Research Publications

Authors

Fiqram, Muhammad; Fattah, Farniwati; Manga, Abdul Rachman, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

Kemajuan dalam dunia e-sport, khususnya melalui platform game Playerunknown's Battlegrounds Mobile (PUBG Mobile), telah merubah pandangan masyarakat terhadap bermain game. Aktivitas yang dahulu sering dianggap hanya sebagai hiburan belaka dan dipandang sebelah mata, kini diakui sebagai kegiatan yang mampu memberikan manfaat positif, terutama dalam mengembangkan kemampuan soft skill melalui kompetisi. Namun, di balik perkembangan ini, masih terdapat beberapa masalah yang belum terselesaikan, salah satunya merupakan kurangnya dukungan infrastruktur jaringan yang memadai. Oleh karena itu, tujuan utama dari penelitian ini merupakan merancang sebuah prototype jaringan yang dapat digunakan untuk turnamen e-sport PUBG Mobile. Perancangan ini menggunakan metode studi literatur dan pengujian melalui simulasi nyata. Penggunaan metode dalam perancangan jaringan turnamen mampu menghasilkan sebuah rancangan prototype jaringan yang dapat memenuhi seluruh kebutuhan teknis untuk kegiatan tournament, sehingga dapat mendukung kelancaran dan kualitas kompetisi e-sport secara optimal. Penelitian ini diharapkan dapat memberikan solusi praktis dan inovatif untuk mendukung perkembangan e-sport yang semakin pesat.

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Authors

Siyu, Maruly Widjaya; Fattah, Farniwati; Gaffar, Andi Widya Mufila, Faculty of Computer Science, Universitas Muslim Indonesia, Jl. Urip Sumoharjo No.km.5, Panaikang, Panakkukang,Makassar, 90231, Indonesia

Abstract

Research on Wireless Sensor Network (WSN) began to be carried out to meet various industrial needs including defense, health, environmental surveillance, and others. However, there are several obstacles in WSN, namely the problem of energy consumption which is the object of research by many researchers. The solution offered in this paper is to use the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol which is a hierarchical protocol where this protocol focuses on saving energy use on WSN. This study used Matlab 2023 simulation software which used several measurement parameters to determine tissue life time, average residual energy, and throughput. The research scenario uses a homogeneous topology with three network sizes, namely 500 x 500, 750 x 750, and 1000 x 1000. Then also used three conditions for the number of sensor nodes, namely 100 nodes, 150 nodes, and 200 nodes. The results showed that the smaller the tissue size, the longer the life time and if the network size is wider, the network life time is shorter. The number of data packets transmitted depends on the number of active sensor nodes and sufficient energy to transmit.

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Authors

Windi, Nadia Astia; Fattah, Farniwati; Gaffar, Andi Widya Mufila, Program Studi Teknik Informatika, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

Dalam era digital saat ini, jaringan komputer menjadi infrastruktur kritis bagi berbagai lembaga pendidikan, termasuk Universitas Muslim Indonesia (UMI) dengan Fakultas Kedokteran sebagai fokus. Ruangan Computer-Based Testing (CBT) di Fakultas Kedokteran UMI memiliki peran dalam penyelenggaraan ujian komputer dengan 188 unit komputer untuk mahasiswa, server, switch dan mikrotik. Permasalahan yang terjadi yaitu gangguan jaringan akibat penggunaan komputer secara bersamaan dan spesifikasi perangkat komputer yang digunakan rendah. Analisis Quality of Service (QoS) menjadi kunci untuk meningkatkan kualitas jaringan LAN di ruangan CBT FK UMI. Parameter QoS, seperti throughput, packet loss, delay dan jitter, menjadi fokus untuk mengidentifikasi dan mengatasi masalah yang dapat meningkatkan efisiensi dan kepuasan pengguna selama ujian komputer. Pada hari Kamis, 13 Juni 2023 nilai Qos sebesar 75% dengan kategori “Bagus” sedangkan pada hari Rabu, 9 Agustus 2023 nilai Qos 70% dengan kategori “Kurang Memuaskan”. Hasil analisis QoS dapat disimpulkan bahwa jaringan LAN pada Fakultas Kedokteran Universitas Muslim Indonesia (UMI) bernilai sebesar 72,5% termasuk dalam kategori “Kurang Memuaskan” berdasarkan standarisasi TIPHON.

