obtained the highest accuracy of 99.61 %, followed by FCNN at 94.95 %, and XGBoost at 99.01 %. Despite the slightly lower accuracy, XGBoost showed the best computational efficiency with the fastest training time of 0.94 seconds and the fastest prediction time of 0.03 seconds, making it an ideal choice for real-time detection system implementation. These findings contribute to the development of a precise, efficient, and adaptive DDoS detection system. Future research is recommended to explore advanced feature selection methods such as Recursive Feature Elimination (RFE) to improve the overall performance and efficiency of the model.

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Fajri; As’ad, Ihwana; Abdullah, Syahrul Mubarak, Universitas Muslim Indonesia

Abstract

Era digital telah membawa angin segar bagi perkembangan global usaha, khususnya pelaku Usaha Mikro, Kecil, serta Menengah (UMKM). Teknologi yang sekarang semakin canggih serta sangat simpel diakses telah sebagai katalisator bagi pertumbuhan dan perkembangan UMKM. Dulu, UMKM mungkin sangat terbatas di pasar lokal dan mempunyai ketergantungan yang sangat tinggi di interaksi secara langsung. tetapi, dengan adanya teknologi UMKM sekarang bisa menjangkau pasar yg jauh lebih luas. menggunakan memanfaatkan teknologi secara efektif UMKM bisa tumbuh serta berkembang pesat. tetapi, UMKM juga perlu terus beradaptasi dengan perkembangan teknologi yg sangat cepat. Penggunaan E-Commercee sangat bermanfaat untuk peningkatan penjualan, memperluas jangkauan pasar, efisiensi operasional, dan penghematan biaya . akan tetapi masih para pelaku perjuangan yg menjalankan penyewaan alat outdoor secara manual sehingga mengakibatkan beberapa kendala mirip berita stok tak sesuai waktu nyata, manajemen pencatatan yang tidak rapi, sebagai akibatnya terjadi resiko double booking. kondisi ini sangat berdampak terhadap turunnya kepuasan pelanggan. oleh sebab itu, sangat diharapkan sebuah solusi digital yang bisa mengatasi pertarungan tadi sekaligus bisa menyampaikan pengalaman kepada pengguna yg optimal. Peneltian ini juga memakai metode Design Thinking yang terdiri asal beberapa tahapan empathize, define, ideate, prototype, dan test. Metode ini dipilih sebab sangat mampu membantu pengembang memahami kebutuhan pengguna secara pribadi serta bisa menciptakan sebuah desain yg sinkron dengan konflik yg terjadi. Selain itu, penelitian ini sangat menekankan pada kualitas desain secara eksklusif dan pengalaman pengguna (UI/UX) buat menaikkan kepuasan serta minat dan loyalitas pelanggan. yang akan terjadi berasal penelitian ini memberikan bahwa perancangan perangkat lunak penyewaan alat outdoor berbasis mobile mampu menjadi suatu solusi yg sangat efektif buat menaikkan efisiensi pengelolaan, memberikan kemudahan buat pengguna dalam melakukan pencarian, pemesanan, dan pembayaran. Fitur tambahan juga berupa integrasi kalender ketersediaan serta metode perhitungan jarak (Haversine) pula menyampaikan nilai lebih, terutama dalam membanatu pelanggan menemukan penyewaan outdoor terdekat. menggunakan demikian, perangkat lunak ini tidak hanya mendukung transformasti digital UMKM, namun pula mampu menaikkan daya saing antar usaha penyewaan alat outdoor pada era digital

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Akbar, Firman; Purnawansyah; Faradibah, Amaliah, Universitas Muslim Indonesia

Abstract

Penelitian ini bertujuan untuk menganalisis sentimen masyarakat di media sosial X terhadap kebijakan penundaan pengangkatan Calon Pegawai Negeri Sipil (CPNS) dan Pegawai Pemerintah dengan Perjanjian Kerja (PPPK) di Indonesia menggunakan algoritma Naïve Bayes. Data dikumpulkan melalui proses crawling pada media sosial X yang menghasilkan 283 komentar terkait isu tersebut. Proses analisis meliputi pre- processing data (pembersihan, case folding, filtering, stemming, dan tokenizing), pelabelan sentimen positif dan negatif, ekstraksi fitur menggunakan metode TF-IDF, klasifikasi dengan algoritma Multinomial Naïve Bayes, serta evaluasi model menggunakan metrik akurasi, presisi, recall, dan F1-score. Hasil penelitian menunjukkan bahwa algoritma Naïve Bayes mampu melakukan klasifikasi sentimen secara efektif terhadap opini publik yang beragam, mulai dari dukungan, kritik, hingga pandangan netral terhadap kebijakan penundaan tersebut. Temuan ini diharapkan dapat menjadi masukan bagi pemerintah dalam merumuskan kebijakan yang lebih responsif terhadap opini publik

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Irsyad, Muhammad; Satra, Ramdan; Faradibah, Amaliah, Universitas Muslim Indonesia

Abstract

This study aims to create and implement a network monitoring system with a telegram bot using a public IP on the 2nd floor access point of FIKOM UMI. The method used is the PPDI method. This study aims to monitor the computer network connection system at the nearest access point on the 2nd floor of FIKOM UMI. Currently, the process of monitoring internet network constraints in the laboratory is still being checked manually, so the process is slow and less efficient. The monitoring system uses the Telegram API Bot and L2tp VPN with Mikrotik to obtain information in the form of real-time telegram notification messages to network administrators. The telegram API bot will send notification messages when internet connection problems occur, both down and up. The results of the internet network monitoring system are very helpful for network administrators in searching for information when internet connection problems occur on the 2nd floor of FIKOM UMI

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Iqbal, Muhammad Farhan ;Fattah, Farniwati, Universitas Muslim Indonesia

Abstract

Seiring dengan perkembangan zaman, maka kebutuhan manusia akan alat pengawasan/pengamanan ikut berkembang, salah satunya contoh yaitu perkebunan. Permasalahan yang sering terjadi pada saat ini adalah kasus gagal panen dikarenakan hama yang seringkali terjadi karena kurangnya pengawasan. Oleh karena itu, karya tulis ini bertujuan untuk merancang sebuah sistem keamanan/pengawasan pada kebun yang dapat dikendalikan secara jarak jauh menggunakan perangkat Smartphone, Sensor PIR, ESP32, Panel Surya, dan Buzzer. Sistem ini dirancang untuk memberikan solusi efektif dalam mengawasi bahkan mengusir hewan penganggu dengan fitur kendali yang fleksibel dan responsif. ESP32 akan bertindak sebagai otak utama sistem, mengumpulkan dan menganalisis data dari Sensor PIR untuk mendeteksi pergerakan. memungkinkan pengguna untuk mengakses sistem melalui perangkat seluler. Sistem ini juga dilengkapi dengan Buzzer untuk memberikan notifikasi audio dalam situasi darurat atau terdeteksi sebuah gerakan dan notifikasi di Aplikasi Telegram. Melalui integrasi komponen-komponen ini, diharapkan sistem dapat memberikan pengawasan yang handal dan memberikan pemilik kebun kendali penuh melalui aplikasi seluler

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Kaunar, Rifaldi Fitra Akbar; Satra, Ramdan; Fattah, Farniwati, Universitas Muslim Indonesia

Abstract

This research aims to design an Arduino-based Solar Tracking system as a monitoring center, allowing users to monitor panel movements. The method used in this research is an experimental method to determine the performance of the solar tracking tool that has been made, by using a servo motor as a panel driver, LDR sensor and MPU6050 with wireless communication can display information to the user's PC/Smartphone through the blynk application. Where users can monitor panel movements against light in real-time. Based on the test results, the prototype that has been made can be used to monitor solar tracking in real time with an average error of 1.55%.

