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All Journal International Journal of Advances in Applied Sciences Bulletin of Electrical Engineering and Informatics PROtek : Jurnal Ilmiah Teknik Elektro Ainet : Jurnal Informatika Indonesian Journal of Data and Science Jurnal Teknik Informatika (JUTIF) Jurnal Algoritma, Logika dan Komputasi Jurnal Informatika Progres Mechatronics Journal in Professional and Entrepreneur (MAPLE) ARUS JURNAL SAINS DAN TEKNOLOGI Journal of Muhammadiyah’s Application Technology
Muhyiddin A.M Hayat
Universitas Muhammadiyah Makassar
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Physics Social Sciences Other

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PENERAPAN ALGORITMA K-NEAREST NEIGHBOR DALAM ANALISIS PEMINJAMAN BARANG PADA DIVISI INVENTARIS TVRI MAKASSAR Risal; Chyquitha Danuputri; Darniati; Muhyiddin AM Hayat
PROGRESS Vol 17 No 2 (2025): September
Publisher : P3M STMIK Profesional Makassar

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Abstract

Inventory management in the TVRI Makassar Inventory Division is inefficient due to the lack of a predictive system, hampering proactive asset requirement planning. This study aims to apply the K-Nearest Neighbor (KNN) algorithm to analyze historical borrowing patterns, predict demand for goods three months in advance, and evaluate model accuracy. Using a quantitative approach, this study implements a systematic machine learning workflow, including data preprocessing, temporal feature engineering, class imbalance handling using the Synthetic Minority Over-sampling Technique (SMOTE), and hyperparameter optimization using GridSearchCV. The results show that the optimized KNN model achieved an overall accuracy of 80.18%, significantly outperforming the baseline model. Key findings revealed that the model's performance is contextual, with very high reliability (F1-Score > 0.95) on frequently borrowed assets, and is able to identify strong temporal demand patterns. It is concluded that KNN is effective for segmented inventory demand prediction and has the potential to serve as a basis for TVRI Makassar to adopt a proactive, datadriven inventory management strategy, enabling more efficient resource allocation.
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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Abstract

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.
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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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.
Web-Based Expert System for Human Skin Disease Diagnosis Using Forward Chaining and Mamdani Fuzzy Inference Muhammad Khadafi; Muhammad Faisal; Muhyiddin AM Hayat; Muh. Arief Muhsin; Muhammad Syafaat S.Kuba; Lukman Anas; Andi Makbul Syamsuri
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 4 (2026): JUTIF Volume 7, Number 4, August 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.4.5676

Abstract

This paper aims to develop a web-based expert system aimed at assisting the general public and parties involved in the health sector, particularly in the field of human skin diseases. This system uses a combination of forward chaining and Mamdani fuzzy inference methods. The knowledge base is created based on observations and interviews with a dermatologist at Bima Regional General Hospital, covering 13 symptoms and 6 types of skin diseases, each equipped with expert confidence weights. Disease candidates are inferred from symptoms selected by the user and rules using forward chaining, while symptom weights are managed through fuzzy logic using the Laravel framework with the PHP language. From 20 test data, a comparison between the results from doctors and the system in an operational web application model shows a compatibility level of 95%. This system is suitable for use by doctors with further review. With this application, it is hoped that it can solve problems experienced by the community, such as limited access to dermatologists and economic problems, which are some of the main reasons why this application was designed and developed.
Co-Authors . Darniati Abd Rahman, Aedah Abrah, Rezkytullah Ahmad Faisal Ahmad Fauzi Saifuddin Akbar DB, Andi Muhammad ALRASYID.S, NUR FUAD Andi Makbul Syamsuri Andi Muhammad Akbar DB arikal khairat Asdar, Muhammad Dasril Bakti, Rizki Yusliana Basyar, Muhammad Yusuf Chyquitha Danuputri Chyquitha Danuputri Danuputri, Chyquitha Danuputri, Chyquitha Danuputri, Chyquitha Darniati Dedi Setiawan Desi Anggraeni Desi Anggreani Djalil, Sony Achmad Dzakki Adam, Ahmad Wildan Fachrim Irhamna Rachman Fahrim Irhmna Rachman Fatimah Azzahra Fauzan Azhari Rahman Fauzi Ahmad Muda FUAD, NUR FUAD ALRASYID.S Hadawina Hadawina Haidul, Haidul Jihan Izzathul Mujidah Khadijah Kusumawardani, Nurul Lukman Lukman Lukman Lukman Anas LUKMAN ANAS Lukman Lukman Lukman Lukman Lukman, Lukman Muh Nur Aqsal Aminullah Muhammad Asygar Faeruddin Muhammad Faisal Muhammad Khadafi Muhammad Syafaat S. Kuba Muhsin, Muh Arief Muliana Nisha, Khairun Nur Alam Nur Annisa Syarifuddin Nurnawaty Nurul Kusumawardani RAHMANIA Rahmania RAMLI, ANDI RAODATUL ADAWIYAH Rasyidi, Muhammad Fachri Reski Abbas Ridwang Ridwang Ridwang Ridwang Ridwang, Ridwang Risal Salsabila, Ismi Sari, Reski Anugrah Sarina Sunarti Suriani Suriani Syahril Akbar Titik Khawa Abdul Rahman Titin Wahyuni Ulil Amri Usman, Ansharullah Patiroi Utama, Prengki Putra Wahid, Abd Rahman