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Authors

Trinanda, Muhammad Satria Putra; Irawati; Hasnawi, Mardiyyah, Program Studi Teknik Informatika, Fakultas Ilmu Komputer, Universitas Muslim Indonesia Jl Urip Sumoharjo, Panaikang. Panakkukang, Kota Makassar

Abstract

Informasi yang diperoleh masyarakat belum memadai, dan terkadang masih sangat membutuhkaninformasi yang lengkap salah satunya tentang penyakit. Penyediaan media informasi harus terbukti akurat dan tidak boleh diberitakan oleh organisasi yang tidak memiliki otoritas. Dalam hal ini, yayasan kesehatan memiliki hak untuk memberikan informasi yang dipercaya keasliannya, terutama dalam memberikan data informasi mengenai penyakit tuberkulosis. Tujuan dari penelitian ini adalah mengembangkan sistem informasi tuberkulosis pada platform Yamali TB dengan pendekatan Goal-Oriented Requirement Engineering (GORE) dan metode Scrum. Pengembangan sistem informasi dilakukan melalui beberapa aktivitas utama yaitu Identifikasi Masalah, Analisis Masalah, Identifikasi Tujuan, Prioritas Backlog, Perancangan Desain Sistem, Diskusi Awal, Perancangan Program dan Evaluasi Akhir dengan menggunakan metode penilaian System Usability Scale. Hasil penelitian menunjukkan bahwa tingkat kepuasan pengguna terhadap sistem informasi berdasarkan uji usability+C67+C63

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Authors

Asfar, A Muh Fitrah; Hasnawi, Mardiyah; Darwis, Herdianti, Informatics engineering, Computer science, Universitas Muslim Indonesia, Indonesia

Abstract

One of the most common causes of death worldwide is skin cancer and its incidence is increasing. To achieve optimal treatment and improve clinical outcomes for patients, precision skin cancer detection and classification approaches are required, which can be achieved through the application of feature extraction and machine learning algorithms. The development of such algorithms to identify important patterns from skin cancer image datasets enables early detection and more accurate classification and more effective treatment. Although previous studies have tried to detect skin cancer using feature extraction techniques such as HFF, HOG, and GLCM, some weaknesses still need to be improved. This research aims to combine various feature extraction methods such as Gray Level Co-occurrence Matrix, Histogram Oriented Gradients, and Local Binary Patterns and machine learning algorithms such as Support Vector Machine, Random Forest, and Gaussian Naïve Bayes in the classification process between Melanoma and Nevus skin cancers. In this research, the number of datasets used is 17,397 derived from the ISIC Dataset. The results showed that the Histogram Oriented Gradients method with Support Vector Machine algorithm achieved the highest accuracy of 92%. The combination of Gray Level Cooccurrence Matrix and Local Binary Patterns with Random Forest algorithm also achieved an accuracy of 92%, the combination of Gray Level Co-occurrence Matrix, Histogram Oriented Gradients, and Local Binary Patterns with Random Forest algorithm also resulted in an accuracy of 92%. These findings suggest that the combination of various feature extraction methods and machine learning algorithms can improve accuracy in skin cancer classification, which in turn can contribute to early detection and more effective treatment.

