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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Advances in Applied Sciences TEKNIK INFORMATIKA Bulletin of Electrical Engineering and Informatics Harmoni: Jurnal Pendidikan dan Penelitian Seni Budaya Journal of Geoscience, Engineering, Environment, and Technology SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan ILKOM Jurnal Ilmiah Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Ainet : Jurnal Informatika International Journal of Industrial Optimization (IJIO) Jurnal Teknik Informatika (JUTIF) Mattawang: Jurnal Pengabdian Masyarakat Jurnal Indonesia : Manajemen Informatika dan Komunikasi Nusantara Hasana Journal Indonesian Community Journal Aptekmas : Jurnal Pengabdian Kepada Masyarakat Buletin Sistem Informasi dan Teknologi Islam Journal of Practical Computer Science (JPCS) Jurnal Informatika Progres ROUTERS: Jurnal Sistem dan Teknologi Informasi Journal of Embedded Systems, Security and Intelligent Systems Jurnal INFOTEL Jurnal Indonesia : Manajemen Informatika dan Komunikasi Journal of Computer Science and Information Technology ARUS JURNAL SAINS DAN TEKNOLOGI Journal of Muhammadiyah’s Application Technology
Muhammad Faisal
Universitas Muhammadiyah Makassar
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

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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.
Enhancing Faculty Digital Competence through Learning Management System Training: A Case Study at STMIK Profesional Makassar Nasir Usman; Muhammad Faisal; Sri Wahyuni; Lisa Fitriani Ishak; A. Muhammad Syafar; Saharuddin Saharuddin; Nurdiansyah Nurdiansyah; Andi Muhammad Nur Hidayat
I-Com: Indonesian Community Journal Vol 5 No 3 (2025): I-Com: Indonesian Community Journal (September 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.v5i3.7787

Abstract

The digital transformation in higher education necessitates the enhancement of faculty capacity in leveraging online learning technologies, one of which is the Moodle Learning Management System (LMS). This community service initiative aimed to strengthen the competencies of lecturers at STMIK Profesional Makassar in effectively and independently utilizing Moodle LMS. The training was conducted in two sessions using a hands-on approach, covering the management of instructional materials, discussion forums, quizzes, assignments, and learning evaluations. The results indicated significant improvements in mastering Moodle’s structure and core features, lecturers’ readiness to manage an inclusive, flexible, and adaptive digital learning ecosystem, as well as their ability to develop digital learning materials, manage discussion forums, and design system-based evaluations. These enhancements had a direct impact on the quality of interaction and student learning outcomes. Moreover, this initiative supported sustainable digital transformation at the institutional level, reinforcing STMIK Profesional Makassar’s image as a progressive higher education institution responsive to the challenges of the Fourth Industrial Revolution.
OPTIMALISASI DISTRIBUSI PEMILIH TERHADAP TPS MENGGUNAKAN METODE CLUSTERING FUZZY C-MEANS Djalil, Sony Achmad; Muhammad Faisal; Muhyiddin AM Hayat; Titin Wahyuni
Ainet : Jurnal Informatika Vol. 7 No. 2 (2025): September (2025)
Publisher : Universitas Muhammadiyah Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26618/6jc0a759

Abstract

General elections are a fundamental pillar of modern democratic systems, requiring an implementation that is efficient, fair, and inclusive. One of the key factors influencing the success of an election is the determination of polling station locations, as their placement directly affects voter accessibility, travel distance, and public participation. Inappropriate polling station allocation can lead to service inequality, voter congestion, and a decline in the overall quality of the voting process. At the local administrative level, polling station determination is still largely conducted manually by grouping voters based on neighborhood or administrative boundaries. This conventional approach is often time consuming, prone to administrative errors, and frequently results in an uneven distribution of voters across polling stations. In addition, electoral regulations impose limits on the maximum number of voters per polling station to ensure smooth and orderly voting procedures, which are not always optimally satisfied through manual methods. As voter data complexity and geographic dispersion increase, computational approaches are needed to support more effective decision making. Clustering techniques in unsupervised learning enable objective grouping of voters based on spatial characteristics. The Fuzzy C-Means method represents a suitable approach because it can accommodate data uncertainty and overlapping service areas. The application of this method is expected to produce a more efficient, equitable, and data driven distribution of polling stations, thereby contributing to the improvement of election management quality and democratic integrity
Stacking architecture-endpoint detection: a hybrid multi layered architecture for endpoint threat detection Wahid, Abd Rahman; Anggreani, Desi; Hayat, Muhyiddin A. M.; Abd Rahman, Aedah; Faisal, Muhammad
International Journal of Advances in Applied Sciences Vol 14, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i4.pp1263-1280

