Analis Data Penjualan Tiket Pesawat Ke Jepang Menggunakan Classic Machine Learning Pada PT. TTD - Syarifuddin (Universitas Pamulang, 2024)
Airline ticket sales to Japan is an important topic in the ever-evolving travel industry. The unpredictable demand for airline tickets is a common challenge in the aviation industry due to many complex and variable factors. This research analyzes the data on airline ticket sales to Japan using classic machine learning approaches. The data used for the research are the airline ticket sales to Japan in 2022 and 2023 for Japan Airlines and All Nippon Airways. Classical methods such as classification were applied to identify factors influencing the ticket sales patterns. The collected data was cleaned and organized to obtain a dataset for use in Machine Learning with the K-Nearest Neighbors, Naïve Bayes, and Decision Tree algorithms. After evaluating the models created using these three algorithms, the evaluation results with prediction models, model test & score, and confusion matrix, showed that the K-Nearest Neighbor algorithm achieved the highest values compared to the Naïve Bayes and Decision Tree algorithms with an accuracy of 99.5% (model predictions evaluation) & 98.9% (model test & score evaluation). The majority of ticket sales to Japan were for JAL flights, economy class tickets, and spring season being the most popular choices. The conclusion from this data is that Japan Airlines holds a strong market share in ticket sales to Japan