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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
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IMPLEMENTASI HYBRID LEXICON-BASED DAN SVM UNTUK KLASIFIKASI ANALISIS SENTIMEN TERHADAP PELATIHAN BBPSDMP KOMINFO MAKASSAR Nur Alam; Muhammad Faisal; Rizki Yusliana Bakti; Muhammad Syafaat; Andi Makbul Syamsuri; Muhyiddin AM Hayat; Andi Lukman Anas
PROGRESS Vol 17 No 2 (2025): September
Publisher : P3M STMIK Profesional Makassar

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Abstract

The evaluation of government training programs is often hindered by manual analysis of unstructured qualitative feedback, making the process inefficient and subjective. This study aims to implement and evaluate a sentiment classification model using a hybrid Lexicon-Based and Support Vector Machine approach to analyze participants’ perceptions of the Vocational School Graduate Academy training organized by BBPSDMP Kominfo Makassar, as well as to compare the performance of a standard SVM model with a model optimized using Particle Swarm Optimization. This quantitative research employs 2,313 unstructured review data, which undergo text preprocessing, initial lexicon-based labeling, and TF-IDF feature extraction before being classified using an SVM with an RBF kernel. The results show that the SVM model optimized with PSO consistently outperforms the standard model across all four evaluation aspects, with the most significant accuracy improvement observed in the instructor category from 84.71% to 89.02% and in the assessor category reaching 91.46%. PSO optimization has proven effective in enhancing the model’s ability to identify negative sentiments, which represent the minority class. The hybrid approach with PSO optimization is capable of producing a more accurate and balanced classification system, with practical implications as an objective automated evaluation tool.
IMPLEMENTASI HYBRID CNN, FACIAL LANDMARK DAN LIVENESS DETECTION PADA SISTEM ABSENSI WAJAH Andi Muhammad Akbar DB; Muhammad Faisal; Muhyiddin AM Hayat
PROGRESS Vol 17 No 2 (2025): September
Publisher : P3M STMIK Profesional Makassar

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This paper presents the implementation of a hybrid approach for face recognition attendance systems, combining Convolutional Neural Network (CNN), facial landmark detection, and liveness detection. The CNN model extracts facial features for identity recognition, while facial landmark detection captures dynamic movements such as eye blinking and mouth motion. Liveness detection ensures system robustness against spoofing attempts including photo and video replay. The system was developed using Python with OpenCV, MediaPipe, and TensorFlow, and tested under multiple spoofing scenarios. Results show a detection accuracy of 95.5%, with real-time performance and resilience against common spoofing threats.
IMPLEMENTASI SISTEM DETEKSI PRODUK BOIKOT BERBASIS WEBSITE REAL-TIME MENGGUNAKAN METODE YOLOv10 Ahmad Nur Rahman; Emil Agusalim Habi Talib; Fahrim Irhamna Rachman; Rizki Yusliana Bakti; Muhammad Faisal; Muhammad Syafaat S.Kuba
PROGRESS Vol 18 No 1 (2026): April
Publisher : P3M STMIK Profesional Makassar

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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.
KLASIFIKASI TANAMAN OBAT TRADISIONAL BERBASIS CITRA BUAH DAN DAUN Nurul Kusumawardani; Chyquitha Danuputri; Darniati; Muhammad Faisal; Muhyiddin A.M Hayat; Muhammad Syafaat S.Kuba; Desi Anggreani
PROGRESS Vol 18 No 1 (2026): April
Publisher : P3M STMIK Profesional Makassar

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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.
PERBANDINGAN CNN DAN YOLO PADA SISTEM PENGENALAN WAJAH BERBASIS PRESENSI Nurfadillah; Ida; Darniati; Rizki Yusliana Bakti; Titin Wahyuni; Muhammad Faisal
PROGRESS Vol 18 No 1 (2026): April
Publisher : P3M STMIK Profesional Makassar

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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.
PENERAPAN ALGORITMA MOBILENETV2 UNTUK KLASIFIKASI HURUF HIJAIYAH BERBASIS GESTUR TANGAN Muh. Riswan; Titin Wahyuni; Chyquitha Danuputri; Emil Agusalim Habi Talib; Muhammad Faisal; Lukman Anas; Andi Agung
PROGRESS Vol 18 No 1 (2026): April
Publisher : P3M STMIK Profesional Makassar

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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.
PENERAPAN MODEL ESRGAN UNTUK UPSCALING CITRA DAN VIDEO DIGITAL Syahrul Suhardi; Emil Agusalim Habi Talib; Fahrim Irhamna Rachman; Titin Wahyuni; Muhammad Faisal; Muhammad Syafaat S.Kuba
PROGRESS Vol 18 No 1 (2026): April
Publisher : P3M STMIK Profesional Makassar

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Low-resolution images and videos remain a common problem in various digital applications due to limited visual quality. Conventional interpolation-based upscaling methods often produce blurry results and lead to the loss of important texture details. This study aims to apply the Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) to improve the resolution of digital images and videos. The dataset used consists of low-resolution images and videos that are processed through preprocessing, model training, and testing stages using the Google Colab environment. The ESRGAN model is trained to generate high-resolution images while preserving visual details and structural information. Model performance is evaluated using the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and visual comparison between images before and after the upscaling process. The results show that ESRGAN significantly improves the quality of images and videos compared to conventional interpolation methods, both quantitatively and qualitatively. Therefore, the application of ESRGAN is considered effective for enhancing the resolution of digital images and videos and can be utilized in applications that require high visual quality.
MONITORING DAN NOTIFIKASI REAL-TIME PERUBAHAN FILE PADA WEB SERVER MENGGUNAKAN WATCHDOG DAN TELEGRAM BOT SEBAGAI SISTEM PERINGATAN DINI Syahrul Hasbir; Emil Agusalim Habi Talib; Fahrim Irhamna Rachman; Titin Wahyuni; Muhammad Faisal; Muhammad Syafaat S.Kuba
PROGRESS Vol 18 No 1 (2026): April
Publisher : P3M STMIK Profesional Makassar

