Predict Airline Customer Satisfaction using a Machine Learning Model - Yoel Dinata Suwito; Yeremia Alfa Susetyo (SISTEMASI, 2026)
Customer satisfaction is a strategic factor for the sustainability of airline businesses amid increasingly intense competition in the aviation industry. This study aims to predict airline customer satisfaction using an Artificial Neural Network (ANN) approach by leveraging a publicly available Kaggle dataset containing 22 airline service features. Two ANN architectures were developed, differing primarily in the number of hidden layers, the number of neurons, and the application of Batch Normalization and LeakyReLU in the second model. The experimental results show that the first ANN model achieves an accuracy of 92.31%, while the second model attains significantly higher performance, with an accuracy of 95.75% on the test dataset. The second model also demonstrates a strong balance between precision and recall (0.94–0.97), with an average F1-score of 0.95–0.96 and a minimal number of misclassifications. These results confirm that employing a more complex ANN architecture can deliver highly accurate predictions of customer satisfaction. The implementation of ANN-based predictive models not only enhances passenger experience quality but also strengthens customer loyalty and helps airlines maintain long-term competitiveness.