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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| Format: | Article |
| Language: | English |
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Ikatan Ahli Informatika Indonesia
2025-05-01
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| Series: | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
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| Online Access: | https://jurnal.iaii.or.id/index.php/RESTI/article/view/6211 |
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| author | Aulia Afifah Endah Ratna Arumi Maimunah Maimunah Setiya Nugroho |
| author_facet | Aulia Afifah Endah Ratna Arumi Maimunah Maimunah Setiya Nugroho |
| author_sort | Aulia Afifah |
| collection | DOAJ |
| description | 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 |
| format | Article |
| id | doaj-art-4f39fefd62aa4671a6888b02cc3d21ea |
| institution | DOAJ |
| issn | 2580-0760 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Ikatan Ahli Informatika Indonesia |
| record_format | Article |
| series | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| spelling | doaj-art-4f39fefd62aa4671a6888b02cc3d21ea2025-08-20T03:15:47ZengIkatan Ahli Informatika IndonesiaJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)2580-07602025-05-019344445210.29207/resti.v9i3.62116211Comparative Evaluation of Preprocessing Methods for MobileNetV1 and V2 in Waste ClassificationAulia Afifah0Endah Ratna Arumi1Maimunah Maimunah2Setiya Nugroho3Universitas Muhammadiyah MagelangUniversitas Muhammadiyah MagelangUniversitas Muhammadiyah MagelangUniversitas Muhammadiyah MagelangWaste 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 classificationhttps://jurnal.iaii.or.id/index.php/RESTI/article/view/6211wastemobilenetv1mobilenetv2preprocessingwaste classification |
| spellingShingle | Aulia Afifah Endah Ratna Arumi Maimunah Maimunah Setiya Nugroho Comparative Evaluation of Preprocessing Methods for MobileNetV1 and V2 in Waste Classification Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) waste mobilenetv1 mobilenetv2 preprocessing waste classification |
| title | Comparative Evaluation of Preprocessing Methods for MobileNetV1 and V2 in Waste Classification |
| title_full | Comparative Evaluation of Preprocessing Methods for MobileNetV1 and V2 in Waste Classification |
| title_fullStr | Comparative Evaluation of Preprocessing Methods for MobileNetV1 and V2 in Waste Classification |
| title_full_unstemmed | Comparative Evaluation of Preprocessing Methods for MobileNetV1 and V2 in Waste Classification |
| title_short | Comparative Evaluation of Preprocessing Methods for MobileNetV1 and V2 in Waste Classification |
| title_sort | comparative evaluation of preprocessing methods for mobilenetv1 and v2 in waste classification |
| topic | waste mobilenetv1 mobilenetv2 preprocessing waste classification |
| url | https://jurnal.iaii.or.id/index.php/RESTI/article/view/6211 |
| work_keys_str_mv | AT auliaafifah comparativeevaluationofpreprocessingmethodsformobilenetv1andv2inwasteclassification AT endahratnaarumi comparativeevaluationofpreprocessingmethodsformobilenetv1andv2inwasteclassification AT maimunahmaimunah comparativeevaluationofpreprocessingmethodsformobilenetv1andv2inwasteclassification AT setiyanugroho comparativeevaluationofpreprocessingmethodsformobilenetv1andv2inwasteclassification |