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All Journal Jurnal Teknik Informatika C.I.T. Medicom International Journal of Enterprise Modelling
A, Galih Prakoso Rizky
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Transportation

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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): November : Intelligent Decision Support System (IDSS)
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.
Dynamic Modeling of Service Quality Improvement through Feedback-Based Governance Alrasyid, Wildan; A, Galih Prakoso Rizky; Dermawan, Budi Arif; Rasenda, Rasenda
International Journal of Enterprise Modelling Vol. 20 No. 1 (2026): January: Publishing Process (Inpress)
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/int.jo.emod.v20i1.144

Abstract

This study aims to develop a dynamic system model designed to improve service quality and reduce the gap between the expected and actual levels of community satisfaction. The study is motivated by the importance of understanding the interaction between key variables such as human resources (HR), information and communication technology (ICT), and evaluation mechanisms within a feedback-based service system. The system dynamics method is employed to construct causal loop and stock–flow models to simulate their impacts over a 12-month period. The simulation results show that strengthening HR, ICT, and evaluation aspects leads to a significant improvement in service quality, which directly increases community satisfaction. In addition, the gap between actual and targeted satisfaction shows a decreasing trend over time. Community feedback emerges as a crucial factor that accelerates performance improvement through reinforcing loops. Graphical and tabular representations support the positive dynamics of the main variables and demonstrate the system’s flexibility in responding to internal changes. This study demonstrates that dynamic system modeling is an effective approach to managing complex service systems. By illustrating nonlinear interactions among variables and considering time delays, decision-makers can simulate policy impacts before implementation. The study concludes that feedback-based models can serve as useful tools for simultaneously improving public services and reducing performance gaps.
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 Alrasyid, Wildan Budi Arif Dermawan Rasenda, Rasenda Rifka Widyastuti, Rifka Sihotang, Hengki Tamando Sihotang, Jonhariono Simbolon, Romasinta