Systematic Comparison of Machine Learning Model Accuracy Value Between MobileNetV2 and XCeption Architecture in Waste Classification System
DOI:
https://doi.org/10.47355/avia.v4i2.70Abstract
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%.References
D. Kusumaningsari, J. Fisika, F. Matematika, D. Ilmu, and P. Alam, “PEMANFAATAN DAN PENGOLAHAN SAMPAH ORGANIK DAN NON-ORGANIK UTILIZATION AND PROCESSING OF ORGANIC AND NON-ORGANIC WASTE.”
M. D. Payana, D. R. Y. TB, Z. Musliyana, and M. B. Wibawa, “DETEKSI MASKER WAJAH MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN) MENINGKATKAN NILAI AKURASI MELALUI ARSITEKTUR LAYER KONVOLUSI,” JOURNAL OF INFORMATICS AND COMPUTER SCIENCE, vol. 8, no. 1, pp. 30–35, 2022, doi: 10.33143/JICS.VOL8.ISS1.2123.
“Pengertian dan Cara Kerja Algoritma Convolutional Neural Network (CNN) - Trivusi.” https://www.trivusi.web.id/2022/04/algoritma-cnn.html (accessed Jan. 25, 2023).
“Keras API reference.” https://keras.io/api/ (accessed Jan. 25, 2023).
Yaya Heryadi and Teguh Wahyono, Machine Learning Konsep dan Implementasi. Yogyakarta: Penerbit Gava Media, 2020.
IdMetafora.com, “Memahami Apa Itu Convolutional Neural Network! Yuk Simak Penjelasannya di Sini, Jasa Pembuatan Website - Metafora Indonesia Tehnology,” Apr. 2014.
“MobileNet: Deteksi Objek pada Platform Mobile | by Rizqi Okta Ekoputris | Nodeflux | Medium.” https://medium.com/nodeflux/mobilenet-deteksi-objek-pada-platform-mobile-bbbf3806e4b3 (accessed Jan. 25, 2023).
Sandhopi, L. Z. P.C.S.W, and Y. Kristian, “Identifikasi Motif Jepara pada Ukiran dengan Memanfaatkan Convolutional Neural Network,” Jurnal Nasional Teknik Elektro dan Teknologi Informasi, vol. 9, no. 4, pp. 403–413, Dec. 2020, doi: 10.22146/JNTETI.V9I4.541.
“Various Optimization Algorithms For Training Neural Network | by Sanket Doshi | Towards Data Science.” https://towardsdatascience.com/optimizers-for-training-neural-network-59450d71caf6 (accessed Jan. 25, 2023).
