Prediksi Kepuasan Pelanggan terhadap Fasilitas dan Layanan Maskapai Penerbangan X dengan Model Machine Learning

Ningrum, Dyah Ayu Puspita (2025) Prediksi Kepuasan Pelanggan terhadap Fasilitas dan Layanan Maskapai Penerbangan X dengan Model Machine Learning. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Industri penerbangan merupakan salah satu sektor yang sangat kompetitif, di mana kepuasan pelanggan menjadi indikator penting dalam mempertahankan dan meningkatkan kualitas fasilitas dan layanan. Kepuasan pelanggan maskapai penerbangan merupakan indikator penting dalam mempertahankan dan meningkatkan kualitas fasilitas dan layanan. Maskapai penerbangan perlu memahami faktor-faktor yang mempengaruhi kepuasan pelanggannya. Penelitian ini menggunakan metode machine learning dengan model Logistic Regression, Random Forest, dan Support Vector Machine (SVM). Hasil penelitian menunjukkan bahwa Random Forest memiliki akurasi tertinggi sebesar 95.09%, diikuti oleh SVM (93.80%) dan Logistic Regression (82.19%). Dan untuk waktu running model, Logistic Regression selama 1 menit 679 detik, Random Forest selama 19 detik 384 milidetik, dan SVM selama 4 menit 72 detik 539 milidetik. Sehingga, model Random Forest adalah model yang paling akurat dan paling cepat. Dari hasil penelitian, faktor utama yang mempengaruhi kepuasan pelanggan adalah Online Boarding, Layanan Wi-Fi, Hiburan, dan Ketepatan Waktu Keberangkatan/Kedatangan. Hasil penelitian ini diharapkan dapat memberikan wawasan bagi industri penerbangan dalam meningkatkan kualitas layanan dan membuat keputusan strategis yang lebih tepat guna meningkatkan kepuasan pelanggan.
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The airline industry is one of the most competitive sectors, where customer satisfaction serves as a crucial indicator for maintaining and improving the quality of facilities and services. Airline customer satisfaction is a vital factor in sustaining and enhancing service quality. Airlines need to understand the factors influencing their customers' satisfaction. This study employs machine learning methods using Logistic Regression, Random Forest, and Support Vector Machine (SVM) models. The results indicate that the Random Forest model achieves the highest accuracy at 95.09%, followed by SVM (93.80%) and Logistic Regression (82.19%). Regarding model runtime, Logistic Regression took 1 minute and 679 seconds, Random Forest took 19 seconds and 384 milliseconds, and SVM took 4 minutes and 72 seconds 539 milliseconds. Hence, Random Forest is identified as the most accurate and fastest model. The study reveals that the main factors influencing customer satisfaction are Online Boarding, Wi-Fi Service, Entertainment, and Departure/Arrival Punctuality. These findings are expected to provide insights for the aviation industry in improving service quality and making more strategic decisions to enhance customer satisfaction.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Machine learning, Customer Satisfaction, Industri Penerbangan, Citilink Indonesia Machine learning, Customer Satisfaction, Airline Industry, Citilink Indonesia
Subjects:	Q Science > Q Science (General) > Q325.5 Machine learning. Support vector machines. Q Science > QA Mathematics > QA278.2 Regression Analysis. Logistic regression T Technology > T Technology (General) > T58.62 Decision support systems
Divisions:	Faculty of Science and Data Analytics (SCIENTICS) > Mathematics > 44201-(S1) Undergraduate Thesis
Depositing User:	Dyah Ayu Puspita Ningrum
Date Deposited:	05 Feb 2025 09:40
Last Modified:	05 Feb 2025 09:40
URI:	http://repository.its.ac.id/id/eprint/118269

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