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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Insect (Informatics and Security) : Jurnal Teknik Informatika Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Indonesian Journal of Data and Science International Journal of Industrial Optimization (IJIO) Jurnal Indonesia : Manajemen Informatika dan Komunikasi Nusantara Hasana Journal Indonesian Community Journal Buletin Sistem Informasi dan Teknologi Islam Jurnal Informatika Progres Journal of Computer Science and Information Technology ARUS JURNAL SAINS DAN TEKNOLOGI Journal of Data Analytics, Information, and Computer Science (JDAICS)
Darniati
Universitas Muhammadiyah Makassar
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Industrial & Manufacturing Engineering Mathematics Nursing Public Health Social Sciences Other

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IMPLEMENTASI ALGORITMA K-MEANS DALAM PENGELOMPOKKAN PEMBERIAN ZAKAT PADA BAZ AL-MARKAZ MAKASSAR SULAWESI SELATAN Darniati
PROGRESS Vol 9 No 1 (2017): April
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (173.384 KB) | DOI: 10.56708/progres.v9i1.71

Abstract

Problematika utama yang sedang dihadapi oleh Badan Amil Zakat adalah masalah penyaluran dana zakat yang kurang tepat sasaran sesuai dengan syariat Islam. Tulisan ini merupakan hasil penelitian yang bertujuan untuk membantu badan amil zakat dalam menyalurkan dana zakat yang terkumpul dari para musakki (pelaku zakat) kepada para mustahik (penerima zakat) yang sesuai dengan syariat Islam. Pada penelitian ini dibuat suatu sistem yang mengimplementasikan metode K-Means. Metode K-Means ini akan mengolah atribut-atribut yang mempengaruhi proses Klasterisasi penduduk calon penerima dana zakat. Atribut-atribut tersebut terdiri dari penghasilan, pekerjaan, alamat, kepemilikan aset, pendidikan terakhir. Proses Klasterisasi terdiri dari data latih yang akan menjadi parameter pada proses klasterisasi data uji pada sistem. Sistem mengelompokkan apakah seseorang memenuhi syarat untuk menerima zakat atau tidak memenuhi syarat menerima zakat. Hasil penelitian ini akan diimplementasikan kepada Badan Amil Zakat dalam menentukan kelayakan penerima zakat.
Implementation of Master Replication Learning Media and Slave Using Kine Master on Makassar Professional STMIK Satriawaty Mallu; Darniati Darniati
Insect (Informatics and Security): Jurnal Teknik Informatika Vol. 8 No. 1 (2022): Oktober 2022
Publisher : Universitas Muhammadiyah Sorong

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33506/insect.v8i1.1970

Abstract

Proses pembelajaran membutuhkan media yang interaktif dan menarik, masa peralihan dari pandemi ke andemi membuat proses pembelajaran saat ini mengharuskan embedded, online diselingi dengan proses offline. Media pembelajaran membuat Replikasi pada Master dan Slave merupakan bagian dari Mata Kuliah Pengolahan Data Terdstribusi pada STMIK Profesional Makassar yang bertujuan untuk mengurangi tingkat kejenuhan mahasiswa belajar online. Aplikasi Kine Master memudahkan mahasiswa untuk memahami proses pembelajaran karena materi yang dikelola dalam bentuk animasi tampilan dalam bentuk video dan teks. Replikasi salah satu metode penyimpanan database pada basisdata terdistribusi. Mahasiswa mampu membuat Replikasi Master dan Slave, mengetahui sejauh mana Efektifitas penggunaan Media Pembelajaran Aplikasi Kine Mater untuk Membuat Replikasi Master dan Slave.
IMPLEMENTASI ALGORITMA K-NEAREST NEIGHBOR TERHADAP PENENTUAN RISIKO KREDIT USAHA MIKRO KECIL DAN MENENGAH Ida; Suardi Hi Baharuddin; Muhammad Faisal; Nur Ramadhan; Darniati
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 4 No. 1 (2023): Jurnal Indonesia : Manajemen Informatika dan Komunikasi (JIMIK)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) AMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v4i1.163

