Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Jurnal Teknik Informatika C.I.T. Medicom International Journal of Enterprise Modelling
A, Galih Prakoso Rizky
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Transportation

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Real Time Pill Counting on Low Power Device: A YOLOv5 Pipeline with Confidence Thresholding and NMS A, Galih Prakoso Rizky; Widyastuti, Rifka
Jurnal Teknik Informatika C.I.T Medicom Vol 17 No 5 (2025): Intelligent Decision Support System (IDSS)- INPRESS
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol17.2025.1286.pp225-241

Abstract

Manual pill counting is still commonly performed in healthcare facilities and pharmacies, but this method is vulnerable to human error and requires significant processing time. This study develops an automatic pill counting pipeline using the YOLOv5 deep learning model, optimized for low-power devices such as Raspberry Pi, Orange Pi, and Jetson Nano. Unlike earlier techniques that depend on conventional retrieval or machine-learning approaches, this pipeline integrates real-time object detection with customized confidence thresholding and Non-Maximum Suppression (NMS), enabling high accuracy and fast performance on edge hardware with limited resources. The development process includes collecting and annotating a dataset of pill images with variations in shape, color, and orientation, followed by training YOLOv5 using optimized parameters. A simple webcam is used as the input device, and system performance is evaluated under different lighting and background conditions. Experimental results show that the model achieves 98% precision, 88% recall, 95% mAP@0.5, and 67% mAP@0.5:0.95, with an average inference speed of around 15 milliseconds per image. Tests on ten pill-counting scenarios under optimal lighting demonstrate strong performance, with only minor discrepancies in dense cases involving 50 and 127 pills, producing accuracies of 98% and 99.21%. These results indicate that the optimized YOLOv5 pipeline provides fast and accurate real-time pill counting on low-power devices. Future work will enhance robustness to lighting variations, validate using external datasets, and incorporate color and shape feature analysis to improve performance in challenging scenarios.
Adaptive Scheduling Model of Ultrasonic Frequencies Based on Environmental Data for Rice Field Rat Pest Control Sihotang, Hengki Tamando; A, Galih Prakoso Rizky; Sihotang, Jonhariono; Simbolon, Romasinta
International Journal of Enterprise Modelling Vol. 19 No. 3 (2025): September: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/int.jo.emod.v19i3.164

Abstract

Rat infestation remains a major constraint to rice production, causing significant yield losses and threatening food security in many rice-growing regions. Although ultrasonic deterrent systems have been promoted as an environmentally friendly alternative to chemical rodenticides, their effectiveness is often inconsistent due to static frequency emission and rapid behavioral habituation. This study proposes an adaptive scheduling model for ultrasonic frequencies based on real-time environmental data to enhance long-term deterrence effectiveness. The model integrates environmental sensing, stochastic frequency selection, and habituation-aware control within a context-aware scheduling framework. Environmental data were acquired using field-deployed sensors, while the adaptive algorithm dynamically adjusted ultrasonic frequency, emission duration, and interval. Field evaluations compared the proposed system with static ultrasonic control. Results demonstrate sustained spectral diversity, reduced habituation, and significant decreases in rat activity and crop damage, alongside improved energy efficiency. These findings highlight the potential of adaptive ultrasonic control as a scalable and sustainable solution for smart agriculture, supporting chemical-free pest management and precision rice farming.
Co-Authors Hengki Tamando Sihotang Rifka Widyastuti, Rifka Sihotang, Jonhariono Simbolon, Romasinta