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Ulya, Fadilla Zundina
Institut Teknologi Telkom Purwokerto
Computer Science & IT Mathematics

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Klasifikasi Bahan Biodegradable dan Non-Biodegradable Menggunakan Convolutional Neural Network (CNN) Latief, Muhammad Abdul; Azfa Riyyasy, Muhammad Rasikh; Ulya, Fadilla Zundina; Puspita, Popy Laras; Claudia, Gavrilla; Nabila, Luthfi Rakan
STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Vol 8, No 3 (2024)
Publisher : Universitas Indraprasta PGRI Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/string.v8i3.19314

Abstract

Deep Learning is a new scientific field in the field of Machine Learning which has recently developed. Deep Learning has excellent capabilities in computer vision. One of its uses is in the case of classifying objects into biodegradable and non-biodegradable materials. By implementing the CNN method in this case, it is possible to classify biodegradable and non-biodegradable waste appropriately and efficiently. This study uses image data of biodegradable and non-biodegradable materials sourced from Kaggle. The stages in this study consist of six stages. The first stage is to retrieve the dataset. The second stage is the preprocessing stage by rescaling the image. The third stage is to create a CNN model. The fourth stage is model training to get higher accuracy. The fifth stage is model evaluation and the last is testing the model. From the classification test using the CNN method, an accuracy of 93% is obtained. So it can be concluded that the CNN method used in this paper is capable of performing a good classification.
Co-Authors Azfa Riyyasy, Muhammad Rasikh Claudia, Gavrilla Latief, Muhammad Abdul Nabila, Luthfi Rakan Puspita, Popy Laras