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Authors

Hayati, Lilis Nur, Program Studi Sistem Informasi, Universitas Muslim Indonesia, Makassar, Indonesia; Manga, Abdul Rachman; Munawir, Ali, Program Studi Teknik Informatika, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

Shalat adalah momen khusyuk yang menghubungkan diri dengan Tuhan, menjadi bentuk komunikasi hamba kepada pencipta, dengan tujuan mendekatkan diri. Dalam menjalankan shalat, bagi Muslim, penting mengikuti gerakan sesuai sunnah Nabi dan tata cara empat mazhab Islam: Hanafi, Maliki, Syafi'i, dan Hambali. Meski gerakan mazhab berbeda, perbedaan ini dapat dipelajari sesuai dengan mazhab yang dianut, tanpa menjadi perdebatan pada masyarakat mengenai perbedaan tata cara gerakan shalat. Teknologi Augmented Reality (AR) 3D dengan metode Marker-based Tracking menjadi alternatif menarik untuk memudahkan pemahaman tata cara gerakan shalat sesuai mazhab. AR adalah salah satu teknologi yang dapat menciptakan aplikas, maka digunakanlah teknologi ini pada proses pengenalannya. Marker-based tracking memanfaatkan penanda dengan pola titik-titik untuk mendeteksi dan proyeksikan objek 3D ke AR. Tujuan penelitian ini adalah membantu umat Muslim, terutama yang baru belajar shalat atau ingin memahami gerakan berbagai mazhab, untuk mengikuti gerakan yang tepat sesuai mazhab yang dianut. Berdasarkan perancangan, implementasi dan pembahasan yang telah dilakukan dalam penelitian ini serta beberapa koresponden yang telah menguji aplikasi ini maka dapat diambil kesimpulan bahwa aplikasi ini memberikan beberapa fitur yang dapat membantu dalam proses pembelajaran khususnya dalam pembelajaran tata cara gerakan shalat.

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Authors

Ramadhani, Tri Anita Resky; Manga, Abdul Rachman; Punawansyah, Universitas Muslim Indonesia, Jl. Urip Sumohardjo KM.05, Makassar, Indonesia

Abstract

Penelitian ini bertujuan untuk membandingkan performa dua model Convolutional Neural Networks (CNN), yaitu VGG-16 dan ResNet50, dalam tugas klasifikasi multilabel gambar kerbau. Dataset yang digunakan terdiri dari 2009 gambar kerbau Toraja yang dilabeli dengan lima kategori: manusia, motor, truk, hewan liar, dan kerbau. Model-model CNN dilatih menggunakan 30 epoch dan dievaluasi dengan menggunakan metrik loss, akurasi, presisi, recall, dan f1-score.Hasil eksperimen menunjukkan bahwa VGG-16 secara konsisten mengungguli ResNet50 dengan mencapai akurasi tertinggi 0.95 pada set pelatihan dan 0.94 pada set validasi, serta f1- score 0.94 pada set pelatihan dan 0.92 pada set validasi. Temuan ini mengindikasikan bahwa arsitektur CNN yang lebih dalam dan terstruktur, seperti VGG-16, memberikan hasil yang lebih baik dalam mengklasifikasikan gambar-gambar kerbau dengan variasi label yang kompleks.

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Authors

Triyadi, Azwan; Purnawansyah; Darwis, Herdianti, Universitas Muslim Indonesia

Abstract

Elon Musk owns the business Neuralink, which attempts to build brain-machine interfaces. This study categorizes public opinion towards the use of Neuralink goods, including whether people agree (positive), disagree (negative), or feel neither way. Without accessing the Twitter API, the Twint Python Libraries were utilised to retrieve a dataset of 3000 using the keyword “neuralink”. What datasets are included in positive, neutral, or negative categories are designated using RoBERTa. Term Frequency Inverse Document Frequency (TF-IDF) is utilized for feature extraction, while Synthetic Minority Over-sampling Technique (SMOTE) is employed to handle class imbalance. Complement Naive Bayes, achieved accuracy of 81%, followed by Multinomial Naive Bayes, which achieved accuracy of 80%, and Gaussian Naive Bayes, which achieved accuracy of 75%. The model Complement Naïve Bayes was used in this study to attain the maximum accuracy, and accuracy increases when employing SMOTE compared to other Naïve bayes variants.

