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All Journal Jurnal Media Infotama Jurnal Informatika IKRA-ITH Informatika : Jurnal Komputer dan Informatika ILKOM Jurnal Ilmiah JE (Journal of Empowerment) Jurnal Pengabdian Masyarakat Bumi Raflesia JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Prima Abdika: Jurnal Pengabdian Masyarakat Jurnal Multidisiplin Dehasen (MUDE) Jurnal Media Computer Science JURNAL BESEMAH Innovative: Journal Of Social Science Research Kohesi: Jurnal Sains dan Teknologi Jurnal INFOTEL Jurnal Penelitian Teknologi Informasi dan Sains GEMBIRA (Pengabdian Kepada Masyarakat) Jurnal Media Akademik (JMA) Jurnal Al-Maun Abdi Reksa JCOSIS (Journal Computer Science and Information Systems) Jurnal Komputer, Informasi dan Teknologi
Sastya Hendri Wibowo
Universitas Muhammadiyah Bengkulu
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Journal : jcosis journal computer science and information systems

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Performance Evaluation of MobileNetV2 and DenseNet169 Using Transfer Learning for Inorganic Waste Image Classification Ari Wibowo; Erwin Dwika Putra; Sastya Hendri Wibowo; Ardi Wijaya
JCOSIS (Journal Computer Science and Information Systems) Vol. 3 No. 2 (2026): October
Publisher : Institute for Research and Community Service

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61567/jcosis.v3i2.302

Abstract

Purpose: Manual waste sorting still has limitations because it requires considerable time and may cause errors in identifying waste categories. The development of Computer Vision and Deep Learning technology provides an opportunity to develop an automated waste image classification system. This study aims to compare the performance of two Convolutional Neural Network (CNN) architectures, namely MobileNetV2 and DenseNet169, for inorganic waste image classification using the Transfer Learning approach. Methods/Study design/approach: This study applies a Transfer Learning method using MobileNetV2 and DenseNet169 architectures. The dataset consists of inorganic waste images from two categories, namely cardboard and paper, obtained from the Kaggle dataset and primary documentation data. The research stages include dataset collection, image preprocessing, dataset splitting, model training, and performance evaluation using Accuracy, Precision, Recall, F1-Score, and Confusion Matrix. The dataset was divided into 80% training data, 10% validation data, and 10% testing data. Result/Findings: The experimental results show that MobileNetV2 achieved an Accuracy of 96.14%, Precision of 95.05%, Recall of 97.82%, and F1-Score of 96.42%. Meanwhile, DenseNet169 achieved an Accuracy of 96.14%, Precision of 95.70%, Recall of 97.09%, and F1-Score of 96.39%. Novelty/Originality/Value: This study provides an objective comparison between a lightweight CNN architecture (MobileNetV2) and a deeper architecture (DenseNet169) using the same experimental configuration. The results indicate that MobileNetV2 can achieve comparable classification performance with lower computational complexity, making it more suitable for implementation on resource-constrained devices.
Deteksi Sampah Plastik Berukuran Kecil Menggunakan Arsitektur Faster R-CNN Imelia Okta Rama Yanti; Erwin Dwika Putra; Sastya Hendri Wibowo; Muntahanah
JCOSIS (Journal Computer Science and Information Systems) Vol. 3 No. 2 (2026): October
Publisher : Institute for Research and Community Service

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61567/jcosis.v3i2.303

Abstract

Abstrak. Tujuan : Sampah plastik berukuran kecil, seperti potongan plastik, tutup botol, dan bungkus kemasan, sulit dipilah secara manual karena detailnya terbatas dan sering menyatu dengan latar belakang yang kompleks. Penelitian ini bertujuan menganalisis kinerja arsitektur Faster R-CNN dalam mendeteksi sampah plastik berukuran kecil pada citra digital. Metode/Design/Pendekatan: Data penelitian berupa 518 citra uji yang berasal dari data primer (60%) dan data sekunder (40%) dengan perbandingan data latih dan data uji sebesar 80:20. Setiap citra dianotasi menggunakan bounding box, kemudian model dilatih menggunakan backbone ResNet-50 FPN dan dievaluasi menggunakan confusion matrix, precision, recall, F1-score, Average Intersection over Union (IoU), dan mean Average Precision (mAP). Hasil/Temuan: Model mengenali 1.496 dari 1.642 objek ground truth (85,34%) dengan precision 85,34%, recall 91,11%, F1-score 88,13%, rata-rata IoU 88,28%, dan mAP@0.50 sebesar 92,01%. Confidence threshold optimal diperoleh pada 0,85 dengan F1-score 89,44%. Kebaharuan/Originalitas/Nilai: Penelitian ini secara khusus mengkaji kemampuan Faster R-CNN pada objek sampah plastik berukuran sangat kecil dan tipis pada berbagai kondisi pencahayaan dan latar belakang, sebuah aspek yang masih terbatas dikaji pada penelitian deteksi sampah plastik sebelumnya, sekaligus menunjukkan bahwa keterbatasan utama model terletak pada kemampuan menemukan objek dan bukan pada ketepatan lokalisasi bounding box. Keywords: Faster R-CNN, deteksi objek, sampah plastik, deep learning, citra digital
Co-Authors A.R Walad Mahfuzhi Abdillah, Muhammad Harits Adi, Puput Dani Prasetyo Afredo Fadlikal Ilham Aditama Ajib Hanani Apridiansyah, Yovi Ari Wibowo Ari Wibowo Bima Aditiansyah Darnita , Yulia Darnita, Yulia Dedi Abdullah Diana Diana Diana Diana Dio Syahputra Ede Ilham Egia Apriansyah Emel Lia Utari Gading Cempaka Ganis Chandra Puspitadewi Gugi Walus Riman Handayani, Sri Harry Soepandi Harry Witriyono Harry Witriyono Witriyono Harry Witryono hidayah, agung kharisma Imanullah, Muhammad Imelia Okta Rama Yanti Indra Kanedi Jericko Vivaldo Joshi Johan Ericka Wahyu Prakasa Juhardi, Ujang Jusuf Wahyudi Khairullah Khairullah, Khairullah Khakim, Ahmad Nuril Kitagawa, Akio KMS M Yeri Habibullah M. Imanullah, M. Imanullah Malik Fajar Alamsyah Maria Veronika, Nuri David Mawarni, Shindy Mgs. M. Ihsan Zamadi Muhammad Fajhry Amir Muhammad Imanullah Muhammad Yassir Muntahanah Muntahanah Muntahanah, Muntahanah Muntahannah Muntahannah Nanda Yugo Pangestu Nora Fransiska Nugraha Apriansyah Nuri David Maria Veronika Onsardi Onsardi Putra Andika, Febby Putra, Erwin Dwika Rido Syahputra Ristontowi Rojali Toyib Rozali Toyib Safrizal, Edy Sandi Anggara Sapitri Ramadhani Sapitri Ramadhani Surya Ade Saputera Suwandi Ramadhan Taufiq Syaifullah Toyib, Rozali Vebi Hardiansyah Wahyu Nugroho Wahyu Redo Ilahi Wijaya, Ardi Wilio Papian Witriyono, Harry Yeri Habibullah, KMS M Yulia Darmi Yulia Darnita Yulia Darnita