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Zalzabila, Izzahara ; Erick Irawadi, Alwi; Fattah, Farniwati, Universitas Muslim Indonesia

Abstract

The advancement of technology has increased convenience in various daily activities, but it has also heightened the potential for misuse in criminal acts such as fraud, corruption, theft, and the dissemination of hoax information. Digital evidence, including files, images, audio, and video, often plays a crucial role in criminal investigations. Each type of digital evidence requires a specific analytical method, one of which is audio forensics. Audio forensics is a sound analysis method aimed at enhancing speech clarity and reducing noise, thereby providing important clues in investigative processes. Tools such as Praat are used to compare allegedly manipulated voice recordings with original voice samples. This study aims to identify digital evidence in the form of voice recordings using Praat and audio forensic methods. By analyzing voice characteristics such as pitch, formant, and spectrogram, this research seeks to determine the similarity between evidentiary recordings and reference samples as well as to perform audio enhancement. The results indicate that in the pitch analysis stage, the samples of Yuki, Sakinah, Sonia, Marwah, and Salsa were identical to the evidence recordings. Furthermore, formant analysis revealed that the samples of Yuki, Sonia, and Salsa were identical to the evidence, while spectrogram analysis showed that only the Salsa sample matched the evidence recordings.

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Sari, Risa Hasmila; Harlinda; Syahar, A. Ulfah Tenripada, Universitas Muslim Indonesia

Abstract

Perkembangan teknologi informasi mendorong pemanfaatan website sebagai media penyampaian informasi yang efektif, termasuk pada bidang jasa event organizer. Namun, penyampaian informasi yang masih tersebar melalui media sosial seringkali menyebabkan pengguna kesulitan dalam memperoleh informasi yang lengkap, terstruktur, dan mudah dipahami. Penelitian ini bertujuan untuk merancang User Interface (UI) dan User Experience (UX) pada website Searah Management sebagai media informasi layanan event organizer dengan menggunakan metode Design Thinking yang meliputi tahapan empathize, define, ideate, prototype, dan testing. Pengumpulan data dilakukan melalui observasi dan wawancara kepada calon klien serta pengguna yang pernah menggunakan jasa event organizer. Hasil analisis menunjukkan bahwa pengguna membutuhkan media informasi yang terpusat, jelas, dan mudah diakses. Selain itu, sistem juga dirancang dengan halaman admin yang berfungsi untuk mengelola dan memperbarui informasi seperti layanan dan galeri kegiatan. Prototipe dibuat menggunakan Figma dan diuji menggunakan platform Maze serta kuesioner skala Likert. Hasil pengujian menunjukkan skor Maze sebesar 84 pada sisi pengguna dan 96 pada sisi admin, serta mayoritas responden memberikan penilaian positif terhadap kemudahan penggunaan dan kejelasan informasi. Dengan demikian, rancangan website yang dikembangkan mampu membantu pengguna dalam memperoleh informasi secara efektif serta memudahkan admin dalam mengelola konten website

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Hidayat, David ; As’ad, Ihwana ; Syahar, A. Ulfah Tenripada, Universitas Muslim Indonesia

Abstract

Penelitian ini bertujuan untuk menganalisis tingkat kepuasan pengguna terhadap Sistem Informasi SIFAISAL pada unit PPK IRWA I BBWS Pompengan Jeneberang. SIFAISAL digunakan untuk verifikasi pertanggungjawaban anggaran kegiatan kontraktual, namun belum pernah dilakukan evaluasi pasca implementasi. Penelitian ini menggunakan kerangka kerja PIECES yang terdiri dari enam variabel: Performance, Information, Economy, Control, Efficiency, dan Service. Metode penelitian yang digunakan adalah deskriptif kuantitatif dengan menyebarkan kuesioner kepada 44 responden yang ditentukan melalui rumus Slovin. Hasil penelitian menunjukkan bahwa tingkat kepuasan pengguna berada pada kategori" Puas" untuk variabel Performance (4, 00), Information (4, 15), dan Economy (4, 05). Sementara itu, variabel Control (4, 20), Efficiency (4, 29), dan Service (4, 24) berada pada kategori" Sangat Puas". Secara keseluruhan, sistem SIFAISAL telah memenuhi kebutuhan pengguna dalam mendukung proses administrasi anggaran, meskipun tetap memerlukan pemeliharaan rutin untuk menjaga stabilitas kinerja sistem.

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Nengsih, Ratika; Kasim, Sinta; Syahar, A. Ulfah Tenripada, Universitas Muslim Indonesia

Abstract

Desa Garaupa Raya di Kabupaten Kepulauan Selayar menghadapi tantangan ekonomi sebagai wilayah pesisir terpencil dengan infrastruktur terbatas dan potensi kelapa yang belum dimanfaatkan optimal. Program pemberdayaan masyarakat ini bertujuan meningkatkan kemandirian ekonomi keluarga melalui produksi Virgin Coconut Oil (VCO) berbasis nilai Islam dan kearifan lokal. Program melibatkan dua mitra utama: PKK dengan 22 anggota aktif dan Karang Taruna dengan 10 anggota. Menggunakan pendekatan Participatory Action Research, kegiatan dilaksanakan melalui lima tahap: sosialisasi, pelatihan teknis dan manajerial, penerapan teknologi tepat guna, monitoring dan evaluasi, serta perencanaan keberlanjutan. Program menyediakan alat pemeras santan semi-hidrolik, wadah fermentasi, dan pelatihan pemasaran digital. Hasil menunjukkan peningkatan signifikan: kapasitas produksi meningkat dari 1-2 liter menjadi 5-10 liter per bulan, 90% anggota menguasai pembukuan sederhana, dan produk dipasarkan melalui platform TikTok dan Shopee dengan merek" Garaupa Raya VCO". Program berhasil mentransformasi masyarakat dari ekonomi subsisten menjadi wirausaha produktif berbasis nilai Islam dan kearifan lokal, berkontribusi pada pencapaian SDGs 1, 5, 8, dan 12.

