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JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat)
Published by Universitas Lambung Mangkurat
ISSN : 25275399     EISSN : 25282514     DOI : http://dx.doi.org/10.20527
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering Engineering
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) is intended as a media for scientific studies on the results of research, thinking and analytical-critical studies regarding research in Systems Engineering, Informatics / Information Technology, Information Management and Information Systems. As part of the spirit of disseminating knowledge from the results of research and thought for service to the wider community and as a reference source for academics in the field of Technology and Information.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 152 Documents
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Edge AI Using MobileNet Architecture for Driver Drowsiness Detection Rafie, Rafi e
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) Vol. 11 No. 1 (2026)
Publisher : Fakultas Teknik Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jtiulm.v11i1.517

Abstract

Driving safety is a crucial issue significantly influenced by the driver's physical condition, where fatigue and drowsiness are major factors causing traffic accidents. This study aims to develop a real-time drowsiness detection system utilizing Edge AI technology based on the MobileNet architecture. This architecture was selected due to its efficiency in performing image classification on resource-constrained devices. The dataset used consists of 4,000 digital images balanced into open-eye and closed-eye classes. The model was trained using the TensorFlow framework and optimized through post-training quantization into the TensorFlow Lite format to reduce model size and inference latency. Performance evaluation was conducted by testing 372 new test images. The results indicate that the balanced model achieved an accuracy rate of 94%. Confusion matrix analysis showed a precision value of 1.000 for the closed-eye class and a recall of 1.000 for the open-eye class, indicating that the system is highly reliable in minimizing detection errors. With processing speeds reaching 10 to 22 Frames Per Second (FPS) on edge devices, this system is proven effective for implementation as a responsive driving safety assistant. Drowsiness detection duration indicator “Closed: 0.32s” represents part of the system logic used to trigger an alert. The system does not immediately activate an alarm during normal blinking, it measures the duration of eye closure. If the duration exceeds a predefined threshold (e.g., >0.30 seconds), an alert is triggered in the form of an audible alarm
Hybrid CNN Feature Extraction and Machine Learning Classification for UAV-Based Vegetation Density Land Cover Mapping in Peatlands Maulidiya, Erika; Sari, Yuslena; Gani, Irham Maulani Abdul; Islami, Achmad Mujaddid; Yunita, Helda
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) Vol. 11 No. 1 (2026)
Publisher : Fakultas Teknik Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jtiulm.v11i1.518

Abstract

Land cover classification from UAV imagery has become increasingly important for environmental monitoring, especially in peatland ecosystems where vegetation density is closely related to land openness, degradation risk, and fire vulnerability. This study proposes a hybrid approach that combines CNN-based feature extraction with machine learning classification for vegetation density-based land cover classification. A total of 3,000 UAV images collected from Block 1 of the Liang Anggang Protected Forest, Banjarbaru, were used in this study and categorized into three classes: bare, moderate, and high vegetation density. The images were preprocessed through cropping, resizing, and labeling prior to feature extraction. ResNet-50 and DenseNet-121 were employed as feature extractors, while ten machine learning classifiers were evaluated, namely CalibratedClassifierCV, SVC, NuSVC, LogisticRegression, PassiveAggressiveClassifier, SGDClassifier, LinearSVC, XGBClassifier, Perceptron, and LGBMClassifier. The results show that ResNet-50 generally outperformed DenseNet-121 as a feature extractor. The best and most balanced performance was achieved by the ResNet-50 + SVC combination, which obtained 84% accuracy, 84% F1-score, 91% precision, 77% recall, and a computation time of 8.58 minutes. Although CalibratedClassifierCV achieved the same accuracy, it required substantially longer processing time. These findings indicate that classification performance is influenced not only by the classifier used, but also by the compatibility between feature representation and classification mechanism. Therefore, the combination of ResNet-50 and SVC is recommended for UAV-based vegetation density land cover classification in peatlands.

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