Amaliah Faradibah

Authors

Widyawati, Dewi; Faradibah, Amaliah; Belluano, Poetri Lestari Lokapitaasari, Universitas Muslim Indonesia

Abstract

This research aims to analyze the performance of three classification models, namely Decision Tree Classifier, Support Vector Machine, and Naive Bayes Classifier, in predicting lung cancer using the "Lung Cancer Prediction" dataset. The performance evaluation metrics used include accuracy, precision weighted, recall weighted, and F1 weighted. As a preliminary step, exploratory data analysis (EDA) and dataset preprocessing, including feature selection, data cleaning, and data transformation, were conducted. The test data results showed that the Decision Tree Classifier and Naive Bayes Classifier had similar performances with high accuracy, precision, recall, and F1 values. Meanwhile, the Support Vector Machine also exhibited competitive performance, although its precision weighted value was slightly lower. Additionally, an outlier analysis was conducted using box plots, revealing that the Decision Tree Classifier had 2 outlier values, while the Support Vector Machine had 4 outlier values, and Naive Bayes had no outlier values. In conclusion, all three classification models demonstrated good potential in lung cancer prediction. However, selecting the best model requires consideration of relevant evaluation metrics for the application and accommodating the limitations of each model. Further evaluation and in-depth analysis are needed to ensure the reliability of the models in predicting lung cancer cases more accurately and consistently.

Related SDGs

Authors

Widyawati, Dewi; Faradibah, Amaliah; Belluano, Poetri Lestari Lokapitaasari, Universitas Muslim Indonesia

Abstract

This research aims to analyze the performance of three classification models, namely Decision Tree Classifier, Support Vector Machine, and Naive Bayes Classifier, in predicting lung cancer using the "Lung Cancer Prediction" dataset. The performance evaluation metrics used include accuracy, precision weighted, recall weighted, and F1 weighted. As a preliminary step, exploratory data analysis (EDA) and dataset preprocessing, including feature selection, data cleaning, and data transformation, were conducted. The test data results showed that the Decision Tree Classifier and Naive Bayes Classifier had similar performances with high accuracy, precision, recall, and F1 values. Meanwhile, the Support Vector Machine also exhibited competitive performance, although its precision weighted value was slightly lower. Additionally, an outlier analysis was conducted using box plots, revealing that the Decision Tree Classifier had 2 outlier values, while the Support Vector Machine had 4 outlier values, and Naive Bayes had no outlier values. In conclusion, all three classification models demonstrated good potential in lung cancer prediction. However, selecting the best model requires consideration of relevant evaluation metrics for the application and accommodating the limitations of each model. Further evaluation and in-depth analysis are needed to ensure the reliability of the models in predicting lung cancer cases more accurately and consistently

Related SDGs

Authors

Jabir, Sitti Rahmah; Tenripada, A Ulfah; Asis, Muhammad Arfah; Widyawati, Dewi; Faradibah, Amaliah; Universitas Muslim Indonesia, Indonesia

Abstract

Autism Spectrum Disorder (ASD) adalah sekelompok kondisi perkembangan saraf. Orang dengan autisme memiliki masalah dengan interaksi sosial. Mereka tidak dapat mengembangkan hubungan dengan orang lain sesuai dengan tingkat perkembangan mereka. Jumlah anak-anak dengan autisme telah tumbuh terus menerus selama beberapa tahun. Mendiagnosis ASD diperlukan pendekatan yang komprehensif, sistematis, dan terstruktur. Untuk mendiagnosis ASD, peneliti memanfaatkan penambangan data untuk menganalisis data terapi perilaku. Data yang didapatkan tidak sepenuhnya data yang bersih, dimana terdapat beberapa data yang hilang. Untuk menangani data yang hilang, pendekatan data pre-processing yang akan digunakan untuk membantu menganalisis dan memperhitungkan nilai yang hilang. Data yang tidak sesuai format akan ditransformasikan terlebih dahulu sebelum divisualisasikan. Sebagian besar kuesioner telah diisi oleh orang tua. Berdasarkan dataset, anak-anak dengan ASD didominasi oleh laki-laki. Dirujuk dari etnis, orang kulit putih-Eropa adalah etnis terbanyak yang terdeteksi memiliki jumlah anak tertinggi dengan ASD. Di dalam etnis, ada berbagai negara. Inggris adalah jumlah terbesar orang yang menderita autisme. Berdasarkan hasil tersebut, bidang kesehatan harus lebih fokus memberikan pengobatan untuk orang kulit putih-Eropa terutama di Inggris. Para peneliti kesehatan harus menghasilkan wawasan yang dapat mengembangkan autisme untuk deteksi dan skrining. Berdasarkan hasil, hal tersebut dapat membantu lebih lanjut yang dapat mengurangi persentase autisme di seluruh dunia. peneliti kesehatan harus menghasilkan wawasan yang dapat mengembangkan autisme untuk deteksi dan skrining.