Abstract

This research was carried out in the context of implementing the K-NN algorithm so that a source of information can be produced as a basis for supporting decisions on initial credit applications by customers so that they can help cooperative managers more as knowledge of the progress of credit proposals that are carried out at the Micro, Small and Medium Enterprise Cooperative Service Office ( SMEs) South Sulawesi Province. The K-Nearest Neighbor algorithm is used to classify objects based on attributes and training samples. Among them, from k objects, the k-Nearest Neighbor algorithm uses neighbor classification as the predicted value. The results show that the algorithm produces a classification with a faster calculation time based on the prediction of customer data resulting from the calculation.
Embedded response technology and service cloud platform for vehicle information tracking Muhammad Faisal; Ida Ida; Darniati Darniati; Irmawati Irmawati; Muh Khayyir
International Journal of Industrial Optimization Vol. 4, No. 1 (2023)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/ijio.v4i1.6928

Abstract

Based on the Indonesia national police crime database, it is reported that vehicle theft cases have increased during the Covid-19 pandemic. The database reported an increasing trend of vehicle theft, 4,065 cases from January 2019 to January 2020 in the province and regency region. Therefore, to help police officers work and minimize the criminal cases of vehicle theft, an effective strategy is needed to reduce these threats. This study proposes implementing SMS and QRcode technology embedded in the vehicle for validation information. Cloud computing capabilities can offer real-time network access to technology resources that can be physically located anywhere geographically based on business needs. This technology can rapidly search and show detailed information regarding the specific vehicle, including the vehicle owner, the vehicle registration number, and the validation of the driver's license. To implement and examine the effectiveness of the proposed technology, this study was conducted an experimental study in a real-world setting from January 2021 until April 2021 in Makassar city, Indonesia. This study concluded that the proposed technology could successfully be implemented and effectively show detailed information regarding the specific vehicle based on the experimental results. This study concluded the potential use of the proposed technology in the real world as an alternative solution to minimize the criminal cases of vehicle theft. It can be used as an alternative solution to reduce the increase in criminal cases of inter-island private vehicle theft syndicates.
COMPARISON OF THE PERFORMANCE OF REGRESSION-SPECIFIC AND MULTI-PURPOSE ALGORITHMS Usman, Nasir; Darniati, Darniati; Rosnani, Rosnani; Musdalifa Thamrin; Nurahmad, Nurahmad; Nurdiansyah, Nurdiansyah; Faisal, Muhammad
Nusantara Hasana Journal Vol. 4 No. 8 (2025): Nusantara Hasana Journal, January 2025
Publisher : Yayasan Nusantara Hasana Berdikari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59003/nhj.v4i8.1274

Abstract

Regression is a data science method for evaluating the relationship between independent and dependent variables. This study compares the performance of various regression algorithms using the Boston Housing Dataset, which consists of 506 samples divided into 80% for training and 20% for testing. Performance evaluation was conducted using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the Coefficient of Determination (R²). All algorithms were implemented with default hyperparameter settings provided by the Scikit-learn library to ensure fair comparison. The results showed that versatile algorithms, particularly Gradient Boosting Machines (GBM) and Random Forest, achieved the best performance with R² values of 0.92 and 0.89, respectively, and lower errors. Conversely, regression-specific algorithms, such as Linear Regression and Ridge Regression, recorded R² values of approximately 0.67, while the k-Nearest Neighbors algorithm had the lowest performance with an R² of 0.65. Versatile algorithms proved to be more effective for datasets with complex non-linear patterns, while regression-specific algorithms were better suited for linear data patterns. These findings provide guidance for practitioners in selecting algorithms based on data characteristics and analysis objectives.
Utilization of Artificial Intelligence to Support Technology Development at PT. Aplikanusa Lintasarta – Makassar Muhammad Faisal; Nasir Usman; Ida Mulyadi; Rosnani Rosnani; Darniati Darniati; Musdalifa Thamrin; Mardiah Mardiah; Alvina Felicia Watratan
I-Com: Indonesian Community Journal Vol 5 No 2 (2025): I-Com: Indonesian Community Journal (Juni 2025)
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/icom.v5i2.6945