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Arifin, Fadil Abdillah; Nurana, Sitti ; Syahar, A. Ulfah Tenripada, Universitas Muslim Indonesia

Abstract

Kelompok kader merupakan anggota masyarakat yang ikut berperan dalam pembangunan desa dalam berbagai aspek, termasuk kesehatan gigi dan mulut. Desa Tellumpoccoe memiliki kelompok kader aktif yang berjumlah 25 orang namun belum pernah mendapatkan penyuluhan/pelatihan mengenai kesehatan gigi. Hal ini selaras dengan tingginya kunjungan poli gigi Puskesmas Marusu, masyarakat yang datang memiliki keluhan gigi berlubang yang perlu perawatan penambalan ataupun pencabutan. Sehingga dapat dikatakan status kesehatan gigi dan mulut masyarakat Desa Tellumpoccoe cukup rendah. Untuk mengatasi hal ini tim pengabdian kepada masyarakat ingin memberdayakan kelompok kader untuk kemudian dibekali dengan berbagai edukasi mengenai metode pencegahan gigi berlubang serta hubungannya dengan kualitas hidup penderita, dalam rangka mendukung program pemerintah Indonesia Bebas Karies 2030. Selain itu, kelompok kader juga akan diperkenalkan dengan inovasi teknologi augmented reality yang akan digunakan sebagai media penyuluhan. Kelompok kader juga akan dibekali dengan keterampilan dan manajemen penyuluhan yang baik, sehingga nantinya kelompok kader dapat menyelenggarakan kegiatan penyuluhan secara mandiri, konsisten, dan berkelanjutan. Hasil dari kegiatan ini menunjukkan adanya peningkatan pada aspek sosial kemasyarakatan dan aspek manajemen. Terjadi peningkatan pengetahuan kelompok kader sebagai mitra mengenai kesehatan gigi dan mulut serta peningkatan kemampuan mitra dalam menyelenggarakan kegiatan penyuluhan secara mandiri.

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Ihsan, Muhammad, Alwi, Irawadi Erick, Salim, Yulita, Universitas Musllim Indonesia

Abstract

Identity management in higher education still generally relies on physical cards and centralized systems that are vulnerable to loss, forgery, and limited independent verification. This study aims to design and implement a blockchain-based digital student identity card system that supports integrated credential issuance, verification, and revocation. The study uses the Design Science Research Methodology (DSRM) through the stages of problem identification, design, prototype development, demonstration, and evaluation. The system is built using a hybrid architecture, in which student identity data are stored off-chain, while credential hashes and revocation status are recorded on-chain through a smart contract deployed on the Polygon Amoy Testnet. Credentials are issued in the form of JWT-based Verifiable Credentials and signed using the ES256K algorithm. The test results show that the system successfully implements its main functions, namely credential issuance, verification, and revocation. Performance testing also shows that verification can be carried out without incurring gas costs and with a response time that remains feasible for practical use. Overall, the developed system is feasible to support digital student identity services with better credential integrity and easier verification.

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Asran, Asran; Alwi, Erick Irawadi; Azis, Husain, Universitas Muslim Indonesia;

Abstract

Steganografi berfungsi sebagai teknik anti-forensik yang memungkinkan pelaku kejahatan menyembunyikan informasi dalam pesan lain, hal tersebut dapat menyulitkan penyelidik untuk menemukan bukti sebenarnya atas suatu kejahatan. Oleh karena itu, penyelidik harus mengenali dan memanfaatkan alat yang tepat untuk ekstraksi guna mengakses pesan yang melibatkan steganografi. Penelitian ini bertujuan untuk menganalisis bukti digital dengan menerapkan metode forensik statis berdasarkan kerangka kerja yang disiapkan oleh National Institute of Standards and Technology (NIST), penelitian ini juga berfokus pada proses ekstraksi file yang disusupi menggunakan steganografi, yang dilakukan berdasarkan skenario kejahatan. Alat yang dipakai dalam penelitian ini mencakup FTK Imager, Autopsy, HashMyFiles, USB Write Blocker, WinHex, Stegspy, serta InvisibleSecrets. Analisis proses ekstraksi menunjukkan bahwa, dari 10 file yang diperiksa sesuai dengan skenario kasus, 9 file berhasil diekstraksi dan menghasilkan tingkat keberhasilan sebesar 90%. Sebaliknya, 10% file yang tersisa tidak berisi data atau informasi tersembunyi yang relevan. Dari hasil tersebut dapat disimpulkan bahwa file steganografi yang berhasil diekstrak dapat dijadikan sebagai alat bukti materiil hukum yang sah.

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Shantika, Shantika; Alwi, Erick Irawadi; Mansyur, St Hajrah, Universitas Muslim;

Abstract

Inventory management at Bengkel Tho’Bone was previously conducted manually, which often resulted in data inconsistencies, delays in stock monitoring, and difficulties in generating accurate inventory reports. This study aims to design a web-based inventory management system using the Agile method as a software development approach to improve efficiency and accuracy in stock management. The study applies the CodeIgniter framework as the system development framework because it adopts the Model–View–Controller (MVC) architecture, which facilitates the separation of business logic, user interface, and data processing. The system development process is carried out through the stages of planning, design, development, testing, and evaluation, which are performed repeatedly according to user requirements. The system is designed to assist administrators and staff in managing item data, suppliers, as well as incoming and outgoing stock transactions. The results show that the developed system is capable of automating stock recording, providing restock notifications when inventory reaches the minimum threshold, and generating structured and easily accessible inventory reports. The implementation of this system improves operational efficiency, reduces human error, and enhances transparency in inventory management. Overall, the application of a web-based inventory management system using the Agile approach and the CodeIgniter framework has proven effective in optimizing inventory control and supporting accurate decision-making at Bengkel Tho’Bone.

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Malla, Agussalim Beddu; Abdullah, Syahrul Mubarak, Ibrahim, Bahtiar, Universitas Muslim Indonesia;

Abstract

This Community Service Program (PkM) aims to empower Islamic boarding school students (santri) through innovation in free-range chicken farming based on local feed and digital marketing as a step towards establishing an eco-green pesantren. The background of this activity lies in the great potential of pesantren as centers of education as well as drivers of community-based economic independence. However, the limited knowledge of santri in managing chicken farming and utilizing digital technology for marketing remains a challenge. The method applied includes education, technical training on free-range chicken farming with environmentally friendly local feed, production management assistance, and training in digital marketing through social media and online marketplaces. The results of the program indicate improved skills of santri in managing free-range chicken businesses, utilization of local feed ingredients that are more economical and sustainable, and the development of digital marketing strategies that expand market reach. The implementation of this program not only enhances the economic independence of santri but also supports the eco-green pesantren concept, which emphasizes environmental sustainability and technology-based entrepreneurship development

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Hakim, Nurul; Anraeni, Siska; St Hajra, Universitas Muslim Indonesia;

Abstract

Heat stress is a significant health problem in tropical work environments such as Indonesia, which can have serious physical and cognitive effects on workers. This study aims to implement the Certainty Factor method in a web-based expert system to address the uncertainty of clinical symptoms in accurately diagnosing heat stress conditions. System knowledge was acquired from general practitioners covering 7 types of diseases and 35 clinical symptoms. The research methods included knowledge acquisition, knowledge modeling, method implementation, and accuracy testing. The evaluation was conducted by comparing the system's diagnosis results with 20 test cases validated by experts. The results showed that the system successfully identified 18 cases correctly and 2 cases incorrectly. Based on these results, this expert system has an accuracy rate of 90%. This accuracy achievement is competitive and in line with previous studies that implemented the Certainty Factor in other health domains. It is concluded that the implementation of this method is feasible and effective in providing an initial diagnosis of heat-related illnesses, thereby assisting healthcare workers and workers in high-risk environments in making appropriate treatment decisions.