Related SDGs

Authors

Manga, Abdul Rachman; Utami, Aulia Putri; Azis, Huzain; Salim, Yulita; Faradibah, Aulia, Department of Computer Engineering, Faculty of Computer Science, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

The surge in machine learning (ML) and artificial intelligence has revolutionized medical diagnosis, utilizing data from chest ct-scans, COVID-19, lung cancer, brain tumor, and alzheimer parkinson diseases. However, the intricate nature of medical data necessitates robust classification models. This study compares support vector machine (SVM), naïve Bayes, k-nearest neighbors (K-NN), artificial neural networks (ANN), and stochastic gradient descent on multi-class medical datasets, employing data collection, Canny image segmentation, hu moment feature extraction, and oversampling/under-sampling for data balancing. Classification algorithms are assessed via 5-fold cross-validation for accuracy, precision, recall, and F-measure. Results indicate variable model performance depending on datasets and sampling strategies. SVM, K-NN, ANN, and SGD demonstrate superior performance on specific datasets, achieving accuracies between 0.49 to 0.57. Conversely, naïve Bayes exhibits limitations, achieving precision levels of 0.46 to 0.47 on certain datasets. The efficacy of oversampling and under-sampling techniques in improving classification accuracy varies inconsistently. These findings aid medical practitioners and researchers in selecting suitable models for diagnostic applications.

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Authors

Syahar, A. Ulfah Tenripada; Faradibah, Amaliah, Universitas Muslim Indonesia

Abstract

Laboratorium merupakan tempat di mana berbagai kegiatan akademik, seperti praktikum dan riset akan dilakukan. Laboratorium harus kita Kelola dengan baik karena sangat penting dan berpengaruh untuk keberhasilan aktivitas akademik pada program studi dan fakultas. Tujuan dari penelitian ini adalah bagaimana kita melakukan analisis dan membuat sebuah perancangabn sistem informasi manajemen laboratorium terpadu pada program studi Teknik Informatika Universitas Muslim Indonesia, dengan focus pada kegiatan praktikum. Penelitian dilaukan dalam beberapa tahap dengan menggunakan metode pengembangan berbasi obyek seperti pengumpulan data, analisis kebutuhan, dan perancangan sistem. Sistem ini dirancang dan dibuat dengan menggunakan metode waterfall dan hasil dari penelitian ini akan digubakan sebagai refrensi untuk dilakukan penegmbengan sistem selanjutnya

Related SDGs

Authors

Herman; Darwis, Herdianti; Nurfauziyah; Puspitasari, Rahma; Widyawati, Dewi; Faradibah, Amaliah, Faculty of Computer Science, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

This study aims to classify anxiety disorders in adolescents using three machine learning algorithms, namely naive bayes, C4.5, and K-Nearest Neighbor (K-NN). The data used was taken from the DASS-21 questionnaire, which contains 418 samples with 11 attributes, including age, gender, and seven anxiety-related questions. The algorithm was tested using the holdout technique with 80:20, 70:30, and 60:40 data splits, as well as the k-fold cross-validation technique. The results showed that the C4.5 algorithm performed best with 100% accuracy in the holdout technique, followed by naive bayes with 99% accuracy, and K-NN with 94% accuracy. In cross-validation testing, C4.5 also showed the highest accuracy of 98%, while naive bayes and K-NN achieved 89% and 92% respectively. This study concludes that the C4.5 algorithm is superior in classifying anxiety compared to naive bayes and K-NN, so it can be relied upon for machine learning-based diagnostic applications in supporting the detection of anxiety disorders efficiently.