Abstract

Modern endpoint threat detection systems face persistent challenges in balancing detection accuracy, resilience against zero-day attacks, and the interpretability of artificial intelligence (AI) models. Although deep learning (DL) approaches often achieve high accuracy on benchmark datasets, they remain vulnerable to adversarial perturbations and operate as opaque “black boxes,” thereby reducing trust and limiting practical adoption in critical infrastructures. This research introduces stacking architecture-endpoint detection (STACK-ED), a hybrid multi-layered architecture for endpoint threat detection. STACK-ED integrates three complementary paradigms: supervised learning for known attack patterns, self-supervised Fgraph-based learning for structural relationships, and unsupervised anomaly detection for emerging or unknown threats. The outputs are consolidated by a meta learner, followed by a post-hoc correction (PHC) mechanism to minimize false negatives. The framework was evaluated on a combined benchmark dataset (CSE-CIC-IDS2018 and UNSW-NB15, hereafter referred to as HIDS-Set). Experimental results demonstrate state-of-the-art performance, achieving an F2-score of 98.89% after hybrid integration and active learning, with the primary optimization objective being the reduction of undetected attacks. Furthermore, the Shapley additive explanations (SHAP) method enhances interpretability by revealing feature contributions, while the PHC successfully recovered 62.64% of missed zero-day candidates. The findings position STACK-ED not only as a highly accurate detection model but also as an adaptive, resilient, and transparent framework, offering practical implications for enterprise-grade endpoint defense and future zero-trust cybersecurity systems.
EKSPRESI SENI DI BALIK JERUJI (PRAKTIK ARTISTIK DI RUMAH TAHANAN NEGARA KELAS IIB KABUPATEN JENEPONTO) Rahmat Anbiyah; Muhammad Faisal; Irsan Kadir
Harmoni: Jurnal Pemikiran Pendidikan, Penelitian Ilmu-ilmu Seni, Budaya dan Pengajarannya Vol 15 No 1 (2025): Harmoni: Jurnal Pendidikan dan Penelitian Seni Budaya
Publisher : Program Studi Pendidikan Seni Rupa Fakultas Keguruan dan Ilmu Pendidikan Universitas Muham

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26618/hw2z5a92

Abstract

Masalah utama dalam penelitian ini yaitu bagaimana praktik dan pola pembinaan keterampilan seni rupa yang dilakukan oleh warga binaan pemasyarakatan di Rumah Tahanan Negara Kelas IIB Kabupaten Jeneponto. Penelitian ini bertujuan untuk mengetahui bagaimana pelaksanaan praktik dan pola pembinaan keterampilan seni rupa yang dihasilkan warga binaan di Rumah Tahanan Negara Kelas IIB Kabupate Jeneponto.Penelitian ini menggunakan metode kualitatif dengan menggunakan pendekatan studi kasus bermaksud menggambarkan dan menjelaskan kondisi praktik dan pola pembinaan disaaat berkarya seni di dalam Rumah Tahanan Negara Kelas IIB Kabupaten Jeneponto. Subjek dalam Penelitian Ini  adalah warga binaan pemasyarakatan dan pembina keterampilan di Rumah Tahanan Negara Kelas IIB Kabupaten Jeneponto. Hasil penelitian menunjukkan bahwa praktik seni rupa di Rumah Tahanan Negara Kelas IIB Kabupaten Jeneponto ialah sebagai media untuk membina warga binaan pemasyaratan. Adapun jenis karya yang dibuat oleh warga binaan ialah, lemari, meja, guci rokok, bingkai, tempat tisu, heli kopter korek dan motor korek. Kemudian untuk pola pembinaan keterampilan terhadap warga binaan beracu pada kurikulum pelatihan berbasis kompetensi dan program pelatihan berbasis kompetensi. Dengan menggunakan pendekatan pedagogy Contructivism. Setelah karya mereka selesai, karya mereka akan ikut serta dalam pameran, menjadi prabotan warga binaan pemsyarakatan dan menjadi hadiah untuk keluarga yang berkunjung. Diharapkan Warga binaan pemasyarkatan yang menghasilkan karya seni sepatutnya mendapatkan apresiasi yang lebih dari pihak Rumah tahanan Negara Kelas IIB Kabupaten Jeneponto.
IMPLEMENTASI SISTEM DETEKSI PRODUK BOIKOT BERBASIS WEBSITE REAL-TIME MENGGUNAKAN METODE YOLOv10 Nur Rahman, Ahmad; Habi Talib, Emil Agusalim; Rachman, Fahrim Irhamna; Bakti, Rizki Yusliana; Faisal, Muhammad; S. Kuba, Muhammad Syafaat
PROGRESS Vol 18 No 1 (2026): April
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56708/progres.v18i1.525

