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All Journal Internet of Things and Artificial Intelligence Journal
Erwin Sirait
Sekolah Tinggi Akuntansi dan Manajemen Indonesia
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Journal : Internet of Things and Artificial Intelligence Journal

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Classification of Customer Satisfaction Through Machine Learning: An Artificial Neural Network Approach Victor Marudut Mulia Siregar; Kalvin Sinaga; Erwin Sirait; Andi Setiadi Manalu; Muhammad Yunus
Internet of Things and Artificial Intelligence Journal Vol. 3 No. 3 (2023): Vol. 3 No. 3 (2023): Volume 3 Issue 3, 2023 [August]
Publisher : Association for Scientific Computing, Electronics, and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/iota.v3i3.643

Abstract

This study aims to classify customer satisfaction data from Café Alvina using Machine Learning, specifically by implementing the Backpropagation Artificial Neural Network. The data used in this study consists of 70 training data and 30 testing data, with the input layer of the Artificial Neural Network having 5 neurons and the output layer having 2 neurons. The tested Artificial Neural Network models include the 5-5-2 model, 5-10-8-8-2 model, 5-5-10-2 model, and 5-8-10-2 model. Among the four models used in the testing process of the Backpropagation Artificial Neural Network system using Matlab, the 5-10-8-8-2 architecture model performed the best, achieving an MSE (Mean Squared Error) of 0.000999932 during training with 2920 epochs and a testing MSE of 0.000997829. After conducting the testing, the performance of the Artificial Neural Network models was as follows: the 5-5-2 model achieved 81%, the 5-10-8-8-2 model achieved 100%, the 5-5-10-2 model achieved 98%, and the 5-8-10-2 model achieved 96%. Through the implementation of Backpropagation Artificial Neural Network, the classification of customer satisfaction can be effectively performed. The trained and tested data demonstrate that the Artificial Neural Network can accurately recognize the input data in the system.
Co-Authors Andi Setiadi Manalu Kalvin Sinaga Muhammad Yunus Victor Marudut Mulia Siregar