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This Author published in this journals
All Journal Journal of Information Technology and Computer Engineering
Rian Ferdian
Computer Engineering Departmen, Universitas Andalas
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Food Classification and Monitoring System in Refrigerators Using YOLO Algorithm Laellatul Husna; Rian Ferdian; Yoan Purbolingga
JITCE (Journal of Information Technology and Computer Engineering) Vol. 9 No. 2 (2025): Journal of Information Technology and Computer Engineering
Publisher : Universitas Andalas

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Abstract

Monitoring food freshness in refrigerators remains a challenge for many users, often leading to food spoilage and waste due to the absence of an automatic monitoring system. This study proposes a computer vision–based food monitoring system that leverages the YOLOv5 algorithm to automatically detect and categorize food items through camera input and deliver real-time notifications to users via a connected application. Experimental results demonstrate that YOLOv5 achieves an average accuracy of over 90% across various distances and object positions. Despite challenges related to limited datasets and lighting variations inside the refrigerator, the system offers a practical and innovative solution to reduce food spoilage, minimize household food waste, and support more efficient food storage management.
Food Classification and Monitoring System in Refrigerators Using YOLO Algorithm Laellatul Husna; Rian Ferdian; Yoan Purbolingga
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

Monitoring food freshness in refrigerators remains a challenge for many users, often leading to food spoilage and waste due to the absence of an automatic monitoring system. This study proposes a computer vision–based food monitoring system that leverages the YOLOv5 algorithm to automatically detect and categorize food items through camera input and deliver real-time notifications to users via a connected application. Experimental results demonstrate that YOLOv5 achieves an average accuracy of over 90% across various distances and object positions. Despite challenges related to limited datasets and lighting variations inside the refrigerator, the system offers a practical and innovative solution to reduce food spoilage, minimize household food waste, and support more efficient food storage management.
Co-Authors Laellatul Husna Yoan Purbolingga