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All Journal Telematika Sistemasi: Jurnal Sistem Informasi Jurnal Informatika IJCIT (Indonesian Journal on Computer and Information Technology) Indonesian Journal on Software Engineering (IJSE) Perspektif : Jurnal Ekonomi dan Manajemen Universitas Bina Sarana Informatika Jurnal Teknik Komputer AMIK BSI Paradigma Jurnal Komputasi JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Teknik Informatika C.I.T. Medicom Akrab Juara : Jurnal Ilmu-ilmu Sosial Infotech: Jurnal Informatika & Teknologi Journal of Informatics Management and Information Technology Jurnal Abdimas Ekonomi dan Bisnis Reputasi: Jurnal Rekayasa Perangkat Lunak IMTechno: Journal of Industrial Management and Technology Sinergi: Jurnal Pengabdian Kepada Masyarakat Bulletin of Informatics and Data Science Darma Abdi Karya: Jurnal Pengabdian Kepada Masyarakat Jurnal Informatika
Agustiani, Sarifah
Universitas Bina Sarana Informatika
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Social Sciences Other

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Comparative Optimization of EfficientNetB3, MobileNetV2, and ResNet50 for Waste Classification Sarifah Agustiani; Haryani Haryani; Agus Junaidi; Rizky Rachma Putri; Meutia Raissa Emiliana
Jurnal Informatika Vol. 12 No. 2 (2025): October
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/

Abstract

Waste management is an important challenge in protecting the environment and public health. Improperly managed waste can cause pollution and hinder the recycling process. This study aims to classify waste based on images by optimizing three deep learning architectures, namely EfficientNetB3, MobileNetV2, and ResNet50, to determine the model with the best performance. The dataset comes from the Kaggle platform, consisting of 4,650 images in six categories: battery, glass, metal, organic, paper, and plastic. The research stages include preprocessing, data augmentation, model development, and evaluation using accuracy, precision, recall, and F1-score metrics. The results show that EfficientNetB3 with the Adam optimizer achieved the best performance with 93% accuracy, followed by ResNet50 with 91%, while MobileNetV2 ranged from 70–73% depending on the optimizer. Variations in optimizers were found to affect model performance, while data augmentation improved generalization capabilities, especially in classes with limited samples. This research confirms the potential of deep learning methods in supporting automatic waste classification systems and provides a basis for the development of technology-based waste management systems in the future.
Co-Authors Abdul Latif Agung Sasongko Agus Junaidi Agus Junaidi Ali Mustopa, Ali Andi Saryoko Andreas Roy Prasetya Arifin, Yosep Tajul Atang Saepudin Atang Saepudin Cucu Ika Agustyaningrum Emiliana, Meutia Raissa Fachry Abda El Rahman Ganda Wijaya Gata, Windu Haliza Ramadhanti, Pristya Haryani Haryani Haryani Haryani Hendri Mahmud Nawawi Kamil, Anton Abdul Basah Laksono Trisnantoro M. Rangga Ramadhan Saelan Maysaroh, Maysaroh Meutia Raissa Emiliana Mustopa, Ali Nanik Wuryani Nawawi, Hendri Mahmud Prawikas, Amanda Pribadi, Denny Pristya Haliza Ramadhanti Putri, Rizky Rachma Rahmat Hidayat Rahmat Hidayat Rianto, Yan Riska Aryanti Rizky Ade Safitri Rizky Rachma Putri Roy Prasetya, Andreas Rusda Wajhillah, Rusda Siti Khotimatul Wildah Siti Marlina, Siti Sopiyan Dalis Suharyanto Syamsul Bahri Titik Misriati tri wahyuni Tri Wahyuni Tya Septiani Nurfauzia Koeswara Wahyudin Wahyudin Windu Gata Windu Gata Yoseph Tajul Arifin Yudhistira Yudhistira yunita yunita