Related SDGs

Authors

Jabir, Sitti Rahmah; Purnawansyah; Darwis Herdianti; Lahuddin Harlinda; Faradibah, Amaliah; Gaffar, Andi Widya Mufila, Faculty of Computer Science, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

Nowadays, Indonesia's tourism sector faced challenges in light of the global recession threat. These challenges encompassed high airline ticket prices and inflation, which in turn influenced consumer spending patterns. To tackle these difficulties, the Ministry of Tourism had taken steps to allow foreign investments in the potential tourism object to invest. The involvement of foreign investors had contributed to substantial growth and advancement within Indonesia's tourism industry, thereby presenting numerous opportunities for prospective investors. Indonesia has set a target of attracting more than 7 million foreign tourists by the year 2023, which has increased double from previous year. Based on the literature, the researcher's objective is to analyze the potential of public tourism sites, categorizing them as viable prospects for potential investors. The data had been obtained from Kaggle which the target variable was the rating from 1 to 5. The initial classification attempt, which utilized these five categories, proved unsatisfactory, prompting the application of unsupervised learning techniques to reduce the number of target variable categories. Through the utilization of k-means clustering, the final classification resulted in two overarching categories: “good” and “bad” ratings. Subsequent analysis revealed that Naïve Bayes emerged as the most effective algorithm for this classification task, albeit with no significant difference in results when compared to support vector machines. In conclusion, future research endeavors might consider exploring alternative unsupervised learning methods or conducting more comprehensive feature selection processes before implementing the classification.

Related SDGs

Authors

Faradibah, Amaliah; Tenripada, A. Ulfa; Belluano, Poetri Lestari Lokapitasari; Fahmi; Utami, Aulia Putri; Rahmadani, Rizal, Teknik Informatika, Universitas Muslim Indonesia

Abstract

Peningkatan alat transportasi tanpa seimbangnya sarana dan prasarana menuntut inovasi teknologi, meski teknologi sudah diterapkan, namun belum mampu mengatasi dampak perkembangan ekonomi yang meningkatkan jumlah kendaraan. Pengaturan optimal dapat dilakukan melalui sistem berbasis Internet of Things untuk mengurangi kemacetan dan menghemat energi. Manajemen yang baik diperlukan untuk menyeimbangkan semua sarana dan prasarana. Melalui pemantauan kendaraan, dapat ditentukan komposisi kendaraan di jalur yang sama untuk dialihkan atau mengatur waktu traffic light. Teknologi kendaraan terhubung dianggap kunci solusi baru untuk masalah transportasi, memungkinkan komunikasi antar kendaraan dan infrastruktur. Connected vehicle technology memungkinkan kendaraan berkomunikasi menggunakan jaringan nirkabel. Penelitian ini melibatkan pengumpulan data dan model matematika untuk memprediksi arus lalu lintas. Hasil perubahan kondisi eksisting setelah menerapkan skenario penggunaan teknologi Connected Vehicle menunjukkan pengurangan Travel Time dengan hambatan dari sekitar 0.21 jam pada tahun 2018 menjadi perkiraan 0.38 jam pada tahun 2030, memberikan implikasi positif terhadap pengelolaan lalu lintas di masa mendatang. Temuan ini memberikan dasar kuat untuk perbaikan kebijakan dan infrastruktur transportasi yang dapat meningkatkan efisiensi dan kenyamanan perjalanan.

Related SDGs

Authors

Belluano, Poetri Lestari Lokapitasari; Purnawansyah; Faradibah, Amaliah, Universitas Muslim Indonesia

