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All Journal Indonesian Journal of Data and Science
Zulkarnain , Ismail Abdurrozzaq
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Computer Science & IT Decision Sciences, Operations Research & Management Mathematics

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Automated Waste Image Classification with Weighted Scoring Using MobileNetV2 on the OLSAM Platform Anggraeni, Kartika Nur; Astuti, Arin Yuli; Zulkarnain , Ismail Abdurrozzaq
Indonesian Journal of Data and Science Vol. 6 No. 3 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i3.295

Abstract

This study presents the development of an automated waste image classification system for the OLSAM platform to enhance community participation in waste management. The objective is to integrate a lightweight CNN-based classifier with a weighted point calculation mechanism for five waste categories. A dataset of 1,500 images was used, split into 80% training, 10% validation, and 10% testing. The MobileNetV2 architecture was applied to perform image classification, while a weighted reward mechanism assigned points based on the detected waste type and its weight. The model achieved its best performance at epoch 65, reaching an accuracy of 96.67% and a weighted F1-score of 0.97. These results indicate that combining CNN-based recognition with a weighted point system effectively supports user engagement and promotes sustainable waste-sorting behavior within community waste management systems.
Co-Authors Anggraeni, Kartika Nur Astuti, Arin Yuli