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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 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 Jurnal INFOTEL Jurnal Indonesia : Manajemen Informatika dan Komunikasi ARUS JURNAL SAINS DAN TEKNOLOGI
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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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.
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.
Student Emotion Recognition from Low-Quality Videos Using Multimodal Deep Learning ANDI MAWADDA TAIBA MAWADDA TAIBA; Rizki Yusliana Bakti; Muhammad Faisal; Muhammad Syafaat S. Kuba; Lukman Anas; Emil Agusalim H. T; Fahrim I. Rahman
JURNAL INFOTEL Vol 18 No 1 (2026): February
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v18i1.1523

Abstract

Emotion recognition plays a critical role in intelligent e-learning systems by enabling adaptive feedback and timely pedagogical interventions based on students’ affective states. However, most existing approaches rely heavily on visual facial cues, which are highly vulnerable to real-world conditions such as low-resolution video, partial facial occlusion, poor lighting, and unstable network connections commonly encountered in online learning environments. These limitations significantly degrade the performance of unimodal deep learning models. To address this challenge, this study proposes a multimodal deep learning framework for student emotion recognition that is robust to low-quality and occluded video input. The proposed model integrates visual and audio modalities through a hybrid architecture, combining a lightweight CNN-based visual feature extractor with a BiLSTM-based speech emotion model. An attention-based fusion mechanism is employed to adaptively weight cross-modal features, allowing the system to compensate for degraded or missing visual information using complementary acoustic cues. Experimental evaluations are conducted using publicly available datasets representative of realistic online learning scenarios, including DAiSEE and RAVDESS, with additional augmentation to simulate varying levels of occlusion and video degradation. The results demonstrate that the multimodal approach consistently outperforms unimodal baselines, particularly under high occlusion conditions, while maintaining computational efficiency suitable for near real-time deployment. These findings confirm that multimodal fusion with attention mechanisms provides a more resilient and practical solution for emotion-aware e-learning systems operating under non-ideal input conditions
MCDA-AHP-GIS-Based Site Suitability Assessment for a Multi-Utility Tunnel in Panakkukang Sub-district, Makassar City , Indonesia Muthalib, Ade Nirwani Abdurahman; Rumata, Nini Apriani; Burhanuddin, Fathurrahman; Faisal, Muhammad; Firdaus; Rahmania; Bakti , Rizki Yusliana
Journal of Geoscience, Engineering, Environment, and Technology Vol. 11 No. 02 (2026): JGEET Vol 11 No 02 : June (2026)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/jgeet.2026.11.02.26770

Abstract

This study develops a transparent MCDA–AHP–GIS framework to screen Multi-Utility Tunnel (MUT) corridor suitability in Panakkukang Sub-district, Makassar City, using 2024 baseline datasets and five criteria: utility/network density (C1), road functional class and corridor capacity (C2), flood susceptibility (C3), activity intensity (C4; proxied by kelurahan-level population density), and spatial planning compatibility with RTRW/RDTR (C5). All layers were standardized and reclassified (1–3 or 1–5) and integrated using Weighted Linear Combination (WLC) with AHP-derived weights (CR = 0.028), where C1 (0.26) and C5 (0.24) were highest, followed by C2 (0.19), C4 (0.16), and C3 (0.15). The 2,918.3-ha study area was classified into Very Unsuitable (88.2 ha; 3.0%), Unsuitable (405.8 ha; 13.9%), Moderately Suitable (764.9 ha; 26.2%), Suitable (935.8 ha; 32.1%), and Highly Suitable (723.6 ha; 24.8%). A corridor-focused overlay shows that 436.9 ha fall within the Suitable–Highly Suitable mask, of which 127.3 ha (29.1%) intersect high flood-hazard zones, indicating that some priority segments require attention during detailed planning. Uncertainty mainly arises from buffer distances and reclassification thresholds and from non-differentiating attributes in some utility layers; however, a ±10% weight sensitivity test yields only minor shifts in class areas and preserves the main priority-corridor pattern.
Optimasi Kinerja Arsitektur CNN Ringan Menggunakan Pendekatan Bayesian untuk Identifikasi Skrip Bima Dayang Aisyah; Muhammad Faisal; Lukman Anas; Abd Rakhim Nanda; Syadiah Nor Wan Shamsuddin; Muhammad Syafaat S. Kuba
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.v7i2.3462

