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All Journal Jurnal Ilmu Komputer dan Teknologi Informasi
Ammar, Daffa Khairul
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Computer Science & IT

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Pengklasifikasian Jenis Sampah Berbasis Visi Komputer Dan Kecerdasan Buatan Wijaya, Gusti Made Kresna Wijaya; Ammar, Daffa Khairul
Jurnal Ilmu Komputer dan Teknologi Informasi Vol. 3 No. 1 (2026): Maret
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63447/jikti.v3i1.1729

Abstract

Waste management presents a significant challenge in ensuring environmental sustainability, requiring an automated classification system to improve efficiency. This study designs a waste classification system (biological, electronic, glass, plastic) using a deep learning approach based on computer vision. The proposed method implements a custom Convolutional Neural Network (CNN) with MobileNet efficiency principles, consisting of Mobile Inverted Bottleneck Convolution (MBConv) and Squeeze-and-Excitation (SE) blocks. The model is developed from scratch using a four-class dataset and optimized with GPU processing and a batch size of 16. After fine-tuning the regularization and hyperparameters, the model achieved the highest accuracy of 75.59%.
Co-Authors Wijaya, Gusti Made Kresna Wijaya