Abstract

This community service activity aimed to enhance the understanding of Machine Learning (ML) and Deep Learning (DL) technologies among employees of PT. Aplikanusa Lintasarta, as an academic contribution to supporting the company’s digital transformation acceleration. Conducted in a hybrid format (offline and online) on April 21, 2025, the program featured expert speakers and employed an interactive outreach approach combined with applicable case studies. To assess its effectiveness, pre-test and post-test instruments were utilized, revealing an average increase of 45% in participants’ comprehension. Participants' responses were highly positive, as demonstrated by their enthusiasm during discussions and interest in implementing ML/DL within the workplace. This activity not only strengthened internal technological literacy but also supported the development of the national AI ecosystem, in alignment with the launch of GPU Merdeka by Lintasarta.
Fine-Tuning a Large Language Model on Vertex AI for a New Student Registration Chatbot at Universitas Muhammadiyah Makassar Desi Anggreani; Muhyiddin A M Hayat; Lukman; Ahmad Faisal; Khadijah; Darniati
Indonesian Journal of Data and Science Vol. 7 No. 1 (2026): Indonesian Journal of Data and Science
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v7i1.341

Abstract

This study addresses the limitations of manual admission services at Universitas Muhammadiyah Makassar, which often result in delayed and inconsistent information delivery. To overcome these challenges, an institution-specific chatbot was developed by fine-tuning the Gemini 2.5 Flash model on the Google Cloud Vertex AI platform. The model was trained using a curated domain-specific dataset of 1,430 question–answer pairs derived from official documents and frequently asked questions. The fine-tuning process employed supervised learning to enhance contextual relevance and response accuracy. System performance was evaluated using automated text quality metrics, achieving an average BLEU score of 0.23526 and a ROUGE-L Recall score of 0.53424, indicating satisfactory lexical and semantic similarity. Furthermore, a user acceptance evaluation involving 52 respondents yielded a Customer Satisfaction Score (CSAT) of 84.2%, reflecting high user satisfaction. These results demonstrate that fine-tuning a Large Language Model (LLM) for specific institutional needs effectively improves both response quality and service reliability. Ultimately, this approach offers a practical and scalable solution for modernizing student admission services in higher education, ensuring that prospective students receive accurate information in a timely and efficient manner.
Education on Hybrid Multi-Criteria Decision Making and Machine Learning through the Morning Class Program: Integration of Engineering and Technology Nasir Usman; Muhammad Faisal; Sri Wahyuni; Saharuddin Saharuddin; Lisa Fitriani Ishak; Darniati Darniati; Musdalifa Thamrin; Emil Agusalim Habi Talib; Alvina Felicia Watratan
I-Com: Indonesian Community Journal Vol 6 No 2 (2026): I-Com: Indonesian Community Journal (Juni 2026)
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/i-com.v6i2.9453

Abstract

The Society 5.0 era requires mastery of transparent and intelligent decision systems, yet practical understanding of integrating Machine Learning (ML) and Multi-Criteria Decision Making (MCDM) through Hybrid Intelligence frameworks remains limited in academic environments. This community service aims to enhance the scientific capacity of academics through the "Morning Class" international program. The methodology employed an online joint lecture approach involving collaboration between Universitas Muhammadiyah Makassar and Multimedia University Malaysia. The activity involved 48 participants and was divided into three phases: initial evaluation, delivery of theoretical-practical integration modules, and final evaluation. Results indicate a significant increase in understanding, with the average score rising from 65.8 in the pre-test to 87.5 in the post-test. The highest improvement (36%) was recorded in the hybrid framework implementation indicator. These findings confirm that the synergy between human expert ethical values and machine data processing speed is a crucial solution for modern decision-making. The program recommends further technical workshops to support deeper research implementation for partner institutions.
A Hybrid K-Means and Neural Network for Enhancing Students’ Academic Performance Suriani Suriani; Muhammad Faisal; Darniati Darniati; Emil Agusalim H. T; Muhammad Syafaat S. Kuba; Swa Lee Lee; Nurdiansyah Nurdiansyah
Buletin Sistem Informasi dan Teknologi Islam (BUSITI) Vol 7, No 2 (2026)
Publisher : Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/busiti.v7i2.3467