Abstract

Identifikasi aksara daerah penting untuk mendukung pelestarian budaya digital, namun masih terkendala keterbatasan dataset, kemiripan karakter, dan kebutuhan model yang efisien. Penelitian ini mengoptimasi arsitektur Lightweight CNN menggunakan Bayesian Optimization untuk identifikasi aksara Bima. Dataset terdiri atas 6.190 citra aksara Bima dalam 44 kelas, mencakup aksara Bima baru dan lama. Model menggunakan MobileNetV3-Large sebagai backbone dengan optimasi learning rate, dropout, batch size, dan konfigurasi fine-tuning melalui Tree-structured Parzen Estimator. Hasil eksperimen menunjukkan accuracy 93,06%, precision 92,26%, recall 92,55%, dan F1-score 91,91%, lebih unggul dibanding machine learning tradisional, CNN konvensional, dan beberapa CNN ringan modern. Target accuracy 90% dicapai pada trial keempat. Dengan 3.253.676 parameter dan waktu inferensi 63,35 ms per citra, model ini terbukti akurat, efisien, dan berpotensi diterapkan pada digitalisasi manuskrip serta OCR aksara daerah.
A Calibrated ROI-Aware Hybrid CNN-Transformer for Kidney Stone Presence Classification on Heterogeneous Axial CT Images Muh Ilham Akbar; Muhammad Faisal; Desi Anggreani; Abd Rakhim Nanda; Try Gustaf Said; Muhammad Syafaat S. Kuba
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.v7i2.3463

Abstract

Batu ginjal merupakan penyebab umum nyeri pinggang akut, dan CT non-kontras menjadi standar referensi untuk mendeteksi kalkulus. Pada penelitian ini, istilah heterogen merujuk pada variasi protokol akuisisi antarrumah sakit, seperti perbedaan dosis radiasi, ketebalan irisan, rekonstruksi, dan bidang pandang, yang dapat mengubah tampilan citra serta menurunkan konsistensi pembacaan. Penelitian ini mengusulkan model hibrida CNN-Transformer yang sadar ROI (implisit) untuk klasifikasi keberadaan batu ginjal pada citra CT aksial heterogen. Arsitektur menggabungkan EfficientNet-B3, encoder Transformer ringan, dan Convolutional Block Attention Module (CBAM) tanpa anotasi ROI manual. Dataset terdiri dari 3.364 citra (1.577 batu, 1.787 non-batu) dengan pemisahan bertingkat 70/15/15. Evaluasi mencakup akurasi, presisi, sensitivitas, spesifisitas, F1, ROC-AUC, PR-AUC, inspeksi kalibrasi, dan audit Grad-CAM. Hasil menunjukkan bahwa penambahan Transformer meningkatkan kinerja dibanding baseline CNN, sedangkan CBAM menggeser profil kesalahan ke sensitivitas yang lebih tinggi. Varian Hybrid+Attention mencapai akurasi 0,9861, F1 0,9851, dan ROC-AUC 0,9967 pada set uji, dengan jumlah negatif palsu lebih rendah dibanding varian hibrida tanpa perhatian. Temuan ini menunjukkan potensi model sebagai alat bantu dokter untuk triase dan pembacaan awal yang lebih konsisten pada data lintas protokol, meskipun validasi eksternal, pemisahan berbasis pasien, dan metrik kalibrasi kuantitatif masih diperlukan sebelum klaim kesiapan klinis.
Explainable Fake News Detection in Indonesian Language Using IndoBERT and SHAP Muhammad Hasraddin Hasnan; Rizky Yusliana Bakti; Muhammad Faisal; Titik Khawa Abd Rahman; Nurnawaty Nurnawaty; Muhammad Syafaat S. Kuba; Titin Wahyuni
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.v7i2.3466

