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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Ilmiah Teknologi dan Rekayasa UG Journal Sebatik Journal of System and Computer Engineering
Cahyanti, Margi
Universitas Gunadarma
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology

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Comparison of YOLOv7 and YOLOv8 Architectures for Detecting Shirt Collars Danyalson, Calvin; Cahyanti, Margi; Swedia, Ericks Rachmat; Sarjono, Mochammad Wisuda
Sebatik Vol. 28 No. 2 (2024): December 2024
Publisher : STMIK Widya Cipta Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46984/sebatik.v28i2.2492

Abstract

The shirt collar is one of the primary aspects monitored during online examinations in the postgraduate program at Gunadarma University. Examinees are required to wear formal, collared attire. Based on these regulations, a study was conducted to develop a collar detection method to facilitate the online exam monitoring process. This research involves a comparative analysis of two detection architectures: You Only Look Once (YOLO) version 7 (YOLOv7) and version 8 (YOLOv8), to determine the most effective architecture for detecting shirt collars using the dataset provided in the study. Detection models developed from both architectures were implemented in a web-based application and tested to evaluate their accuracy and efficiency. The testing results showed that YOLOv7 achieved an average accuracy of 95%, outperforming YOLOv8, which had an average accuracy of 75%. However, despite YOLOv8's lower accuracy, it excelled in detection speed, with an average processing time of 2.27 seconds, significantly faster than YOLOv7's average processing time of 22.42 seconds. Considering both accuracy and speed, YOLOv7 demonstrated the best overall performance in this study. Nonetheless, it is possible that YOLOv8 could surpass YOLOv7 in the future if significant improvements are made to its detection accuracy.
A Real-Time Helmet Detection System Based on YOLOv8 to Support Traffic Law Enforcement Puspita, Tiara; Swedia, Ericks Rachmat; Cahyanti, Margi; Septian, M Ridwan Dwi
Sebatik Vol. 29 No. 1 (2025): June 2025
Publisher : STMIK Widya Cipta Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46984/sebatik.v29i1.2585

Abstract

Helmet use is a critical safety measure for motorcycle riders, yet non-compliance remains high in Indonesia. This study introduces a real-time helmet detection system using the YOLOv8 architecture, deployed on Android devices with the Kotlin programming language. A dataset of 1,197 digital images was collected and annotated using Roboflow Annotate, containing two classes: helmet users (True) and non-users (False). To improve model generalization, data augmentation techniques such as rotation and shear were applied. The model was trained using the pretrained yolov8n.pt weights and evaluated based on mAP and Intersection over Union (IoU). During training, the model achieved a mAP50 of 98% and a mAP50–95 of 59.6%. In testing, the mAP50 reached 98.3% and mAP50–95 reached 61%, with an average IoU of 0.73. The trained model was then converted into TensorFlow Lite format and integrated into an Android application. Real-time testing showed a detection accuracy of 93.3%. These results demonstrate that YOLOv8 is effective for mobile-based real-time helmet detection and has strong potential to support traffic law enforcement systems, especially in urban environments where manual monitoring is inefficient. The system contributes to enhancing public safety through smart technology integration.
Co-Authors Afif, Anggara Fajri Andi Asrafil Ardan Paliwang Danyalson, Calvin Ericks Rachmat Swedia Farhan Adiandoro M Ridwan Dwi Septian Moch Wisuda Sardjono Mochamad Wisuda Sardjono Muhamad Jaelani Akbar Muhammad Andri Imawan Mujahidin, Maulana Puspita, Tiara Ramadhan, Valdy Ravi A Salim Rini Arianty Sardjono, Mochamad Wisuda Sarjono, Mochammad Wisuda Septian, M Ridwan Dwi Swedia, Ericks Rahmat