Abstract

Sistem Informasi Akademik (xSIA) adalah aplikasi yang dibangun untuk mengelola modul transaksi nilai akademik yang memberikan kemudahan kepada pengguna mengelola nilai dalam kegiatan administrasi akademik secara online. Kebutuhan rekonstruksi arsitektur microservice xSIA dari model domain driven design yang dibangun sebelumnya menggunakan format data json (javascript object notation), protokol komunikasi REST (Representational State Transfer and an architectural style for distributed hypermedia systems), terjadi proses otorisasi dan otentikasi yang ada di setiap microservice, terdapat penyatuan data yang dibebankan kepada client telah menyebabkan client harus melakukan banyak request ke berbagai microservice yang tersedia, serta pembuatan dokumentasi jika ada penambahan microservice. Rekonstruksi sistem xSIA dikembangkan dengan mengubah arsitektur microservice xSIA sehingga konsep responsibility autorisasi dan autentifikasi dapat dilakukan sesuai dengan kebutuhan service. Pendekatan dalam melakukan rekontruksi arsitektur microservice pada aplikasi xSIA menggunakan konsep baru dengan model single gateway microservice (layanan satu gerbang) dan dibangun menggunakan GraphQL Federation untuk mempermudah komunikasi data antara backend dan frontend dari aplikasi, serta dapat diimplementasikan di berbagai Bahasa pemrograman sehingga meminimaliasir terjadinya downtime saat proses modifikasi terjadi. Hasil penelitian ini berupa aplikasi xSIA pada modul transaksi rencana studi (KRS) menggunakan GraphQL Federation Concept dengan model single gateway microservice sehingga responsibility autorisasi dan autentifikasi dapat dilakukan sesuai dengan kebutuhan service dengan rerata realtime 373.15 millisecond.

Related SDGs

Authors

Widyawati, Dewi; Tenripada, A. Ulfah; Jabir, Sitti Rahmah; Faradibah, Amaliah, Universitas Muslim Indonesia

Abstract

Pengelolaan buku administrasi desa memegang peranan penting bagi jalannya suatu organisasi, yaitu sebagai sumber informasi dan sebagai pusat ingatan organisasi sebagai dasar pengambilan keputusan. Masalah yang muncul pada Lembang Marinding Kecamatan Mengkendek Kab. Tana Toraja yaitu masih mengelola data administasi desa secara manual, sehingga muncul masalah jika berkas administrasi dibutuhkan tidak ditemukan atau hilang. Selain itu belum tersedianya fasilitas atau media yang mengatur data administrasi secara digital, sehingga pelayanan kepada masyarakat belum maksimal. Staf lembang juga belum mahir dalam menerapkan teknologi informasi dalam manajemen administrasi. Hasil yang diperoleh dari kegiatan pengabdian yaitu dapat meningkatkan pemahaman dan kemampuan serta keterampilan perangkat desa Lembang Marinding dalam mengelola administrasi persuratan secara digital sehingga kegiatan administrasi menjadi lebih baik. Selain itu pelayanan kepada masyarakat lebih baik sehingga membuat kinerja staf Lembang Marinding juga meningkat. Sebagai penunjang dalam kegiatan tersebut, staf yang mengikuti kegiatan pelatihan diberikan modul sebagai panduan penggunaan sistem. Pada kegiatan ini menghasilkan sebuah sistem informasi berupa buku administrasi berbasis web yang akan dikelola langsung oleh staf lembang. Luaran akhir berupa publikasi di media massa dan artikel yang akan dipublikasikan pada Jurnal Ilkomas

Related SDGs

Authors

Faradibah, Amaliah; Widyawati, Dewi; Belluano, Poetri Lestari Lokapitasari; Tenripada, A. Ulfah; Utami, Aulia Putri; Fatahillah, Furqon; Tuasamu, Fatimah A.R.

Abstract

The use of Information Technology has penetrated various sectors, including education. In the digital era, video editing applications are among the most widely used tools, as students tend to be more engaged with video-based content than with text-based learning materials. This study aims to improve teaching methods by empowering teachers to create engaging learning videos using digital applications. The program was implemented at SDN 133 Inpres Paririsi Takalar, involving 27 elementary school teachers with diverse educational backgrounds, teaching subjects, and teaching experience. A Practical and Participatory training model was applied over a two-month period. Evaluations were conducted before and after the training to assess its effectiveness in enhancing teachers’ video editing skills. The results showed a significant improvement in teachers' abilities to use video editing applications and increased confidence in delivering digital-based learning content. The main challenges faced by teachers included limited initial digital literacy and time constraints during implementation. This study contributes to addressing the digital content creation skill gap among elementary school teachers. The expected outputs include: (1) a user guide module for video editing applications, (2) publication in an accredited national journal, and (3) enrichment materials for the Multimedia Systems course. Further research is recommended to explore the long-term impact of the training and its applicability to other educational levels.

