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All Journal International Journal of Aviation Science and Engineering
Mugahed Al-Rahmi, Waleed
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Aerospace Engineering Automotive Engineering Electrical & Electronics Engineering Mechanical Engineering Transportation

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Systematic Comparison of Machine Learning Model Accuracy Value Between MobileNetV2 and XCeption Architecture in Waste Classification System Mulyani, Yessi; Kurniawan, Rian; Budi Wintoro, Puput; Komarudin, Muhammad; Mugahed Al-Rahmi, Waleed
International Journal of Aviation Science and Engineering - AVIA Vol. 4, No. 2 (December 2022)
Publisher : FTMD Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47355/avia.v4i2.70

Abstract

Garbage generated every day can be a problem because some types of waste are difficult to decompose so they can pollute the environment. Waste that can potentially be recycled and has a selling value is inorganic waste, especially cardboard, metal, paper, glass, plastic, rubber and other waste such as product packaging. Various types of waste can be classified using machine learning models. The machine learning model used for classification of waste systems is a model with the Convolutional Neural Network (CNN) method. The selection of the CNN architecture takes into account the required accuracy and computational costs. This study aims to determine the best architecture, optimizer, and learning rate in the waste classification system. The model designed using the MobileNetV2 architecture with the SGD optimizer and a learning rate of 0.1 has an accuracy of 86.07% and the model designed using the Xception architecture with the Adam optimizer and a learning rate of 0.001 has an accuracy of 87.81%.
Co-Authors Budi Wintoro, Puput Muhammad Komarudin Mulyani, Yessi Rian Kurniawan