Abstract

Perkembangan teknologi informasi dan media sosial telah mempercepat penyebaran berita palsu, sehingga diperlukan sistem deteksi yang akurat, andal, dan mudah diinterpretasikan. Penelitian ini bertujuan mengembangkan sistem deteksi fake news berbahasa Indonesia dengan mengintegrasikan IndoBERT sebagai model klasifikasi teks dan SHAP sebagai pendekatan Explainable Artificial Intelligence (XAI) untuk menjelaskan kontribusi kata terhadap hasil prediksi. Dataset diperoleh dari TurnBackHoax dan Kaggle, kemudian melalui tahapan preprocessing berupa cleaning text, filtering bahasa Indonesia, tokenisasi, serta penyeimbangan data menggunakan random oversampling pada data latih. Dari 5.347 data awal, diperoleh 4.980 data setelah filtering bahasa Indonesia, terdiri atas 3.613 data valid dan 1.367 data hoaks. Data dibagi secara stratifikasi dengan rasio 80% untuk pelatihan dan 20% untuk pengujian. Setelah oversampling, data latih menjadi seimbang dengan masing-masing 2.890 sampel per kelas. Hasil eksperimen menunjukkan bahwa model baseline TF-IDF dan Logistic Regression memperoleh akurasi 77%, sedangkan IndoBERT mencapai akurasi 87%, dengan precision 0,87, recall 0,95, dan F1-score 0,91 pada kelas hoaks. Visualisasi SHAP menunjukkan token penting yang memengaruhi klasifikasi. Hasil ini membuktikan bahwa integrasi IndoBERT dan SHAP efektif meningkatkan deteksi berita palsu sekaligus memberikan transparansi model.
Co-Authors . Darniati Abd Rahman, Aedah Abdul Rakhim Nanda Adnan Ahsan Agung, Andi Ahmad Nur Rahman Akbar DB, Andi Muhammad Akbar, Syahril Alvina Felicia Watratan Alvina Felicia Watratan Andi Agung Andi Citra Ayu Lestari Andi Harmin Andi Makbul Syamsuri Andi Makbul Syamsuri ANDI MAWADDA TAIBA MAWADDA TAIBA Andi Muhammad Akbar DB Andi Muhammad Nur Hidayat 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 Emil Agusalim H. T Emil Agusalim H. T Erick Yusuf Kotte Erika Yanti Fachrim Irhamna Rachman Faeruddin, Muhammad Asygar Fahrim I. Rahman Feng, Zhipeng Ferdiansyah Firdaus Hamdan Gani Herlinah Herlinah Hi Baharuddin, Suardi HS, Hafsah Ida Ida Ida Mulyadi, Ida Indra Aditya Irmawati Irmawati IRSAN KADIR Jihan Izzathul Mujidah Kusumawardani, Nurul Lisa Fitriani Ishak Lukman LUKMAN ANAS Lukman Lukman Made Widia, I Dewa Mardiah Mardiah Mardiah Mardiah Medy Wisnu Prihatmono Muh Ilham Akbar Muh Khayyir Muh. Riswan Muhammad Asygar Faeruddin Muhammad Hasraddin Hasnan Muhammad Khaiyyir Muhammad Syafaat Muhammad Syafaat S. Kuba Muhyiddin A.M Hayat Mujidah, Jihan Izzathul Musdalifa Thamrin Musdalifa Thamrin Muthalib, Ade Nirwani Abdurahman Nasir Usman Nasir Usman Nini Apriani Rumata Nur Alam Nur Alam Nur Annisa Syarifuddin Nur Rahman, Ahmad Nur Ramadhan Nur Ramadhan, Nur Nurahmad Nurdiansyah Nurdiansyah Nurfadillah Nurfadillah Nurnawaty Nurul Kusumawardani Nurul Qalbi Rahmania Rahmat Anbiyah Rasyidi, Muhammad Fachri Riswan, Muh. Rizky Yusliana Bakti Rosnani Rosnani Rosnani Rosnani Saharuddin Saharuddin Sarina Sarina Sri Wahyuni Suardi Hi Baharuddin Suriani Suriani Swa Lee Lee Syadiah Nor Wan Shamsuddin SYAFAR, A. MUHAMMAD Syahril Akbar Syahrul Hasbir Syahrul Suhardi Syamsuri, Andi Makbul Syarifuddin, Nur Annisa Titik Khawa Abd Rahman Titik Khawa Abdul Rahman Titin Wahyuni Try Gustaf Said Wahid, Abd Rahman