Related SDGs

Authors

Manga, Abdul Rachman; Atmajaya, Dedy; Faradibah, Amaliah, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

Agriculture utilizes biological resources to produce food, industrial raw materials, energy sources, and manage the environment. This sector plays a strategic role in national economic development. This research aims to design an automatic spraying system for shallot plants based on soil moisture using soil moisture sensors. This system utilizes soil moisture sensors to detect the water content in the soil as well as soil moisture sensors to measure the air humidity around the plants. Data from both sensors are processed by the microcontroller to regulate the timing and duration of the spraying. The prototype of this system was built using soil moisture sensors, soil moisture sensors, microcontrollers, water pumps, solenoid valves, and other supporting components. Testing was conducted in the field with red onion plants as the test subjects. The results show that the system is capable of functioning effectively in watering plants based on soil moisture levels. The sensor works accurately in measuring water content, while the microcontroller successfully controls the spraying optimally. The implementation of this system has proven to increase water usage efficiency and support better growth of red onion plants. Thus, this automatic spraying system offers an environmentally friendly and efficient solution for irrigation based on soil moisture and soil moisture sensors.

Related SDGs

Authors

Rifaat, Muhammad Taufik; Faradibah, Amaliah, Program Studi Teknik Informatika, Fakultas Ilmu Komputer, Universitas Muslim Indonesia, Jl.Urip Sumoharjo km.05, Makassar dan 90231, Indonesia; Fauzi, Achmad Nuri, Program Studi Teknik Elektro, Fakultas Teknik, Universitas Negeri Malang, Malang dan 65145, Indonesia

Abstract

This study aims to analyze and identify vulnerabilities to Distributed Denial of Service (DDoS) and phishing attacks on FIKOM UMI’s Wi-Fi with the access point SSID “UMI Connect,” and to provide recommendations to FIKOM UMI. The method employed is Vulnerability Assessment using the Fluxion tool and a TP-Link wireless adapter. The findings reveal two types of vulnerabilities—packet injection and wireless hijacking—each with medium risk and medium confidence. These weaknesses reside at the SSID layer. Attackers can exploit them to disconnect clients from the network and subsequently perform phishing to obtain the access-point password of the targeted SSID. While some security components on the target access point are functioning properly, several areas still require improvement—specifically, unlimited packet rates per second passing through TCP/UDP data transmissions between users and the access point, which should be rate-limited.

Related SDGs

Authors

As’ad; Ihwana, Faradibah; Amaliah

Abstract

Penelitian ini mengkaji penerapan algoritma Naïve Bayes dalam mengklasifikasikan beban kerja pegawai di Badan Kepegawaian Kabupaten Barru guna meningkatkan efektivitas manajemen sumber daya manusia berbasis data. Tantangan utama dalam pengelolaan pegawai meliputi ketidakseimbangan distribusi tugas, kurangnya transparansi penilaian, serta keterbatasan metode konvensional, yang dapat diatasi melalui analisis data historis dengan pendekatan probabilistik. Algoritma Naïve Bayes digunakan untuk mengelompokkan beban kerja pegawai berdasarkan parameter jumlah jam kerja, kompleksitas tugas, dan pencapaian target, yang menghasilkan distribusi beban kerja rendah (55,7%), sedang (31,4%), dan tinggi (12,8%). Model ini menunjukkan akurasi tinggi dalam klasifikasi dan mampu mengidentifikasi pegawai dengan kelebihan atau kekurangan beban kerja secara objektif. Studi kasus pada PT. Rikasa Dinar Djaya juga membuktikan keandalan metode ini dalam mengevaluasi kinerja karyawan dengan akurasi 99,44%. Hasil penelitian ini diharapkan dapat meningkatkan transparansi dan keadilan dalam evaluasi kinerja pegawai, meminimalkan bias subjektif, serta menjadi referensi bagi instansi pemerintah lainnya dalam mengadopsi teknologi machine learning untuk pengelolaan SDM yang lebih modern, efisien, dan berkelanjutan

Related SDGs