Abstract

Manual identification ofboycott products remains a challenge for the public due to limited access to information and the complexity of brand affiliations. This study aims to develop a real-time, website-based boycott product detection system using the You Only Look Once version 10 (YOLOv10) algorithm. The dataset consists of images of food and beverage product packaging collected from various online sources, annotated using the bounding box method, and classified into five categories. The model was trained and tested using separate test data, while performance evaluation was conducted using a confusion matrix with precision, recall, and f1-score metrics. In addition, functional testing of the system was performed using the Black Box Testing method. The result indicate that the YOLOv10 model is capable of detecting boycott product with good performance and can be effectively integrated into a real-time web-based system. The proposed system is expected to assist users in identifying boycott products more quickly and accurately.
PERBANDINGAN CNN DAN YOLO PADA SISTEM PENGENALAN WAJAH BERBASIS PRESENSI Nurfadillah; Ida; Darniati; Yusliana Bakti, Rizki; Wahyuni, Titin; Faisal, Muhammad
PROGRESS Vol 18 No 1 (2026): April
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56708/progres.v18i1.532

Abstract

Face recognition based on image data has been widely applied in automated attendance systems; however, it still faces challenges related to accuracy and efficiency under varying lighting conditions and facial pose variations. This study aims to compare the performance of Convolutional Neural Network (CNN) and You Only Look Once (YOLO) methods for face detection and recognition in a deep learning–based attendance system. The dataset consists of facial images collected from students in a limited campus environment with several variations in viewpoint and illumination. The research stages include image preprocessing, training of CNN and YOLO models, and performance evaluation using accuracy, precision, recall, and computation time metrics. The experimental results indicate that YOLO outperforms CNN in terms of detection speed and performance stability, while CNN demonstrates competitive classification performance on limited datasets. This study provides empirical insights into the characteristics of both methods in attendance system scenarios and can serve as a reference for selecting appropriate models for real-world implementation. The main limitations of this study are the dataset size and the restricted data acquisition scope.
KLASIFIKASI TANAMAN OBAT TRADISIONAL BERBASIS CITRA BUAH DAN DAUN Kusumawardani, Nurul; Danuputri, Chyquitha; Darniati; Faisal, Muhammad; A.M Hayat, Muhyiddin; S. Kuba, Muhammad Syafaat; Anggreani, Desi
PROGRESS Vol 18 No 1 (2026): April
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56708/progres.v18i1.534

