XGBoost for Predicting Airline Customer Satisfaction Based on Computational Efficient Questionnaire
DOI:
https://doi.org/10.21108/ijoict.v9i2.864Keywords:
Adaptive Boosting (AdaBoost), Prediction, Airline Customer, Missing ValueAbstract
Customer satisfaction can be created through a well-crafted service quality strategy, which forms the cornerstone of a successful business-customer relationship. Establishing and nurturing these relationships with customers is vital for long-term success. Within the airline industry, a persistent challenge lies in enhancing the passenger experience during flights, necessitating a comprehensive understanding of customer demands. Addressing this challenge is crucial for airlines aspiring to thrive in a competitive landscape, thus underlining the significance of providing top-notch services. This study addresses this issue by leveraging predictive airline customer satisfaction data analysis. We forecast customer satisfaction levels using a powerful Extreme Gradient Boosting (XGBoost) ensemble-based model. An integral aspect of our methodology involves handling missing values in the dataset, for which we utilize mean-value imputation. Furthermore, we introduce a novel logistic Pearson Gini (Log-PG) score to identify the factors that significantly influence airline customer satisfaction. In our predictive model, we achieved notable results, showing an accuracy and precision of 0.96. To ascertain the efficiency of our model, we conducted a comparative analysis with other boosting-type ensemble prediction models, such as gradient boosting and adaptive boosting (AdaBoost). The comparative assessment established the superiority of the XGBoost model in predicting airline customer satisfaction.
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References
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[17] S. F. Pane, A. G. Putrada, N. Alamsyah, and M. N. Fauzan, “A PSO-GBR Solution for Association Rule Optimization on Supermarket Sales,” in 2022 Seventh International Conference on Informatics and Computing (ICIC), IEEE, 2022, pp. 1–6, doi: 10.1109/ICIC56845.2022.10007001.
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[21] T. A. Prasetya, C. T. Harjanto, and A. Setiyawan, “Analysis of student satisfaction of e-learning using the end-user computing satisfaction method during the Covid-19 pandemic,” J. Phys. Conf. Ser., vol. 1700, no. 1, p. 012012, Dec. 2020, doi: 10.1088/1742-6596/1700/1/012012.
[22] D. R. Gopinath and D. R. Kalpana, “RELATIONSHIP OF JOB INVOLVEMENT WITH JOB SATISFACTION,” Adalya J., vol. 9, no. 7, Jul. 2020, doi: 10.37896/aj9.7/029.
[23] Md. A. I. Gazi, Md. A. Islam, J. Shaturaev, and B. K. Dhar, “Effects of Job Satisfaction on Job Performance of Sugar Industrial Workers: Empirical Evidence from Bangladesh,” Sustainability, vol. 14, no. 21, p. 14156, Oct. 2022, doi: 10.3390/su142114156.
[24] N. G. Ramadhan, Adiwijaya, and A. Romadhony, “Preprocessing Handling to Enhance Detection of Type 2 Diabetes Mellitus based on Random Forest,” Int. J. Adv. Comput. Sci. Appl., vol. 12, no. 7, 2021, doi: 10.14569/IJACSA.2021.0120726.
[25] M. Liu et al., “Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques,” Artif. Intell. Med., vol. 142, p. 102587, Aug. 2023, doi: 10.1016/j.artmed.2023.102587.
[26] I. D. Oktaviani and A. G. Putrada, “KNN imputation to missing values of regression-based rain duration prediction on BMKG data,” J. INFOTEL, vol. 14, no. 4, Nov. 2022, doi: 10.20895/infotel.v14i4.840.
[27] M. Szczepa?ski, M. Pawlicki, R. Kozik, and M. Chora?, “The Application of Deep Learning Imputation and Other Advanced Methods for Handling Missing Values in Network Intrusion Detection,” Vietnam J. Comput. Sci., vol. 10, no. 01, pp. 1–23, Feb. 2023, doi: 10.1142/S2196888822500257.
[28] N. G. Ramadhan, “Data Mining Techniques in Handling Personality Analysis for Ideal Customers,” J. OfInformation Syst. Eng. Bus. Intell., vol. 8, no. 2, pp. 175–181, Oct. 2022, doi: 10.20473/jisebi.8.2.175-182.
[29] W.-C. Lin and C.-F. Tsai, “Missing value imputation: a review and analysis of the literature (2006–2017),” Artif. Intell. Rev., vol. 53, no. 2, pp. 1487–1509, Feb. 2020, doi: 10.1007/s10462-019-09709-4.
[30] T. Aittokallio, “Dealing with missing values in large-scale studies: microarray data imputation and beyond,” Brief. Bioinform., vol. 11, no. 2, pp. 253–264, Mar. 2010, doi: 10.1093/bib/bbp059.
[31] K. Shyamala and C. S. Padmasini, “Mtanh-Attention-BiLSTM model for prediction of Automobile Export Customer Satisfaction,” in 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), IEEE, 2021, pp. 652–660, doi: 10.1109/ICAIS50930.2021.9395837.
[32] C. Kadilar and H. Cingi, “Ratio estimators in simple random sampling,” Appl. Math. Comput., vol. 151, no. 3, pp. 893–902, Apr. 2004, doi: 10.1016/S0096-3003(03)00803-8.
[33] S. Elmståhl, J. Sanmartin Berglund, C. Fagerström, and H. Ekström, “The life satisfaction index-A (LSI-A): normative data for a general Swedish population aged 60 to 93 years,” Clin. Interv. Aging, pp. 2031–2039, 2020, doi: 10.2147/CIA.S275387.
[34] K. Patidar, R. K. Gour, A. Dixit, M. Verma, and A. K. Pal, “An Improved Method for the Data Cluster Based Feature Selection and Classification,” in 2023 International Conference for Advancement in Technology (ICONAT), Goa, India: IEEE, Jan. 2023, pp. 1–6. doi: 10.1109/ICONAT57137.2023.10080669.
[35] S. Buyruko?lu and A. Akba?, “Machine Learning based Early Prediction of Type 2 Diabetes: A New Hybrid Feature Selection Approach using Correlation Matrix with Heatmap and SFS,” Balk. J. Electr. Comput. Eng., vol. 10, no. 2, pp. 110–117, Apr. 2022, doi: 10.17694/bajece.973129.
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Published
2023-12-29
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Nur Ghaniaviyanto Ramadhan, & Aji Gautama Putrada. (2023). XGBoost for Predicting Airline Customer Satisfaction Based on Computational Efficient Questionnaire. International Journal on Information and Communication Technology (IJoICT), 9(2), 120–136. https://doi.org/10.21108/ijoict.v9i2.864
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