Comparative Evaluation of Preprocessing Methods for MobileNetV1 and V2 in Waste Classification

Waste management remains a critical challenge for many countries, including Indonesia, which ranks as the world's second-largest contributor of waste. As tens of millions of tons are produced each year and the management system remains ineffective, environmental conditions and public health con...

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Published inJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) (Online) Vol. 9; no. 3; pp. 444 - 452
Main Authors Aulia Afifah, Arumi, Endah Ratna, Maimunah, Maimunah, Setiya Nugroho
Format Journal Article
LanguageEnglish
Published Ikatan Ahli Informatika Indonesia 01.06.2025
Subjects
mobilenetv1
mobilenetv2
preprocessing
waste
waste classification
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ISSN2580-0760
2580-0760
DOI10.29207/resti.v9i3.6211

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Summary:Waste management remains a critical challenge for many countries, including Indonesia, which ranks as the world's second-largest contributor of waste. As tens of millions of tons are produced each year and the management system remains ineffective, environmental conditions and public health continue to deteriorate. To address this issue, it is imperative to develop more accurate and efficient solutions to enhance waste classification and management. This study investigates the influence of various image preprocessing techniques on the performance of MobileNetV1 and MobileNetV2 models in the classification of waste images. Preprocessing is crucial for enhancing data quality, particularly when dealing with real-world images that are affected by inconsistent lighting, texture, and clarity. Five preprocessing scenarios were evaluated: Baseline, CLAHE with Bilateral Filtering, CLAHE with Sharpening, Grayscale with CLAHE, and Gaussian Blur with Bilateral Filtering. Among these, the combination of CLAHE and Bilateral Filtering applied to MobileNetV1 achieved the best results, with 85% training accuracy, 96% validation accuracy, a training loss of 0.3178, and the lowest validation loss of 0.1630. Overall, MobileNetV1 benefited more significantly from preprocessing variations than MobileNetV2, particularly in terms of accuracy improvement and reduction in prediction error. These findings underscore the importance of effective preprocessing in enhancing model performance for waste image classification
ISSN:2580-0760
2580-0760
DOI:10.29207/resti.v9i3.6211