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Authors

Saly, Intan Novita; Purnawansyah, Universitas Muslim Indonesia, Sulawesi Selatan, Makassar, 90231, Indonesia; Azis, Huzain, Universitas Muslim Indonesia, Sulawesi Selatan, Makassar, 90231, Indonesia, Universiti Kuala Lumpur, Kuala Lumpur, Malaysia, 50250, Malaysia

Abstract

This study aims to evaluate the performance of various classification methods in recognizing handwritten Arabic numerals, particularly the K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), and NU Support Vector Classifier (NU SVC) algorithms. In this study, a dataset of handwritten Arabic numerals consisting of 9,350 samples with 10 different classes was used. The research process involved data collection, data labeling, dividing the dataset into training and testing data, implementing classification algorithms, and performance testing using cross-validation methods. The results showed that NU SVC had more stable performance with accuracy close to KNN, while GNB showed the lowest performance. The conclusion of this study emphasizes that the selection of algorithms and parameter optimization is crucial to improve the accuracy and efficiency of handwriting recognition systems. Support Vector Machine (SVM) based algorithms proved to be superior in handling complex classification tasks compared to GNB. This study provides significant contributions to the field of handwriting recognition, particularly in the context of Arabic numeral handwriting, and can serve as a reference for developers of optical character recognition (OCR) systems in the future. Future research is recommended to increase the variety of datasets and further explore parameter optimization and data preprocessing techniques to improve system accuracy.

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Authors

Bustam, Faida Daeng; Purnawansyah, Universitas Muslim Indonesia, Sulawesi Selatan, Makassar, 90231, Indonesia; Azis, Huzain, Universitas Muslim Indonesia, Sulawesi Selatan, Makassar, 90231, Indonesia, Universiti Kuala Lumpur, Kuala Lumpur, Malaysia, 50250, Malaysia

Abstract

This research explores the performance of several classification algorithms on handwritten Lontara digits, a script traditionally used by the Bugis and Makassar communities in South Sulawesi, Indonesia. The dataset comprises 10,890-digit samples, contributed by 99 individuals, and is categorized into 10 distinct classes corresponding to the digits 0- 9. The classification methods evaluated in this study include K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), and Nu-Support Vector Classifier (NuSVC). Cross-validation techniques are employed to evaluate the performance of these classifiers using standard metrics such as accuracy, precision, recall, and F1 score. The findings demonstrate varying levels of performance across the algorithms. Notably, GNB achieves the highest recall, indicating its ability to correctly identify positive samples, whereas KNN and NuSVC exhibit moderate effectiveness across other performance metrics. KNN shows potential with its simple yet robust approach to classifying complex datasets, while NuSVC demonstrates a balanced performance, particularly in precision. However, all classifiers face challenges in achieving optimal accuracy, particularly due to the complexity of the handwritten Lontara digits, which exhibit unique and intricate patterns. The study concludes by suggesting that further improvements can be achieved by refining feature extraction techniques and optimizing the classifiers used. Enhancing feature extraction could provide better representations of the Lontara digits, potentially leading to improved classification accuracy. Additionally, algorithm optimization and the exploration of more advanced classification methods could further enhance the overall performance. This research provides a foundation for the development of automated recognition systems for Lontara script, contributing to its preservation and modern use.

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Authors

Suhendra, Ade; As'ad, Ihwana; Sugiarti, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