Abstract

Ketersediaan data pada Learning Management System (LMS) mendorong penerapan pembelajaran adaptif di pendidikan tinggi. Penelitian ini mengusulkan kerangka kerja hybrid berbasis kecerdasan buatan yang mengintegrasikan K-Means clustering dan Neural Network untuk profil mahasiswa berbasis perilaku dan prediksi kinerja akademik. Model divalidasi menggunakan Open University Learning Analytics Dataset yang mencakup data demografi, interaksi, dan performa akademik. Hasil menunjukkan akurasi sebesar 0,68 dan F1-score sebesar 0,66, melampaui metode dasar dengan stabilitas yang lebih baik. Clustering menghasilkan silhouette score 0,62 yang menunjukkan pemisahan kelompok yang jelas. Selain itu, sistem meningkatkan relevansi konten sebesar 27% dan menurunkan risiko putus studi sebesar 18%, dengan waktu inferensi rata-rata 0,85 detik. Temuan ini menunjukkan efektivitas kerangka dalam mendukung pembelajaran adaptif yang dipersonalisasi dan skalabel
Strengthening AI and DSS Synergy for Sustainable Research: A Community Engagement for Lecturers and Researchers in Palopo Muhammad Faisal; Nasir Usman; Emil Agus Salim Habi Talib; Medy Wisnu Prihatmono; Lisa Fitriani Ishak; Musdalifa Thamrin; Darniati Darniati; Alvina Felicia Watratan; Saharuddin Saharuddin; Muh Ilham Akbar
I-Com: Indonesian Community Journal Vol 5 No 4 (2025): I-Com: Indonesian Community Journal (Desember 2025)
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/i-com.v5i4.8547

Abstract

The rapid development of digital technology demands a more innovative and data-driven research paradigm, yet the utilization of Artificial Intelligence (AI) and Decision Support Systems (DSS) in academic environments remains hindered by digital literacy gaps and the dominance of subjective manual methods. This community engagement program aims to introduce and strengthen participants’ understanding of the synergy between AI and DSS in supporting sustainable research in the era of digital transformation. The program employed a participatory approach through the Quadruple Helix model involving 359 participants consisting of lecturers, researchers, and practitioners. Methods included interactive lectures, technical mentoring on hybrid intelligence (integration of Machine Learning and Multi-Criteria Decision Making), and collaborative discussions via the Zoom platform. The results indicate a 35.6% improvement in participants' digital literacy, with the mean score increasing from 62.5 to 84.8. Furthermore, the technical readiness survey yielded a high score of 4.35 on a Likert scale, with participants successfully identifying practical AI–DSS applications in smart agriculture and MSME development. This program has successfully established an initial foundation for an adaptive and inclusive research ecosystem.
Co-Authors Ahmad Faisal Alvian Syah Burhani Alvina Felicia Watratan Andi Makbul Syamsuri Ashabul Kahfi Bakti, Rizki Yusliana Chyquitha Danuputri Chyquitha Danuputri Desi Anggreani Emil Agus Salim Habi Talib Fachrim Irhamna Rachman Fahrim Irhamna Rachman Farida Gaffar Ida Ida Mulyadi, Ida Irmawati Irmawati Khadijah Lisa Fitriani Ishak Lukman M. Ray Togubu Mallu, Satriawaty Mardiah Mardiah Medy Wisnu Prihatmono Muh Ilham Akbar Muh Khayyir Muhammad Alif Syafan Muhammad Dzulfikar Hidayat Muhammad Faisal Muhammad Syafaat S. Kuba Muhyiddin A.M Hayat Musdalifa Thamrin Nasir Usman Nasir Usman Nur Ramadhan Nurdiansyah Nurdiansyah Nurfadillah Nurul Kusumawardani Risal Rosnani Rosnani Rosnani Rosnani Saharuddin Saharuddin Sri Wahyuni Suardi Hi Baharuddin Suriani Suriani Swa Lee Lee Syafruddin Syarif Titin Wahyuni Zulfajri Basri Hasanuddin