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Marlang, Muh Fatwah Fajriansyah; Darwis, Herdianti; Azis, Huzain; Manga, Abdul Rachman, Universitas Muslim Indonesia;

Abstract

Speech in the Bugis-Makassar dialect poses a major challenge for Automatic Speech Recognition (ASR) systems, particularly in detecting clitic particles that are essential to utterance meaning but do not exist in standard Indonesian. This study investigates the effect of synthetic speech data augmentation using OpenAI's gpt-4o-mini-tts on ASR performance for this dialect, testing four ratios of synthetic-to-real data (0%, 50%, 100%, and 150%) through fine-tuning of the wav2vec2-large-xlsr-indonesian model. Evaluation was conducted using Word Error Rate (WER), Character Error Rate (CER), and Particle Detection Rate (PDR) as a dedicated metric for clitic particle detection. Results show that augmentation at a 50% ratio produced consistent improvements across all metrics compared to baseline, with WER decreasing by 3.47 percentage points and PDR increasing by 3.62 percentage points. Further increases in

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Lahuddin, Harlinda, Program Studi Sistem Informasi, UniversitasMuslim Indonesia, Makassar, Indonesia.; Astuti, Wistiani, Program Studi Teknik Informatika, Universitas Muslim Indonesia, Makassar, Indonesia.

Abstract

Program Pelatihan Computational Thinking dan Problem Solving untuk Generasi Muda Kedah dilaksanakan sebagai respons terhadap rendahnya literasi digital di daerah rural Malaysia, khususnya di Sekolah Kebangsaan Ulu Sedaka, Yan, Kedah. Tujuan program adalah membekali siswa dengan keterampilan berpikir komputasi yang mencakup decomposition, pattern recognition, abstraction, dan algorithmic thinking sebagai fondasi menghadapi era digital. Metode pelaksanaan meliputi baseline assessment, workshop interaktif, hands-on activities, serta evaluasi melalui pre-test dan post-test. Sebanyak 30 siswa dan 5 guru terlibat aktif dalam program selama 12 minggu. Hasil evaluasi menunjukkan peningkatan signifikan pemahaman computational thinking dari skor rata-rata 45,2 menjadi 78,6 dengan peningkatan 73,9 persen. Tingkat partisipasi mencapai 93,3 persen dengan kepuasan peserta 4,5 dari skala 5. Program berhasil mengembangkan modul pembelajaran digital dan panduan aplikasi yang dapat digunakan berkelanjutan. Kesimpulan menunjukkan bahwa pendekatan pembelajaran interaktif berbasis unplugged activities efektif meningkatkan kemampuan berpikir logis siswa di daerah dengan keterbatasan infrastruktur teknologi, serta memberikan dampak positif terhadap minat siswa pada bidang STEM dan kesiapan menghadapi transformasi digital.

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Manga, Abdul Rachman; Suat, Wahyu Kadri Rahmat; Azis, Huzain, Universitas Muslim Indonesia;

Abstract

SQL Injection (SQLi) remains a critical cybersecurity threat because it exploits vulnerabilities in user input validation and can compromise the confidentiality, integrity, and availability of information systems. This study compares Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) architectures for detecting malicious SQL queries under identical experimental conditions. Method A publicly available dataset containing 148,327 malicious and benign SQL query instances was preprocessed through missing-value removal, label encoding, tokenization, sequence transformation, padding, and embedding representation. LSTM and CNN models were evaluated using three train-test split scenarios of 70:30, 80:20, and 90:10. Performance was assessed using accuracy, precision, recall, F1-score, and confusion matrices, with the 80:20 split selected for detailed evaluation. Results and Discussion LSTM consistently achieved higher accuracy across the evaluated splits, ranging from 97.84% to 98.00%. Under the 80:20 configuration, LSTM achieved 97.86% accuracy, 99.34% precision, 96.55% recall, and a 97.92% F1-score, compared with CNN at 97.00%, 97.68%, 96.56%, and 97.12%, respectively. LSTM also reduced false positives from 356 to 99, demonstrating better discrimination between legitimate and malicious queries. Conclusion LSTM provides more reliable SQL Injection detection than CNN by better capturing sequential dependencies within SQL query structures, making it a promising approach for practical cybersecurity systems

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Syaifuddin, Muhamad Faza Almaliki; Nurrifqhi, Arian; Fadilah, Nur; Ramadhan, Ahmad Mufli; Manga, Abdul Rachman; Rakasyah, Athar Fathana; Taqwa, Abd Salam At; Ilmawan, Lutfi Budi; Ahmad; Khawaritzmi Abdallah, Universitas Muslim Indonesia;

Abstract

Perkembangan teknologi informasi, kecerdasan buatan (Artificial Intelligence), dan analisis data telah membawa perubahan besar dalam berbagai sektor kehidupan, mulai dari pendidikan, kesehatan, industri, transportasi, keuangan, hingga pemerintahan. Di tengah perkembangan tersebut, Machine Learning hadir sebagai salah satu cabang kecerdasan buatan yang memiliki kemampuan untuk mempelajari pola dari data dan menghasilkan prediksi maupun keputusan secara otomatis. Kemampuan ini menjadikan Machine Learning sebagai teknologi yang sangat penting dalam menghadapi tantangan era digital dan revolusi industri modern.

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Gobel, Fatmah Afrianty; Satra, Ramdan; Zakira, Putri; Zalsasabilah, Putri, Universitas Muslim Indonesia;

Abstract

Situs website dapat memberikan akses yang mudah dan aman bagi remaja khususnya santri untuk edukasi, konsultasi, dan memberikan informasi komprehensif serta meningkatkan pengetahuan dan kesadaran dalam pencegahan perilaku berisiko tertular HIV/AIDS di lingkungan santri. Santri merupakan remaja usia yang relatif rentan terpapar informasi dari berbagai platform media sosial. Penelitian ini bertujuan mengetahui pengaruh edukasi menggunakan website terhadap pengetahuan, sikap dan tindakan santri dalam upaya pencegahan HIV/AIDS. Metode penelitian quasi eksperimen dengan kelompok perlakuan dan kelompok kontrol. Populasi adalah santri pondok Pesantren Darul Aman Gombara Kota Makassar, sampel diambil secara purposive sampling sebanyak 20 orang kelompok perlakuan dan 20 orang kelompok kontrol. Kedua kelompok mengisi kuesioner pre test dan post test. Hasil penelitian memperlihatkan intervensi edukasi menggunakan website pada santri terbukti efektif meningkatkan pengetahuan dan sikap, sedangkan tindakan tidak menunjukkan perubahan signifikan.