Kota Kaimana merupakan kota yang indah dari segi wisata dan parawisata sehingga kota ini juga sering disebut kota 1001 senja dan kota ini terletak di Provinsi Papua Barat. Kota Kaimana memiliki potensi pariwisata seperti wisata alam , sejarah, kuliner, pantai, budaya dan agrowisata. Potensi pariwisata di Kota Kaimana ini belum dikembangkan secara maksimal oleh pemerintah daerah. Hal ini terlihat dari penyediaan sarana prasarana wisata yang belum memadai dan masih kurangnya jumlah wisatawan yang mengunjungi destinasi wisata yang berada di Kota Kaimana. Pemerintah Kabupaten Kaimana akan berupaya mendorong optimalisasi pengembangan parawisata di Kabupaten Kaimana guna mengejar ketertinggalan dengan daerah-daerah lain diluar Papua Barat. Oleh sebab itu pariwisata yang dikembangkan harus melibatkan masyarakat sekitar yaitu menggunakan media–media online seperti website dan konten-konten (youtube) untuk mempromosikan parawisata yang ada di Kota Kaimana. Tujuan perancagan ini untuk mengetahui potensi dan permasalah pengembangan wisata, serta mengetahui potensi yang melibatkan masyarakat lokal. Analisis ini menggunakan model Extreme Programming berbasis web. Hasil yang diharapkan dari Rancangan ini dapat meningkatkan jumlah parawisatawan yang berkunjung ke Kota Kaimana.

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Authors

Syam, Netty, Program Studi Agroteknologi, Fakultas Pertanian, Universitas Muslim Indonesia; Nurliani, Program Studi Agribisnis, Fakultas Pertanian, Universitas Muslim Indonesia; Jabir,Sitti Rahmah, Program Studi Sistem Informasi, Fakultas Ilmu Komputer, Universitas Muslim Indonesia; Hidrawati, Program Studi Agroteknologi, Fakultas Pertanian, Universitas Muslim Indonesia

Abstract

Pesantren Darul Mukhlisin milik Universitas Muslim Indonesia (UMI) yang ada di Desa Mitra Binaan Desa Padanglampe memiliki lahan yang sebahagian digunakan untuk tanaman lada sejak tahun 2015. Populasi lada sekitar 800 pohon dan sudah beberapa kali dipanen. Pengembangan lada oleh masyarakat di sekitar pesantren terkendala oleh adanya musim kering yang panjang di Desa Padanglampe yang berlangsung selama ≥ 6 bulan. Upaya pengembangan lada dilakukan dengan Program Pemberdayaan kelompok wanita untuk membangun daya, mendorong motivasi, membangkitkan kesadaran akan potensi yang dimilikinya dan berusaha untuk mengembangkannya. Metode yang digunakan berupa metode pelatihan partisipatif, yaitu melibatkan sebanyak mungkin peran serta mitra dalam kegiatan ceramah, diskusi, dan praktek pendampingan teknologi dan cipta karya. Teknologi yang diberikan pada mitra berupa Pembibitan lada perdu dan metode penanaman bibit ke planterbag di pekarangan. Pelaksanaan kegiatan Pelatihan dan pendampingan sudah dilaksanakan melalui transfer teknologi pada Aspek produksi Mitra sangat antusias dan berpartisipasi sangat aktif dalam semua kegiatan pelatihan dan pendampingan.

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Authors

Salim, Yulita; Azis, Huzain; Darwis, Herdianti; Purnawansyah,Departement of Computer Science, Universitas Muslim Indonesia, Makassar, Indonesia; Kurubacak, Gulsun; Anggreani, Desi;

Abstract

The scope of this book focuses on how information technology may assist in achieving goals and in providing solutions to problems such as a pandemic. Research on the Internet and on technology has been done, and the findings have applications in various sectors that rely on interdisciplinary knowledge. This book explores and describes state-of-the-art research conducted during the COVID-19 pandemic. Topics covered include the IT viewpoint and the rules governing digital transformation throughout the pandemic. The Digital Revolution sped up by a decade during COVID-19, which impacted both the user experience and that of software developers. As a component of the digital transformation process, this book explores the experiences of both the user and developer when attempting to change and adapt while utilizing an information technology program. This book includes five topics: (1) multidisciplinary artificial intelligence, (2) Smart City and Internet of Things applications, (3) game technology and multimedia applications, (4) data science and business intelligence, and (5) IT hospitality and information systems. Each topic is covered in several book chapters with some application in several countries, especially developing countries. The chapters provide insight from contributors with different perspectives and several diverse fields who present new ideas and approaches to solving problems associated with the worldwide pandemic.

