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All Journal EDUMATIC: Jurnal Pendidikan Informatika Indonesian Journal of Innovation Studies
Muhtadi, Muis
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Computer Science & IT Education Engineering Law, Crime, Criminology & Criminal Justice

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Journal : EDUMATIC: Jurnal Pendidikan Informatika

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Optimasi Deteksi Objek pada Video dengan Kompresi Region of Interest menggunakan Model YOLOv8 Assagaf, Azhryl; Muhtadi, Muis
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 2 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i2.30007

Abstract

The demand for real-time object detection systems, such as those used in video surveillance and autonomous vehicles, drives the need for efficient data storage and transmission without compromising accuracy. One promising approach is Region of Interest (ROI)-based video compression, which preserves visual quality in important areas. This study aims to evaluate the impact of video compression on object detection accuracy using the YOLOv8 model through statistical analysis using Analysis of Variance (ANOVA), and to compare the effectiveness of uniform and ROI-based compression methods. Videos from the VIRAT Video Dataset were compressed using the Constant Rate Factor (CRF) parameter and evaluated based on mAP_50, mAP_50_95, and file size. ANOVA results indicate no statistically significant differences between the two methods. At CRF 50, file size can be reduced by over 60%, but mAP_50 accuracy drops below 50% due to quality degradation in non-ROI areas, which disrupts the spatial context required by the model. This study contributes by examining the compression tolerance limits of YOLOv8 and reveals that overall visual quality, rather than just object-focused quality, plays a crucial role in model performance. These findings have important implications for real-time applications such as CCTV and autonomous vehicles, where a balance between compression efficiency and detection accuracy is critical. Future studies may explore adaptive ROI approaches that consider dynamic object movement.
Sleeption: Aplikasi Deteksi Gerakan Video Real-time berbasis Mobile untuk Pemantauan Gangguan Tidur Bayi Andreanto, Dodik Dwi Andreanto; Muhtadi, Muis; Ariyandi, Haffas Zikri
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 2 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i2.30373

Abstract

A baby's quality sleep is very important for his or her growth and development. Infant sleep disturbances have a negative impact on their physical health. Monitoring baby's sleep becomes very important to identify potential sleep disorders and ensure optimal sleep quality for babies. This research aims to build an android application called Sleeption using the waterfall model, a mobile-based baby sleep pattern monitoring application connected to a Raspberry Pi 3 device as an edge computing device. The development phase includes the stages of analysing the needs of parents in monitoring activities, application design, implementation of application design, verification of the success of features in the application, application and application testing along with periodic maintenance on the application. Data collection is done by observation with the development team on the needs of parents in conducting baby sleep monitoring activities. The result of our findings is a mobile application that helps parents in the process of monitoring their baby's sleep activities. The results of black box testing show that the Sleeption application is able to provide accurate and real-time information with a latency of 41 ms about the baby's sleep activity. The application of the Sleeption application helps parents in monitoring their baby's sleep patterns, along with the automation of reports on changes in baby sleep patterns helping parents to identify potential sleep disorders more accurately.
Metode Frame Difference untuk Deteksi Gerakan Tidur Bayi berbasis Computer Vision Ariyandi, Haffas Zikri; Muhtadi, Muis; Andreanto, Dodik Dwi
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 1 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i1.29004

Abstract

Monitoring a baby's sleep is a critical task for parents, especially when balancing household responsibilities. This study combines the MobileNet-SSD object detection model with the Frame Difference method to analyze sleep movements based on motion thresholds. The system's performance was evaluated by calculating accuracy, precision, recall, and latency, implemented on both laptop and Raspberry Pi devices, and tested using 720p and 480p resolution videos. Results showed accuracy of 82%, precision of 81%, and recall of 92% at 720p, and accuracy of 77%, precision of 80%, and recall of 86% at 480p. However, the Raspberry Pi exhibited a latency of 400ms, 10 times higher than the laptop's 41.28ms latency. Compared to optical flow, this method offers ease of use, and lower computational complexity. The results of this study highlight the impact of resolution on motion detection accuracy, where higher-resolution videos yield more optimal performance. Limitations under low-light conditions suggest potential improvements using deep learning techniques like YOLO and Mediapipe to detect eye conditions. This research contributes to the development of computer vision where the frame differential and object detection methods are proven to provide a fairly high level of accuracy in detecting movement.
Co-Authors Andreanto, Dodik Dwi Andreanto, Dodik Dwi Andreanto Ariyandi, Haffas Zikri Assagaf, Azhryl Rafi, Muhammad Zulfiqar Ramadan, Nofri Waluyo, Rinakit Estu