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All Journal Indonesian Journal of Information System Journal of Information Technology and Computer Engineering IJID (International Journal on Informatics for Development) Abdimasku : Jurnal Pengabdian Masyarakat
Prastyo, Pulung Hendro
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Social Sciences

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Journal : Journal of Information Technology and Computer Engineering

 1 
Optic Cup Segmentation using U-Net Architecture on Retinal Fundus Image Prastyo, Pulung Hendro; Sumi, Amin Siddiq; Nuraini, Annis
JITCE (Journal of Information Technology and Computer Engineering) Vol. 4 No. 02 (2020)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jitce.4.02.105-109.2020

Abstract

Retinal fundus images are used by ophthalmologists to diagnose eye disease, such as glaucoma disease. The diagnosis of glaucoma is done by measuring changes in the cup-to-disc ratio. Segmenting the optic cup helps petrify ophthalmologists calculate the CDR of the retinal fundus image. This study proposed a deep learning approach using U-Net architecture to carry out segmentation task. This proposed method was evaluated on 650 color retinal fundus image. Then, U-Net was configured using 160 epochs, image input size = 128x128, Batch size = 32, optimizer = Adam, and loss function = Binary Cross Entropy. We employed the Dice Coefficient as the evaluator. Besides, the segmentation results were compared to the ground truth images. According to the experimental results, the performance of optic cup segmentation achieved 98.42% for the Dice coefficient and loss of 1,58%. These results implied that our proposed method succeeded in segmenting the optic cup on color retinal fundus images.
A Cardiotocographic Classification using Feature Selection: A comparative Study Prasetyo, Septian Eko; Prastyo, Pulung Hendro; Arti, Shindy
JITCE (Journal of Information Technology and Computer Engineering) Vol. 5 No. 01 (2021)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jitce.5.01.25-32.2021

Abstract

Cardiotocography is a series of inspections to determine the health of the fetus in pregnancy. The inspection process is carried out by recording the baby's heart rate information whether in a healthy condition or contrarily. In addition, uterine contractions are also used to determine the health condition of the fetus. Fetal health is classified into 3 conditions namely normal, suspect, and pathological. This paper was performed to compare a classification algorithm for diagnosing the result of the cardiotocographic inspection. An experimental scheme is performed using feature selection and not using it. CFS Subset Evaluation, Info Gain, and Chi-Square are used to select the best feature which correlated to each other. The data set was obtained from the UCI Machine Learning repository available freely. To find out the performance of the classification algorithm, this study uses an evaluation matrix of precision, Recall, F-Measure, MCC, ROC, PRC, and Accuracy. The results showed that all algorithms can provide fairly good classification. However, the combination of the Random Forest algorithm and the Info Gain Feature Selection gives the best results with an accuracy of 93.74%.
Begal-Detector: A Real-Time Street Crime Detection Framework Combining Human Activity Recognition and Object Detection on Raspberry Pi Prastyo, Pulung Hendro; Agung, Ismi Batari; Ikram, Ahmad Fadahil; Pratama, Muhammad Herlan; Mandasari, Nia
JITCE (Journal of Information Technology and Computer Engineering) Vol. 9 No. 2 (2025): Journal of Information Technology and Computer Engineering
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Currently, street crime remains a serious challenge in Indonesia, while conventional CCTV systems still function passively as recorders. One of the most concerning types of crime is robbery with violence, commonly known in Indonesia as begal, which remains among the most frequently reported cases. This study proposes the Begal-Detector, a YOLOv8-based system that integrates Human Activity Recognition (HAR) and Object Detection to identify suspicious activities in real time on edge devices. The experiments were conducted on Raspberry Pi 4, Raspberry Pi 5, and Raspberry Pi 5 with Hailo AI Kit, with variations in distance, camera angle, and lighting conditions. The test dataset consisted of 72 video samples, including both street crime and non-street crime scenarios, recorded using the EZVIZ H8C Outdoor CCTV camera. Experimental results show that the Begal-Detector performs very well, achieving a 100% detection accuracy at a distance of 2 meters, 94% at 3 meters, and 94% at a 45° camera angle. Under low-light conditions supported by infrared light, the system maintained an accuracy of up to 79%, making it feasible for real-world deployment. In terms of hardware performance, the Raspberry Pi 5 with Hailo AI Kit provided the most optimal results, achieving an average of 52.71 FPS with a stable temperature of 63 °C, significantly outperforming the Raspberry Pi 4 and Raspberry Pi 5 without an accelerator, both of which failed to operate the system in real time. The findings confirm that utilizing Raspberry Pi 5 with Hailo AI Kit is an effective solution to ensure that the Begal-Detector operates quickly, stably, and reliably.
Co-Authors Agung, Ismi Batari Amiruddin, M. Rudini Kurniawan Andi Asvin Mahersatillah Suradi ARDIANSYAH ARDIANSYAH Arti, Shindy Budy Santoso Ikram, Ahmad Fadahil Mandasari, Nia MUHAMMAD RIZAL Nuraini, Annis Prasetyo, Septian Eko Pratama, Muhammad Herlan Saputra, Wahyuddin Sri Suning Kusumawardani Sumi, Amin Siddiq