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Authors

Manga, Abdul Rachman; Latief, Muhammad Acqmal Fadhilla; Gaffar, Andi Widya Mufila; Azis, Huzain; Satra, Ramdan; Salim,Yulita, Departement of Computer Science, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

Visual pattern recognition, selection of appropriate image processing techniques, and network architecture are key factors in achieving optimal model performance. This article focuses on the application of Identity Blocks in the context of image processing, especially on unbalanced datasets. Three different datasets, namely Plant Diseases, Rock & Paper Scissors, and Animal Faces, are used in this study, each with unique characteristics. Identity Block, implemented in the ResNet network architecture, helps to overcome the gradient loss problem that often occurs in deep neural networks (DNN) with deep layers. This research specifically explores Identity Block optimization using the hyperband method to improve model performance. The average performance improvement of all optimized models is 4.45% in accuracy, 5.39% in precision, 6.4% in recall, and 6.48% in F1-score. These results show that model optimization is very good at improving identity block performance using the hyperband method.

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Authors

Belluano, Poetri Lestari Lokapitasari; Mashar, Audi Faathirmansyah; Gaffar, Andi Widya Mufila; Manga, Abdul Rachman; Purnawansyah, Departement of Computer Science, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

The scope of this book focuses on how information technology may assist in achieving goals and in providing solutions to problems such as a pandemic. Research on the Internet and on technology has been done, and the findings have applications in various sectors that rely on interdisciplinary knowledge. This book explores and describes state-of-the-art research conducted during the COVID-19 pandemic. Topics covered include the IT viewpoint and the rules governing digital transformation throughout the pandemic. The Digital Revolution sped up by a decade during COVID-19, which impacted both the user experience and that of software developers. As a component of the digital transformation process, this book explores the experiences of both the user and developer when attempting to change and adapt while utilizing an information technology program. This book includes five topics: (1) multidisciplinary artificial intelligence, (2) Smart City and Internet of Things applications, (3) game technology and multimedia applications, (4) data science and business intelligence, and (5) IT hospitality and information systems. Each topic is covered in several book chapters with some application in several countries, especially developing countries. The chapters provide insight from contributors with different perspectives and several diverse fields who present new ideas and approaches to solving problems associated with the worldwide pandemic.

Related SDGs

Authors

Herman, Faculty of Computer Science, Universitas Muslim Indonesia, Makassar, Indonesia; Nasir, Haidawati; Noor, Megat Norulazmi Megat Mohamed, Computer Engineering Technology, MIIT Universiti Kuala Lumpur, Kuala Lumpur, Malaysia; Hasanuddin, Tasrif; Indra, Dolly, Faculty of Computer Science, Universitas Muslim Indonesia, Makassar, Indonesia; Lumentut, Hence Beedwel, Computer Engineering, STMIK Agamua Wamena, Wamena, Indonesia

Abstract

Toraja is a part of Indonesia located on the island of Sulawesi. One form of Toraja culture is carving art made from wood, bamboo or stone. There are around 70 types of Toraja carving motifs. With so many motifs and some motifs being almost similar to each other, it can make it difficult for the public and tourists to know the name of the motif. This research focuses on classifying Toraja carving images using Convolutional Neural Network (CNN) and measuring the performance of the LeN et-5 architecture. The number of Toraja carving motifs used in this research is 7 and the data is 700, where each motif is represented by 100 data. In testing, 3 explorations were carried out, namely dividing training and validation data, Batch Size values, and Target Size values. Based on test results, the 70:30 data division provides the highest level of accuracy compared to other data divisions. Increasing the Batch Size value has a negative impact on the level of accuracy. On the other hand, increasing the Target Size value has a positive impact on the accuracy value. The best Batch Size and Target Size values in this study were 32 and 256x256 with respective accuracies of 50.67% and 69.33%.

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