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The Telematika, with registered number ISSN 2442-4528 (online) ISSN 1979-925X (print) is a scientific journal published by Universitas Amikom Purwokerto. The journal registered in the CrossRef system with Digital Object Identifier (DOI) prefix 10.35671/telematika. The aim of this journal publication is to disseminate the conceptual thoughts or ideas and research results that have been achieved in the area of Information Technology and Computer Science. Every article that goes to the editorial staff will be selected through Initial Review processes by the Editorial Board. Then, the articles will be sent to the Mitra Bebestari/ peer reviewer and will go to the next selection by Double-Blind Preview Process. After that, the articles will be returned to the authors to revise. These processes take a month for a minimum time. In each manuscript, Mitra Bebestari/ peer reviewer will be rated from the substantial and technical aspects. The final decision of articles acceptance will be made by Editors according to Reviewers comments. Mitra Bebestari/ peer reviewer that collaboration with The Telematika is the experts in the Information Technology and Computer Science area and issues around it.
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Telematika
Published by Universitas Amikom Purwokerto
ISSN : 1979925X     EISSN : 24424528     DOI : 10.35671/telematika
Core Subject : Education,
Education
Jl. Letjend Pol. Soemarto No.126, Watumas, Purwanegara, Kec. Purwokerto Utara, Kabupaten Banyumas, Jawa Tengah 53127
Arjuna Subject : -
Articles 262 Documents
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Development of a Multi-point IoT Based Ship Draft Monitoring System for Barge Mounted Power Plants Elisabeth Tansiana Mbitu; Marceau A. F. Haurissa; Fatima Zahra
Telematika Vol 19, No 2: August (2026)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v19i2.3414

Abstract

Continuous monitoring of ship draft is essential for maintaining vessel stability, operational safetakay, and fuel distribution efficiency, particularly in Barge Mounted Power Plants (BMPP), where load variations directly influence the vessel’s balance. At the PLTMG BMPP Nusantara-1, draft measurements are currently performed through manual visual inspection of draft marks, making the process susceptible to human error and unsuitable for continuous real time monitoring. This study proposes the development of a multipoint Internet of Things (IoT) based ship draft monitoring system employing six waterproof JSN-SR04T ultrasonic sensors integrated with an Arduino Mega, ESP32 communication module, RTC DS3231, and Arduino IoT Cloud platform. The proposed system continuously measures draft values at the port and starboard sides along the bow, midship, and stern, enabling comprehensive monitoring of vessel trim and heel conditions. Measurement data are transmitted in real time to a cloud dashboard and automatically stored in Google Sheets for historical data logging. Ten latency trials produced a mean end to end response time of 30.00 s. In addition, the system incorporates an automatic warning mechanism that detects overdraft, minimum draft, and vessel imbalance conditions based on predefined operational thresholds. Experimental evaluation using a laboratory scale prototype of the BMPP Nusantara-1 demonstrates that the proposed system successfully performs continuous draft monitoring, provides real time visualization with an observed 30 s response interval, records historical measurement data, and generates timely warning notifications under various operating scenarios, including overdraft, heel, and minimum draft conditions. The developed system offers a practical IoT based solution for improving operational safety and supporting digital monitoring of floating power plants.
Robustness Evaluation of YOLOv8, YOLOv11, and YOLOv12 for Personal Protective Equipment Detection under Photometric Saturation Variations Zaid Romegar Mair; Rudi Heriansyah; Septa Cahyani; M. Ravensky Taro Danayaksa
Telematika Vol 19, No 2: August (2026)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v19i2.3362

Abstract

Computer vision-based Personal Protective Equipment (PPE) detection has become increasingly important for improving Occupational Safety and Health (OSH) compliance through automated, real-time monitoring of workers' safety practices. Although recent studies have reported promising performance for YOLO-based object detection models, most evaluations have been conducted under standard imaging conditions, providing limited evidence of model robustness against photometric variations. To address this gap, this study proposes a systematic robustness evaluation framework based on controlled photometric saturation variations to compare the performance stability of three state-of-the-art object detection models: YOLOv8, YOLOv11, and YOLOv12, for multi-class PPE detection. The experimental dataset comprised 3,116 images annotated into eight PPE-related classes: Helmet On, No Helmet, Vest On, No Vest, Gloves On, No Gloves, Boots On, and No Boots. To ensure a fair comparison, all models were trained using identical experimental settings and evaluated using Precision, Recall, mAP@50, and mAP@50–95. Model robustness was assessed under three saturation conditions (−30%, 0%, and +30%), representing realistic color variations commonly encountered in construction-site surveillance. The experimental results revealed that photometric saturation variations produced only marginal changes in detection performance across all evaluated models. Among the three architectures, YOLOv8 achieved the highest overall performance, attaining an mAP@50 of 55.1%, compared with 52.9% for YOLOv11 and 48.6% for YOLOv12, while maintaining the most stable performance under varying saturation levels. Although YOLOv12 demonstrated relatively better capability for detecting several small-object classes, its overall detection performance remained inferior to that of YOLOv8. These findings indicate that YOLOv8 provides the best trade-off between detection accuracy and robustness under moderate photometric saturation variations. This study contributes a systematic robustness evaluation of recent YOLO architectures under controlled photometric conditions and offers practical insights for selecting reliable object detection models for real-world PPE monitoring systems.

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