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Machmud, Fikri Ramadhan; Satra, Ramdan; Mansyur, Hajrah, Universitas Muslim Indonesia;

Abstract

The Traditional Clothing Information System as an Icon of the Archipelago is designed to help communities throughout Indonesia overcome the obstacles in recognizing and learning about traditional clothing, its historical value and meaning in this digital era. Development is carried out according to the RAD principle, including Requirements Planning, System Design, Development Process and Feedback Collection, and Product Implementation and Completion. Testing using blackbox testing and questionnaires shows that this application is easy to use, effective, and attractive to the community. A web evaluation with 10 respondents was also conducted. The results show that respondents gave a positive assessment of the ease of use and design of the application. Of the 50 answers, 48% of respondents stated "Strongly Agree", 40% "Agree", 12% "Neutral", and 0% for "Disagree" and "Strongly Disagree". The average rating is 4.5 on a scale of 5.

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Nurjannah; Satra, Ramdan, Universitas Muslim Indonesia;

Abstract

Program Kemitraan Masyarakat (PKM) ini bertujua untuk meningkatkan kemampuan baca tulis Al-Qur’an pengurus dan anggota Majelis Taklim An Nur Desa Borikamase melalui pemanfaatan teknologi berbasis Android. Latar belakang kegiatan ini adalah keterbatasan metode pembelajaran yang efektif, keterbatasan tenaga pengajar, serta rendahnya pemanfaatan teknologi sebagai media pembelajaran mandiri. Meskipun aebagian besar anggota majelis taklim telah memiliki perangkat Android, mereka belum terbiasa menggunakan aplikasi berbasis Android untuk belajar baca tulis al Qur’an. Kondisi ini menyebabkan proses belajar mengaji cenderung bergantung pada pertemuan tatap muka yang bersifat terbatas dan tidak berkelanjutan. Pelaksanaan program meliputi beberapa tahapan, yaitu persiapan (identifikasi peserta dan pemilihan aplikasi), pelatihan penggunaan aplikasi Al-Qur’an digital, praktik baca

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Widiyanti, Nabila; Hasanuddin, Tasrif; Azis, Huzain, Universitas Muslim Indonesia;

Abstract

Managing multiple academic tasks with overlapping deadlines remains challenging for university students, while conventional task-management applications still require substantial manual organization and prioritization. This study develops an LLM-based AI Task Agent that enables conversational academic task management through Telegram and workflow automation. Method The proposed system integrates Telegram as the interaction interface, n8n for workflow orchestration, an LLM-based AI agent for natural-language interpretation and tool selection, Google Sheets for task-data operations, PostgreSQL for conversational memory, and scheduled workflows for automated reminders. The system supports Create, Read, Update, and Delete operations, contextual priority recommendations based on deadline, urgency, and lecturer strictness, and proactive reminders. Functional performance and response time were evaluated across the primary system functions. Results and Discussion Create, Read, Update, and Delete operations achieved 100% functional accuracy, while priority recommendation and automated reminder functions achieved 95%. Recorded processing times ranged from 3.1 to 3.5 seconds, with an average of approximately 3.32 seconds. The results demonstrate that separating LLM-based interpretation from predefined external tool execution enables reliable conversational task management while maintaining controlled data operations. Conclusion The proposed LLM-based AI Task Agent demonstrates the feasibility of integrating conversational interaction, executable task-management functions, contextua

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Atmajaya, Dedy; Basalamah, Abdullah; Kurniati, Nia; Iqbal, Muhammad; Jasmin, Thalita Sherly Putri, Universitas Muslim Indonesia;

Abstract

Temperature monitoring is critical in hydroponic cultivation because it influences nutrient solubility, dissolved oxygen, and root uptake, yet low-cost digital sensors commonly used in Internet of Things (IoT) systems may experience accuracy degradation during long-term deployment. This study develops a low-cost IoT monitoring platform for nutrient film technique (NFT) hydroponics and characterizes temperature-sensor drift under continuous operating conditions. Method The system employed two redundant temperature sensors in the nutrient channel and one ambient sensor, with measurements timestamped, filtered, stored locally at the edge, and visualized through a cloud dashboard. Temperature data were recorded hourly for 40 days, producing 960 observations per sensor. Drift was evaluated from the deviation between the primary and redundant channel sensors using mean absolute error, maximum absolute deviation, standard deviation, drift onset, and estimated drift rate. Results and Discussion The primary sensor showed a mean absolute deviation of 0.82 °C over the full monitoring period and a maximum deviation of 1.47 °C. Sensor agreement remained close during the first 10 days but diverged after approximately day 12, with late-period MAE increasing to 1.18 °C and an estimated drift rate of about 0.04 °C/day. Conclusion Long-term drift in low-cost temperature sensors can materially affect hydroponic monitoring accuracy, and redundant sensing provides a practical baseline for future adaptive edge-based calibration methods.

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Idris, Fairus P; Sharief, Suchi A ; Atmajaya, Dedy; Asrina, Andi, Universitas Muslim Indonesia;

Abstract

Exclusive breastfeeding is scientifically known to reduce child mortality; however, coverage remains low in Bulukumba Regency, which has the lowest rates in South Sulawesi Province, Indonesia. This study aimed to examine the influence of the Theory of Planned Behavior (TPB) on exclusive breastfeeding behavior among mothers in the region. A quantitative cross-sectional study was conducted with 161 mothers of infants aged 6–12 months. Data were collected using a validated and reliable questionnaire and analyzed using Structural Equation Modeling-Partial Least Squares (SEM-PLS). The findings indicate that maternal attitudes towards exclusive breastfeeding (β= 0.619) and perceived control (β= 0.338) had significantly positive effects on breastfeeding intention, whereas subjective norms were not significantly associated with breastfeeding intention (p= 1.000). Furthermore, attitudes, subjective norms

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Hasnawi, Mardiyyah; Astuti, Wistiani; Puspitasari, Andi; Kurniati, Nia; Indra, Dolly

Abstract

Data processing in waste bank management systems faces substantial challenges when extracting structured information from unstructured textual data containing transaction records, customer information, and waste categorization details. This study develops and validates an automated Named Entity Recognition (NER) system based on the IndoBERT transformer architecture to process textual records from Makassar waste bank operations. The approach fine-tunes the indolem/indobert-base-uncased model through domain-specific adaptation targeting waste management vocabulary and operational terminology. The dataset comprises 975 textual records collected from 8 Waste Bank Units in Makassar City. These records were systematically annotated using the BIO tagging scheme for eight entity types: B-LOCATION, B-PERSON, B-WASTE_CATEGORY, B-WASTE_TYPE, I-PERSON, I-WASTE_CATEGORY, I-WASTE_TYPE, and O. The dataset was partitioned into training (682 samples, 69.9%), validation (146 samples, 15.0%), and test sets (147 samples, 15.1%) using stratified sampling methodology with high inter-annotator agreement (κ=0.91). Experimental results show outstanding performance with 94.37% test accuracy, 90.65% precision, 94.37% recall, and 92.21% F1-score, outperforming general-purpose Indonesian NER approaches. Perfect performance was achieved for waste type recognition (B-WASTE_TYPE: 100% F1-score) and location identification (B-LOCATION: 100% F1-score), while waste category classification reached 96% F1-score. This implementation successfully automates entity extraction from Makassar waste bank textual data, reducing manual processing time by 95% while maintaining high accuracy levels. This research makes important contributions to Indonesian environmental natural language processing. These contributions include transformer adaptation methodologies for resource-constrained domains, validated IndoBERT performance on Indonesian waste bank data, the first Indonesian waste management NER dataset, and demonstrated feasibility of NLP-based environmental policy systems.

