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All Journal Sinergi
Safira Amalia
Graduate Program of Science Management, Universitas Padjadjaran
Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Comparative analysis of classification algorithm: Random Forest, SPAARC, and MLP for airlines customer satisfaction Safira Amalia; Irene Deborah; Intan Nurma Yulita
SINERGI Vol 26, No 2 (2022)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2022.2.010

Abstract

The airline business is one of the businesses determined by the quality of its services. Every airline creates its best service so that customers feel satisfied and loyal to using their services. Therefore, customer satisfaction is an essential metric to measure features and services provided. By having a database on customer satisfaction, the company can utilize the data for machine learning modelling. The model generated can predict customer satisfaction by looking at the existing feature criteria and becoming a decision support system for management. This article compares machine learning between Split Point and Attribute Reduced Classifier (SPAARC), Multilayer Perceptron (MLP), and Random Fores (RF) in predicting customer satisfaction. Based on the data testing, the Random Forest algorithm provides better results with the lowest training time compared to SPAARC and MLP. It has an accuracy of 95.827%, an F-score of 0.958, and a training time of 84.53 seconds.
Co-Authors Intan Nurma Yulita, Intan Nurma Irene Deborah