Abstract

Indonesia is a megabiodiversity country with extensive use of traditional medicinal plants; however, plant identification in natural environments remains largely manual and error-prone. Recent advances in deep learning, particularly Vision Transformer (ViT), provide a promising solution by effectively capturing global spatial features for image classification. This study applies a ViT-Base/16 model to automatically classify fruit and leaf images of Indonesian medicinal plants. The dataset comprises 1,000 field-collected images from Galung Village, West Sulawesi, covering 20 classes (10 medicinal and 10 non-medicinal plants). The model was fine-tuned using the AdamW optimizer with a learning rate of 2×10⁻⁵ and trained for 30 epochs with cosine annealing. The proposed approach achieved high performance, with 99.33% accuracy, 99.41% precision, 99.33% recall, and a 99.33% F1-score, while binary classification between medicinal and non-medicinal plants reached 100% accuracy. The system was deployed as a Flask-based web application, demonstrating reliable functionality and practical response times. Overall, the results confirm the effectiveness of Vision Transformer for medicinal plant classification under natural conditions and highlight its potential to support digital documentation, education, and the preservation of local ethnobotanical knowledge.
PENERAPAN ALGORITMA MOBILENETV2 UNTUK KLASIFIKASI HURUF HIJAIYAH BERBASIS GESTUR TANGAN Riswan, Muh.; Wahyuni, Titin; Danuputri, Chyquitha; Habi Talib, Emil Agusalim; Faisal, Muhammad; Anas, Lukman; Agung, Andi
PROGRESS Vol 18 No 1 (2026): April
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56708/progres.v18i1.535

Abstract

The digitalization of religious education offers significant opportunities to enhance Hijaiyah letter learning, particularly for the hearing-impaired community through visual gesture recognition. This study aims to develop and evaluate a real-time web-based classification system for 28 Hijaiyah hand gestures using the MobileNetV2 architecture. The research methodology involves a quantitative approach utilizing transfer learning with a balanced dataset of augmented images. The model was trained using fine-tuning techniques and deployed on a web platform using TensorFlow.js and MediaPipe for efficient on-device inference. Experimental results demonstrate that the model achieved an overall accuracy of 84% on the independent test set, with specific classes reaching near-perfect detection in real-time scenarios, although misclassification persisted among visually similar gestures. The system effectively balances computational efficiency with classification performance, minimizing latency during user interaction. In conclusion, the implementation of MobileNetV2 facilitates a responsive and accessible educational tool, proving the viability of computer vision in creating inclusive religious learning environments without requiring complex server-side infrastructure.
Klasifikasi Mahasiswa Berprestasi Menggunakan Algoritma Fuzzy K-Nearest Neighbors Berdasarkan Riwayat Akademik dan Aktivitas Organisasi FUAD, NUR FUAD ALRASYID.S; ALRASYID.S, NUR FUAD; Muhammad Faisal; Muhyiddin A.M Hayat
Ainet : Jurnal Informatika Vol. 8 No. 1 (2026): Maret (2026)
Publisher : Universitas Muhammadiyah Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26618/tvh1qe20