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Ashar, Muh As' ad; Salim, Yulita; Alwi, Erick Irawadi

Abstract

The Free Nutritious Meal Program (MBG) is a government initiative aimed at improving public nutritional quality and reducing stunting rates in Indonesia. The implementation of this program has generated various public responses, particularly those expressed through social media platform X. Therefore, sentiment analysis is required to identify public opinion tendencies toward the program. This study aims to analyze public sentiment toward the Free Nutritious Meal Program using the Bidirectional Encoder Representations from Transformers (BERT) method, specifically the IndoBERT model. The dataset consists of 10,925 tweets collected through scraping using the X API and a public dataset from Kaggle. The data were processed through preprocessing stages including cleaning, case folding, and normalization. Sentiment labeling was conducted using a lexicon-based approach combined with manual labeling into three sentiment classes, positive, negative, and neutral. To address data imbalance, random oversampling was applied prior to model training. Model performance was evaluated using a confusion matrix with accuracy, precision, recall, and F1-score metrics. The results show that the IndoBERT model performs well in classifying public sentiment, achieving an accuracy of 90.2%, precision of 90.4%, recall of 90.2%, and an F1-score of 90.3%. Overall, public sentiment toward the Free Nutritious Meal Program is predominantly positive.

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Mz, Muhammad Fahd; Salim, Yulita; Irawati

Abstract

Layanan konseling mahasiswa merupakan salah satu layanan non-akademik yang berperan penting dalam mendukung keberhasilan studi dan kesejahteraan mahasiswa. Namun, keterbatasan akses informasi serta belum optimalnya pemanfaatan teknologi dapat menjadi kendala dalam penyelenggaraan layanan konseling. Penelitian ini bertujuan untuk merancang user interface layanan konseling mahasiswa Fakultas Ilmu Komputer Universitas Muslim Indonesia yang berorientasi pada kebutuhan pengguna. Metode yang digunakan dalam penelitian ini adalah Human-Centered Design (HCD), yang meliputi tahapan empathize, define, ideate, prototype, dan evaluate. Pendekatan ini digunakan untuk memahami kebutuhan dan permasalahan pengguna sebagai dasar dalam perancangan antarmuka sistem. Hasil penelitian berupa rancangan user interface layanan konseling mahasiswa yang memiliki alur layanan terstruktur, fungsi yang jelas, serta antarmuka yang mudah dipahami oleh pengguna. Pemodelan sistem dilakukan menggunakan use case diagram, activity diagram, dan sequence diagram untuk menggambarkan interaksi dan alur proses layanan konseling. Rancangan user interface yang dihasilkan diharapkan dapat meningkatkan kemudahan akses, kenyamanan penggunaan, dan efektivitas layanan konseling bagi mahasiswa Fakultas Ilmu Komputer Universitas Muslim Indonesia

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Ashad, Bayu Adrian; Ramdaniah; Mansur; Ilyas, Andi Muhammad; Siswanto, Agus; Jaya, Arif

Abstract

Monitoring kualitas air merupakan salah satu faktor penting dalam keberhasilan budidaya tambak karena berpengaruh terhadap pertumbuhan, kesehatan, dan produktivitas ikan. Namun, sebagian besar masyarakat pembudidaya masih melakukan pemantauan kualitas air secara manual sehingga perubahan kondisi air sering terlambat diketahui. Kegiatan Pengabdian kepada Masyarakat ini bertujuan memberdayakan masyarakat melalui penerapan sistem monitoring kualitas air tambak berbasis Internet of Things (IoT). Kegiatan dilaksanakan di Kabupaten Pangkajene dan Kepulauan (Pangkep), Sulawesi Selatan, menggunakan pendekatan partisipatif yang meliputi penyuluhan, implementasi sistem monitoring berbasis IoT, pelatihan, pendampingan, dan evaluasi. Sistem yang diterapkan memanfaatkan sensor suhu, pH, salinitas, dan dissolved oxygen (DO) yang terhubung dengan mikrokontroler ESP32 untuk mengirimkan data secara real-time ke dashboard berbasis web. Hasil kegiatan menunjukkan adanya peningkatan pemahaman dan keterampilan peserta dalam memanfaatkan teknologi IoT, yang ditunjukkan oleh meningkatnya kemampuan peserta dalam memahami konsep monitoring kualitas air, mengenali fungsi sensor, mengoperasikan sistem monitoring, serta membaca data pada dashboard. Penerapan teknologi IoT memberikan kemudahan bagi masyarakat dalam memantau kondisi kualitas air secara lebih cepat dan efektif sehingga mendukung pengelolaan budidaya tambak yang lebih produktif dan berkelanjutan.

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Masnur, Andi Putri Atirah; Hayati, Lilis Nur; Faradibah, Amaliah, Universitas Musllim Indonesia

Abstract

Art studios play an important role in preserving traditional dance as a valuable cultural heritage; however, many still face operational challenges such as limited promotion, manual administrative processes, and unstructured rental services that reduce management effectiveness and public access. This study aims to design a web-based Management Information System (MIS) for Sanggar Lontara in Bone Regency using the prototyping method to support administrative management and service rental activities in accordance with user needs. The system was developed through an iterative prototyping approach involving requirement analysis, design, implementation, evaluation, and refinement with active participation from studio administrators. System evaluation consisted of alpha testing using black-box testing and beta testing through a Likert-scale questionnaire distributed to 32 respondents across six assessment indicators. The developed system includes service catalogs, online booking, payment proof uploads, administrative verification, reporting, portfolio galleries, studio information, and role-based access for administrators, renters, and managers. Alpha testing demonstrated a 100% success rate for core functionalities, while beta testing achieved a user satisfaction index of 88.02%, classified as “Very Good,” indicating that the system is feasible for implementation with minor usability improvements.

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Astuti, Wistiani; Alwi, Erick Irawadi; Fattah, Farniwati; Hasanuddin, Tasrif; Sari, Ulfa; Almagfirah, Ainur Rahma, Universitas Musllim Indonesia

Abstract

Introduction: Tea leaf diseases can substantially reduce crop quality and productivity, making early and accurate diagnosis important for effective disease management. This study compares ResNet50 and ResNet101 as pretrained deep feature extractors combined with Support Vector Machine (SVM) to determine whether a deeper residual architecture can improve discrimination among visually similar tea leaf disease classes. Method: Images were obtained from the Kaggle “Identifying Disease in Tea Leaves” dataset comprising eight classes. Data augmentation increased each class to 800 images, yielding 6,400 images that were divided into training and testing sets using an 80:20 ratio. ResNet50 and ResNet101 pretrained on ImageNet were used as fixed feature extractors, and the resulting feature vectors were standardized and classified using an RBF-kernel SVM. Results and Discussion: ResNet101–SVM achieved the best performance with 97.97% accuracy and precision, recall, and F1-score of 98%, substantially outperforming ResNet50–SVM, which achieved 89.15% accuracy, 90% precision, 89% recall, and 89% F1-score. The deeper ResNet101 architecture provided more discriminative representations for visually similar disease patterns, although a small number of misclassifications remained. Conclusion: ResNet101 combined with SVM provides a more accurate and reliable framework than ResNet50–SVM for multi-class tea leaf disease classification and offers a promising foundation for automated disease diagnosis systems.

