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All Journal Jupiter JUSIFO : Jurnal Sistem Informasi SCIENCE TECH: Jurnal Ilmiah Ilmu Pengetahuan dan Teknologi Jurnal Ilmiah Matrik Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Jurnal Pendidikan dan Konseling Journal of Information Systems and Informatics Jurnal Nasional Ilmu Komputer Jurnal Indonesia : Manajemen Informatika dan Komunikasi KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Pengabdian kepada Masyarakat Bina Darma Jurnal Pengabdian Masyarakat Information Technology (JPM ITech) Ngabdimas Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Jurnal Informatika Dan Tekonologi Komputer
Syahril Rizal
Universitas Bina Darma
Agriculture, Biological Sciences & Forestry Humanities Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Other

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Detection of Inorganic Waste Using Convolutional Neural Network Method Achmad Riduan; Febriyanti Panjaitan; Syahril Rizal; Nurul Huda; Susan Dian Purnamasari
Journal of Information System and Informatics Vol 6 No 1 (2024): March
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i1.662

Abstract

Waste, encompassing both domestic and industrial materials, presents a significant environmental challenge. Effectively managing waste requires accurate identification and classification. Convolutional Neural Networks (CNNs), particularly the Residual Network (ResNet) architecture, have shown promise in image classification tasks. This research aims to utilize ResNet to identify types of waste, contributing to more efficient waste management practices. The ResNet101 architecture, comprising 101 layers, is employed in this study for waste classification. The dataset consists of 2527 images categorized into six classes: Cardboard, Glass, Metal, Paper, Plastic, and Trash. The ResNet model is pre-trained, leveraging existing knowledge to enhance classification accuracy. The dataset is divided into training and testing sets to evaluate the model's performance. The testing results, evaluated using a Confusion Matrix, demonstrate strong performance in waste classification. The ResNet101 model achieves 92% accuracy in detecting inorganic waste objects within the training dataset and maintains a high accuracy of 90% on the testing dataset. This indicates the effectiveness of the ResNet architecture in accurately identifying various types of waste, contributing to improved waste management efforts. he utilization of ResNet101 for waste classification yields promising results, with high accuracy rates observed across both training and testing datasets. By effectively identifying types of waste, this approach facilitates more efficient waste management practices, enabling better resource allocation and environmental conservation. Further research and application of CNN architectures in waste management could lead to enhanced sustainability efforts and improved waste-handling strategies.
Co-Authors Aan Restu Mukti Achmad Riduan Ahmad Syazili Alek Wijaya Amalia , Rahayu Andries Anwar Benny Wilson Saputra Choirummansyah Muiz Dian Lestari Dwi Satria Putra Elsa Oktaviana Fatoni Febriyanti Panjaitan Felli Afriyanti Fikri Ya'afi Mustika Hadinata, Novri Ilman Zuhriyadi Loren Diaz Lintang M. Denny Tri Lisandi M. Rido Fadilah M. Soekarno Putra Margareta Andriani Maria Ulfa Marsuki Marsuki Merry Agustina Muhammad Imam Faisal Nurul Huda Nyimas Sopiah Oktaviani, Nia Qoriani Widayati Renaldi Agustian Hamzah Rian Suprayogi Rindi qusnul Khotimah Sunda Ariana, Sunda Surya Adi Saputra Suryanto Suryanto Susan Dian Purnamasari Trisninawati Wahyu Wijaya Wydyanto Yanti Pasmawati