Abstract

Penentuan mahasiswa berprestasi di perguruan tinggi masih banyak dilakukan secara manual dengan fokus utama pada aspek akademik, sehingga berpotensi menimbulkan subjektivitas dan ketidakefisienan. Permasalahan ini mendorong perlunya pendekatan berbasis data yang mampu mengintegrasikan aspek akademik dan non-akademik secara objektif. Penelitian ini bertujuan untuk membangun model klasifikasi mahasiswa berprestasi menggunakan algoritma Fuzzy K-Nearest Neighbors (F-KNN) berdasarkan riwayat akademik dan aktivitas organisasi. Data penelitian berasal dari 112 mahasiswa Universitas Muhammadiyah Makassar yang mencakup data akademik, prestasi, dan keaktifan organisasi. Metode penelitian meliputi pra-pemrosesan data, feature engineering, pembobotan fitur, penerapan algoritma F-KNN, serta evaluasi model menggunakan confusion matrix dan 5-fold cross validation. Hasil pengujian menunjukkan bahwa model F-KNN dengan parameter k = 5 dan m = 2.0 mampu mencapai akurasi 91,3%, precision 81,82%, recall 100%, dan F1-score 90%. Hasil ini menunjukkan bahwa F-KNN efektif dalam mengidentifikasi mahasiswa berprestasi secara akurat dan stabil. Implikasi penelitian ini adalah tersedianya sistem pendukung keputusan yang objektif dan efisien untuk membantu perguruan tinggi dalam proses seleksi mahasiswa berprestasi.
Co-Authors . Darniati Abd Rahman Wahid Abd Rahman, Aedah Abdul Rakhim Nanda Adnan Ahsan Agung, Andi Ahmad Nur Rahman Akbar DB, Andi Muhammad Alizha Nur Arspandy ALRASYID.S, NUR FUAD Alvian Syah Burhani Alvina Felicia Watratan Alvina Felicia Watratan Andi Agung Andi Citra Ayu Lestari Andi Harmin Andi Makbul Syamsuri ANDI MAWADDA TAIBA MAWADDA TAIBA Andi Muhammad Akbar DB Andi Muhammad Nur Hidayat Ari Ahmad Dahril Ashabul Kahfi Azzah Aulia Syarif Baharuddin, Suardi Hi Bakti, Rizki Yusliana Billy Eden William Asrul Burhanuddin, Fathurrahman Chyquitha Danuputri Chyquitha Danuputri Chyquitha Danuputri Danuputri, Chyquitha Darniati Dayang Aisyah Desi Anggreani Djalil, Sony Achmad Emil Agus Salim Habi Talib Erick Yusuf Kotte Erika Yanti Fachrim Irhamna Rachman Fahrim I. Rahman Fahrim Irhamna Rachman Fahrim Irhamna Rachman Fahrim Irhamna Rachman Farida Gaffar Feng, Zhipeng Ferdiansyah Firdaus Fidaus FUAD, NUR FUAD ALRASYID.S Galbi Nadifah Hamdan Gani Hamzah Al Imran Hardita Subanda Herlinah Herlinah Hi Baharuddin, Suardi HS, Hafsah Ida Ida Ida Mulyadi, Ida Indra Aditya Indriyanti Indriyanti Azis Irmawati Irmawati Irnawaty Idrus IRSAN KADIR Jihan Izzathul Mujidah Kusumawardani, Nurul Lisa Fitriani Ishak Lukman Lukman Anas LUKMAN ANAS Lukman Anas Lukman Lukman M Agusalim M Agusalim M. Fikri Haikal Ayatullah Made Widia, I Dewa Majeri Majeri Mardiah Mardiah Mardiah Mardiah Medy Wisnu Prihatmono Medy Wisnu Prihatmono Muh Akram Riyadi Ramadhan Muh Dzikri Alfauzan Nuzul Muh Ilham Akbar Muh Ilham Akbar Muh Khayyir Muh. Amir Zainuddin Muh. Fikri Haekal Muh. Riswan Muhammad Aditya Yudhistira Muhammad Agusalim Muhammad Asygar Faeruddin Muhammad Hasraddin Hasnan Muhammad Khadafi Muhammad Khaiyyir Muhammad Syafaat S. Kuba Muhsin, Muh Arief Muhyiddin A.M Hayat Muhyiddin A.M Hayat Muhyiddin A.M. Hayat Musdalifa Thamrin Musdalifa Thamrin Muthalib, Ade Nirwani Abdurahman Nasir Usman Nasir Usman Nini Apriani Rumata Nur Alam Nur Annisa Syarifuddin Nur Milani Hidayah Nur Rahman, Ahmad Nur Ramadhan Nur Ramadhan, Nur Nurahmad Nurahmad Nurdiansyah Nurdiansyah Nurfadillah Nurfadillah Nurnawaty Nurul Kusumawardani Nurul Qalbi Parwati Parwati Praja, Soemitro Emin Rahmania Rahmat Anbiyah Rasyidi, Muhammad Fachri Rio Prasetyo Lukodono Riswan, Muh. Rizky Yusliana Bakti Rosnani Rosnani Rosnani Rosnani Saharuddin Saharuddin Samsuria, Samsuria Sarina Siti Marwa Sri Wahyuni Suardi Hi Baharuddin Suharmin Djumali Suriani Suriani Swa Lee Lee Syadiah Nor Wan Shamsuddin SYAFAR, A. MUHAMMAD Syahril Akbar Syahrul Hasbir Syahrul Suhardi Titik Khawa Abd Rahman Titik Khawa Abd Rahman Titik Khawa Abd Rahman Titik Khawa Abdul Rahman Titin Wahyuni Tri Wahyuni Try Gustaf Said Wa Nanda Sulystrian Wahid, Abd Rahman Wiwin Fuad Sanjaya