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Tenripada, Andi Ulfah; Syafie, Lukman; Mu'min, Muh; Ataillah, Raqhib; Nawir, Muh, Universitas Musllim Indonesia

Abstract

Introduction: Soybean productivity in Indonesia remains below national demand, while the effectiveness of Rhizobium inoculation depends strongly on dynamic soil conditions such as pH, moisture, temperature, and nitrogen availability. This study develops an Internet of Things (IoT)-based decision support system for adaptive Rhizobium inoculation in highland soybean cultivation. Method: The system integrates a Soil NPK RS485 Modbus sensor, ESP32-WROOM-32D microcontroller, MQTT communication, and the SMARTO web dashboard to monitor four environmental parameters in real time. A Mamdani Fuzzy Inference System was implemented using 17 membership functions and 25 expert-derived IF–THEN rules, with centroid defuzzification producing Rhizobium dose recommendations from 0 to 200 g/ha. Results and Discussion: Field readings of pH 7.5, soil moisture 55%, temperature 26°C, and nitrogen 155 mg/kg generated a recommendation of 16.56 g/ha, classified as Very Low, with a pump volume of 33 mL/ha. Expert validation across 30 simulated highland scenarios produced an overall agreement rate of 83.3%, demonstrating satisfactory consistency between system recommendations and agronomic judgment. Conclusion: The proposed IoT-Fuzzy DSS demonstrates the feasibility of location-specific, real-time, and interpretable Rhizobium inoculation support for highland soybean cultivation, providing a practical foundation for precision biological-input management.

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Wulandari, LMC; Nurhayati, Lilis; Dimas, Ravael; Pijoh, Fidelchristo, Prodi Teknik Industri, Fakultas Teknik, Universitas Katolik Darma Cendika, Indonesia, 61557

Abstract

Sector Logistics plays an important role in maintaining the smoothness and efficiency of national and global supply chains, especially for third-party logistics ( 3PL ) service providers who face demands for reliability and speed of service amidst global competition. The complexity of the logistics process creates the need for a structured and measurable performance evaluation model. This study aims to apply the Supply Chain Operations Reference ( SCOR ) model in identifying and prioritizing key performance indicators ( KPI ) in a 3PL company . The method used involves distributing questionnaires to decision makers in operational and managerial fields. The weights of SCOR performance attributes including reliability, responsiveness, flexibility, cost measures, and asset management efficiency are calculated using the Fuzzy Analytic Hierarchy Process ( FAHP ) method. Furthermore, the Technique for Order Preference by Similarity to Ideal Solution ( TOPSIS ) is used to determine KPI priorities at PT.X. The results of the study show that there are 17 indicators to measure the performance of the freight forwarding sector with the largest weight being operator reliability (0.251) on the reliability attribute, the number of on-time deliveries (0.694) on the responsiveness attribute, load flexibility (0.317) on the flexibility attribute, shipping costs per km (0.379) on the cost attribute and cash-to-cash cycle time (0.479) on the asset management attribute. The SCOR model has proven effective as an initial framework in measuring the performance of 3PL logistics service providers, because it is able to integrate various aspects of performance systematically and quantitatively.

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Nurhayati, Lilis; Andrian,David, Universitas Musllim Indonesia

Abstract

The utilization of log residue waste as an alternative raw material in the wood processing industry has the potential to improve resource efficiency; however, material heterogeneity often leads to product quality issues. This study aims to identify the dominant types of defects and their contributing factors in wooden cable pallets made from log residue waste, as well as to determine quality improvement priorities through the integration of Seven Tools of Quality and Quality Function Deployment (QFD). The study was conducted at UD Nurari Perdana Kabelindo, Sidoarjo, using production and defect data collected during September 2025 through observations, interviews, documentation, and questionnaires. The Seven Tools analysis employed check sheets, Pareto charts, and cause-and-effect diagrams to identify dominant defects and their root causes. The results were subsequently integrated into QFD through the development of a House of Quality to translate customer needs into priority technical characteristics. Customer requirements were determined based on product quality attributes obtained from 30 customer respondents using a census method. The results showed a defect rate of 11.51% of total production, with the dominant defects consisting of thickness deviation, wavy surfaces, knot defects, and asymmetrical circular shapes. The QFD analysis revealed that operator precision and raw material selection were the top priorities for quality improvement. These findings demonstrate that the integration of Seven Tools and QFD is effective in linking process defect analysis with customer needs to support sustainable product quality control.

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Mubarak, Mush’ab Al; Syahar, A. Ulfah Tenripada; Kasim, Fadly, Program StudiTeknik Informatika, FakultasIlmu Komputer, UniversitasMuslim Indonesia, Jl.Urip Sumoharjo Km.05, Kota Makassar, Sulawesi Selatan, Indonesia; Hayati, Lilis Nur; Mansyur, St Hajrah, Program StudiSistem Informasi, FakultasIlmu Komputer, UniversitasMuslim Indonesia, Jl.Urip Sumoharjo Km.05, Kota Makassar, Sulawesi Selatan, Indonesia

Abstract

Vanilla is a high-value plantation commodity whose productivity is significantly affected by plant diseases that are difficult to identify accurately using conventional methods. This study aims to develop a mobile-based vanilla plant disease identification system using a Convolutional Neural Network (CNN) with the ResNet152 architecture. The dataset consists of primary field-acquired images, which were augmented to produce a total of 1,616 images across five disease classes. The model was trained using a transfer learning scheme with parameter adjustments designed to handle variations in field lighting conditions, image angles, and real-world visual characteristics. Experimental results demonstrate that the proposed ResNet152 model achieves high and stable classification accuracy. The integration of the trained model into a mobile application enables fast and practical disease diagnosis in real plantation environments. The novelty of this study lies in the field-oriented optimization of the ResNet152 model and its direct deployment in a mobile diagnostic system tailored for vanilla plant disease identification.

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Arif, Husnul Khotimah; Hayati, Lilis Nur; Sugiarti, Universitas Musllim Indonesia

Abstract

Lack of early treatment leads many patients to seek medical attention only when they have Grade IV Internal Hemorrhoids. Without proper treatment, this condition can lead to serious complications such as severe pain and anemia due to repeated bleeding. Hemorrhoids are a medical condition characterized by swelling of the veins in the anus and lower rectum, which can cause pain, itching, and bleeding during bowel movements. Hemorrhoids include 18 symptoms and are divided into six types: grades I-IV internal hemorrhoids, external hemorrhoids, and thrombosed hemorrhoids. The purpose of this research is to develop an expert system application for diagnosing hemorrhoids to help the public determine the severity of their condition. This system is built using a knowledge base obtained from doctors who have treated hemorrhoids. The Certainty Factor method helps explain the level of confidence in a symptom based on expert knowledge, so users can obtain initial information before seeking further examination by a medical professional. From the trial of 22 samples with a comparison of the system test results with experts, there were 4 that did not match, so the system obtained a percentage of 81,82% system accuracy in diagnosing hemorrhoids, so this application makes it easier for users to diagnose the type of hemorrhoids.

