Article Per Year (5 Year)
p-Index From 2021 - 2026
1.325
P-Index
This Author published in this journals
All Journal International Journal of Advances in Intelligent Informatics Jurnal Teknologi dan Sistem Komputer Jurnal Sains dan Informatika Jurnal Sustainable: Jurnal Hasil Penelitian dan Industri Terapan KOMPUTA : Jurnal Ilmiah Komputer dan Informatika Jurnal ABDINUS : Jurnal Pengabdian Nusantara Jurnal Anugerah: Jurnal Pengabdian Kepada Masyarakat Bidang Keguruan dan Ilmu Pendidikan Journal of Information Systems and Technology (JISTech)
Muhamad Radzi Rathomi
Computer Science Department, Engineering Faculty, Universitas Maritim Raja Ali Haji
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

Published : 11 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 11 Documents
Search

 1   2 
Klasifikasi Sampah Plastik di Laut Menggunakan Algoritma Faster R-CNN (Studi Kasus Tanjungpinang) Novrizal Fattah Fahmitra; Farel Putra Albana; Muhamad Radzi Rathomi
Jurnal Sains dan Informatika Vol. 12 No. 1 (2026): Jurnal Sains dan Informatika
Publisher : Teknik Informatika, Politeknik Negeri Tanah Laut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34128/jsi.v12i1.1606

Abstract

The global environment is facing a serious challenge in the form of increasing plastic waste. Indonesia is one of the world’s largest producers of plastic waste, and Tanjungpinang City faces a similar issue, generating approximately 100–150 tons of waste per day, predominantly plastic waste. A significant portion of this waste originates from marine environments, while its management remains limited by inadequate facilities and technological resources. Therefore, a waste detection solution is needed to improve the efficiency of plastic waste management in the region.. This study aims to evaluate the detection performance of a Faster R-CNN model using pre-trained weights with a ResNet-50 Feature Pyramid Network (FPN) backbone. Experimental results show an mAP@0.50:0.95 of 59.55% and an mAP@0.50 of 82.64%. Further evaluation for each plastic waste category resulted in accuracies of 90% for plastic bottles, 88% for food wrappers, 78% for beverage wrappers, and 86% for plastic bags. These findings indicate that the pre-trained Faster R-CNN model with a ResNet-50 FPN backbone is capable of effectively detecting and classifying different types of plastic waste in marine environments. Consequently, this model has the potential to support more effective waste management and sorting processes in the coastal waters of Tanjungpinang City.
Co-Authors Bachtiar, Ibnu Kahfi Chahyadi, Ferdi Farel Putra Albana Fiani, Mia Al Hayaty, Nurul Hekso, Anton Martaleli Bettiza Matulatan, Tekad MHD. Reza M.I. Pulungan Nola Ritha Novrizal Fattah Fahmitra Nurfalinda, Nurfalinda Prayuda, Hardi Raja Azian Ritha, Nola