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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Tropical and Infectious Disease JOIN (Jurnal Online Informatika) Fountain of Informatics Journal Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Paediatrica Indonesiana Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JRST (Jurnal Riset Sains dan Teknologi) Dinamisia: Jurnal Pengabdian Kepada Masyarakat JSiI (Jurnal Sistem Informasi) JPPM (Jurnal Pengabdian dan Pemberdayaan Masyarakat) Jurnal Gema Ngabdi Jurnal Repositor Indian Journal of Forensic Medicine & Toxicology Journal of System and Computer Engineering AL-UMRON: Jurnal Pengabdian kepada Masyarakat Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Jurnal Komputer, Informasi dan Teknologi
Basuki, Setio
Universitas Muhammadiyah Malang
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Journal : journal of system and computer engineering

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Explainable Non-Organic Waste Classification: A Comparative Study of CNN with SHAP Interpretability Against Vision Transformer Approaches Setio Basuki; Lika Anjelina; Alfian Wahyu Juhar Putra; Yusuf Nur Muhammad
Journal of System and Computer Engineering Vol 7 No 3 (2026): JSCE: July 2026
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v7i3.2694

Abstract

This study develops a classification model based on Machine Learning (ML) and Computer Vision (CV) to automatically distinguish recyclable and non-recyclable waste. Waste management still faces the challenge of low manual sorting efficiency, thus reducing recycling potential. The dataset consists of 399 waste images divided with a ratio of 80:10:10 for training, validation, and testing. Three combinations of visual features were tested, namely Mean Color (RGB) + LBP + HOG, Histogram + HOG + Edge, and Mean Color (RGB) + Histogram + GLCM, each of which was evaluated using four conventional ML algorithms, namely SVM-RBF, SVM-Linear, Random Forest, and Gradient Boosting. Meanwhile, deep learning models namely CNN, ViT, and LoRA ViT were trained directly on raw images without manual feature extraction. Experimental results show that CNN achieved the highest testing accuracy of 82.50%, outperforming all conventional ML models that achieved a maximum accuracy of 75.00%, as well as ViT (72.50%) and LoRA ViT (70.00%). The application of SHAP-based Explainable AI (XAI) provides transparency to the model's decision-making process. These findings demonstrate that CNN with certain regularization settings are effective for distinguishing recyclable and non-recyclable waste, in supporting sustainable smart waste management systems.
Co-Authors Abdachul Charim Abdillah, Abid Famasya Agus Eko Minarno Akbi, Denar Regata Akmal Shahib, Maulana Alfian Wahyu Juhar Putra Alfira Rizky Alimuddin Hasan Al Kabir Amelia Khoidir Aminudin Aminudin Amrul Faruq Aulia Arif, Wardhana Baiduri, Senja Bangkit Putrawan Charim, Abdachul Diany Yogiantoro Dini Tri Purwaningsih Dominicus Husada Dwiyanti Puspitasari, Dwiyanti Edo Ardhiansyah Effendy, Nico Ardia Faiqurrahman, Mahar Faizun Nuril Hikmah Gita Indah Marthasari Haq, Arini Hariyady Hariyady Hendra Saputra Hilman Hilman Hilwana, Lutifta Husada, Dominicius Indrabayu, Rizky Irfan, Muhammad irma fitriani Irwanto Irwanto Ismoedijanto Izzah, Tsabita Nurul Kartina, Leny Khoirir Rosikin Kusuma, Selvia Ferdiana La Febry Andira Rose Cynthia Lika Anjelina Lina Dwi Yulianti M.Rafly Rahman Mahar Faiqurahman Masatoshi Tsuchiya Mauridhi Hery Purnomo Mizwar Mizwar Muhammad Daffa Nugraha Muhammad Fadliansyah Muhammad Ilham Perdana Muhammad Nasrul Tsalatsa Putra Muhammad Rizki Muhammad Yusuf Mustikasari, Rahma Ira Novita Daian Marlissa Nugraha, Muhammad Daffa Nur Hayatin Putri, Valencia Sefiana Rangga Pratama Rima Mediana Mashita Risa Etika, Risa Rizky Indrabayu Rizky Sulaiman Rizky, Alfira S, Vinna Rahmayanti Sari, Zamah Shafiyah, Rahajeng Febri Siti Maghfiroh Soegeng Soegiyanto Sumadi, Fauzi Dwi Setiawan Syafaah, Lailis Titin Eka Puspitawati Tsuchiya, Masatoshi Wibowo, Prasetyo Wicaksono, Galih Wasis Wisnujono Soewono Yuda Munarko Yufis Azhar Yusuf Nur Muhammad Zachra, Fatimatus Zakiyah Rakhmawati Zamah Sari