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Salsabilah, Nadya; Irawati; Hayati, Lilis Nur, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

Tomatoes are one of the most widely cultivated and consumed crops, but they are highly susceptible to disease attacks. The main diseases that often attack tomato plants are early blight and late blight. This study compares two machine learning-based classification methods, namely Convolutional Neural Network (CNN) and Naïve Bayes, in detecting tomato leaf diseases. The dataset used consists of 1,255 images obtained from Kaggle, which have been processed and divided into three data ratio scenarios (70:30, 80:20, and 90:10) for training and testing. The results showed that CNN is superior to Naïve Bayes, with the highest accuracy reaching 83.01%, while Naïve Bayes only achieved 34%. With better stability and accuracy, CNN has the potential to help farmers detect diseases more quickly and increase agricultural productivity

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Azis, Huzain; M, Muh Fatwah Fajriansyah; Darwis, Herdianti; Purnawansyah; Hasanuddin, Tasrif; Sugiarti, Faculty of Computer Science, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

Automatic Speech Recognition (ASR) systems perform well for high-resource languages but remain unreliable for regional dialects with limited data. The Makassar dialect, spoken by approximately nine million people in South Sulawesi, Indonesia, presents unique phonetic and grammatical challenges, including high-frequency particles such as mi, ji, and ko. This study introduces the first benchmark evaluation of state-of-the-art ASR models on Makassar speech. Four models, Whisper (tiny, base, small) and Wav2Vec2 Large XLSR Indonesian, were tested on 305 spontaneous utterances (∼10 minutes) from 10 native speakers. Results show severe performance degradation: the best model (Wav2Vec2 Indonesian) reached 87.73% of word error rate (12.27% accuracy). Error analysis reveals two dominant failure modes: Dialect Particle Blindness (average detection rate 2.9%) and Systematic Phonetic Mismatch (89 vowel confusions), indicating that current models treat dialectal features as noise. These findings underscore the urgent need for dialect-aware ASR adaptation and dataset development, providing a foundation for inclusive speech technology across Indonesia's linguistic diversity.

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Darwis, Herdianti; Adnan, Adam; Purnawansyah; Manga, Abdul Rachman, Faculty of Computer Science, Universitas Muslim Indonesia, Makassar, Indonesia; Wibawa, Aji Prasetya; Irawati, Department of Electrical and Informatics Engineering, Universitas Negeri Malang, Malang, Indonesia

Abstract

The proliferation of online learning platforms has necessitated automated systems for monitoring students' emotional states, given that variations in facial expressions significantly influence engagement and learning outcomes. This study proposes a spatio-temporal classification framework for recognizing emotion intensity levels in the DAiSEE dataset, utilizing Vision Transformer as a spatial feature extractor alongside various temporal models, including LSTM, BiLSTM, TimeSformer, and their hybrid variants. Embeddings are extracted via ViT, after which temporal dependencies are captured by each classifier, incorporating feature-level oversampling to mitigate severe class imbalance. Experimental findings reveal that, despite ViT's ability to generate robust spatial representations, all temporal models struggle to identify minority classes, resulting in predictions biased toward the majority class as evidenced by low balanced accuracy scores and overlapping clusters in t-SNE visualizations. Among all configurations, the ViT + LSTM model delivered the most reliable performance, attaining 59 % accuracy and a 0.59 weighted F1-score on engagement labels, while remaining competitive with prior methods. In essence, integrating spatial and temporal features enhances classification efficacy, yet its effectiveness is substantially constrained by imbalanced data distributions. These results offer a thorough examination of representational challenges in imbalanced affective datasets, along with recommendations for mitigation techniques, crossdataset assessments, and multimodal integrations.

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Manga, Abdul Rachman; Nirmala; Azis, Huzain; Fattah, Farniwati; Salim, Yulita; Darwis, Herdianti, Departement of Computer Science, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

Flower image classification poses a challenge in digital image processing, requiring effective methods for feature extraction and classification. The aim of this research is to improve the accuracy of flower image classification by employing ResNet-50 for feature extraction, with and without segmentation, and evaluating the effectiveness of various classification algorithms. The dataset consists of images of Calendula and Coreopsis flowers, totaling 2,025 samples. Four segmentation techniques-Canny, Thresholding, Otsu, and Mean Shift-along with a non-segmentation approach are applied. The features extracted using ResNet-50 are classified with Naive Bayes, Support Vector Machine (SVM), Decision Tree, and K-Neighbors Classifier (KNN). Performance evaluation is conducted using accuracy, precision, recall, and F1-score with 5-fold cross-validation. The results show that SVM delivers the best performance in most scenarios. The highest accuracy in segmentation scenarios was achieved with the Mean Shift technique at 0.91, while the non-segmentation approach yielded the highest accuracy of 0.95. The non-segmentation approach proves more effective, indicating segmentation is not always required for high classification accuracy with ResNet-50. This study shows that ResNet-50, especially without segmentation, can significantly improve flower image classification compared to traditional methods, opening opportunities for more efficient systems.

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Salim, Yulita; Rakasyah, Athar Fathana; Darwis, Herdianti; Herlinda; Irawati; Manga, Abdul Rachman, Departement of Computer Science, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

In the era of burgeoning image data, the demand for robust image classification algorithms has never been more pressing. This research article delves into the realm of image classification, encompassing a comprehensive analysis of diverse datasets and machine learning algorithms, While simultaneously studying the efficiency of ensemble approaches in improving classification performance. The study employs five distinct datasets, spanning medical images, everyday objects, and natural scenery, ensuring a broad spectrum of classification challenges. These datasets include the Skin Cancer ISIC dataset, CIFAR-10, Flowers, Apparel Image, and Brain Tumor dataset. The image classes within these data sets vary significantly, presenting an ideal testbed for assessing algorithmic versatility. Our investigation scrutinizes five machine learning algorithms: Support Vector Classifier (SVC), Random Forest Classifier (RFC), Gradient Boosting Classifier (GBC), K-Nearest Neighbors (KNN), and Gaussian Naive Bayes (GNB). Each algorithm is evaluated individually, followed by a comprehensive ensemble approach involving a Voting Classifier. The results unveil nuanced performance variations across diverse datasets. Notably, RFC and GBC exhibit remarkable accuracy in brain tumor image classification, while KNN demonstrates strengths in classifying apparel images. Ensemble techniques, embodied by the Voting Classifier, harmonize these algorithms, yielding competitive and balanced performance across the datasets. This article contributes valuable insights into the realm of image classification, shedding light on algorithmic strengths and limitations, the efficacy of ensemble techniques, and their applicability to diverse image datasets. These findings hold significance for fields ranging from medical diagnostics to everyday object recognition, paving the way for more precise and versatile image classification solution

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