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Abstract

Web servers are critical infrastructures for delivering digital services and are highly vulnerable to unauthorized file changes that may threaten system security and service availability. However, many conventional monitoring systems still rely on periodic checking mechanisms, which often fail to provide timely detection of security incidents. This study aims to design and implement a real-time file change monitoring system on a web server using the Watchdog library and a Telegram Bot as an early warning mechanism. The research adopts an applied research method with an experimental approach. The system is developed using the Python programming language and evaluated in a local XAMPP-based web server environment, with the uploads directory selected as the monitoring target. Experimental results demonstrate that the proposed system is capable of detecting various file change events, including file creation, deletion, content modification, and file renaming, in real time without event loss. Notifications delivered via the Telegram Bot provide clear, timely, and actionable information to administrators. These findings indicate that the proposed event-driven monitoring system is effective and efficient in enhancing web server security and improving incident response capabilities.
A Hybrid Salp Swarm Optimization and Behavioral Nudge Framework for Optimizing Software Developer Task Allocation Ashabul Kahfi; Muhammad Faisal; Titin Wahyuni; Desi Anggreani; Darniati Darniati; Muhammad Syafaat S Kuba; Andi Makbul Syamsuri; Ida Mulyadi
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 15 No. 2 (2026)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v15i2.106920

Abstract

Effective task allocation is critical in Agile software development, yet most optimization-based approaches treat it as a purely technical scheduling problem and disregard behavioral factors such as motivation, fairness, and engagement. This study proposes a Hybrid Salp Swarm Optimization–Behavioral Nudge Framework (HSSO–BNF) for developer–task allocation that integrates technical constraints with human-centered cues. The model formulates allocation as a multi-objective function combining workload balance, skill mismatch, deadline penalties, and a motivation score derived from three nudge components: Motivational Cue (MC), Social Comparison (SC), and Effort–Reward Feedback (ERF). These behavioral signals are embedded directly into the SSO position update and fitness evaluation, enabling the swarm to adapt simultaneously to performance and motivational states. Experiments on real developer–task records collected from GitHub compare HSSO–BNF against GA, PSO, and standard SSO using convergence behavior, allocation cost, fairness, satisfaction, and motivation dynamics. The results show that HSSO–BNF achieves faster and more stable convergence, reduces allocation cost by approximately 32% compared with GA and SSO and about 25% compared with PSO, and improves workload fairness and developer satisfaction while preserving psychologically sustainable specialization patterns. Heatmap visualizations and motivation trends further confirm that the behavioral layer produces more coherent and interpretable task assignments, indicating that behavior-aware metaheuristics are a promising direction for intelligent, human-centered task allocation in Agile teams.
Analisis Kontribusi Deteksi Tangan dan Ekstraksi Landmark untuk Pengenalan Alfabet BISINDO Wa Nanda Sulystrian; Muhammad Faisal; Fahrim Irhamna Rachman; Abd Rakhim Nanda; Rizki Yusliana Bakti; Muhammad Syafaat S. Kuba; Andi Makbul Syamsuri
Buletin Sistem Informasi dan Teknologi Islam (BUSITI) Vol 7, No 3 (2026)
Publisher : Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/busiti.v7i3.3506

Abstract

Komunitas tunarungu menghadapi hambatan dalam aspek bahasa dan komunikasi sehingga membutuhkan dukungan teknologi yang sesuai dengan karakteristik visual mereka. Kondisi tersebut menunjukkan pentingnya pengembangan teknologi untuk mendukung akses komunikasi yang lebih inklusif melalui pengenalan Bahasa Isyarat Indonesia (BISINDO). Penelitian ini menganalisis kontribusi deteksi tangan dan ekstraksi landmark terhadap performa pengenalan alfabet BISINDO menggunakan pendekatan berbasis computer vision. Sistem yang diusulkan mengintegrasikan YOLOv8 untuk deteksi tangan, MediaPipe Hands untuk ekstraksi dua puluh satu landmark, normalisasi landmark, serta Multi-Layer Perceptron sebagai model klasifikasi. Evaluasi dilakukan menggunakan dataset berisi 1066 citra alfabet BISINDO dari 26 kelas melalui tiga skenario eksperimen. Hasil penelitian menunjukkan bahwa kombinasi deteksi tangan dan normalisasi landmark menghasilkan performa terbaik dengan nilai accuracy 0.915, precision 0.913, recall 0.915, dan F1-score 0.896, serta meningkatkan accuracy 14.8% dibandingkan pendekatan tanpa deteksi tangan. Temuan ini menunjukkan bahwa deteksi tangan dan representasi landmark berkontribusi penting terhadap peningkatan akurasi sistem. Pendekatan yang diusulkan berpotensi diterapkan pada aplikasi penerjemah alfabet BISINDO berbasis kamera secara real-time untuk mendukung komunikasi yang lebih inklusif bagi komunitas tunarungu di Indonesia.
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