Analis Data Penjualan Tiket Pesawat Ke Jepang Menggunakan Classic Machine Learning Pada PT. TTD
Keywords:
Data mining, Classification, KNN, Naive Bayes, Decision TreeAbstract
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
References
[1] A’yuniyah, Q., Elvira, W., Nazira, N., Ambarani, I., Intan, S. F., & Ramadhani, D. (2023). Analisa Algoritma Naïve Bayes Classifier (NBC) Untuk Prediksi Penjualan Alat Kesehatan. IJIRSE: Indonesian Journal of Informatic Research and Software
Engineering Journal, 3(2), 119–126.
[2] Fadillah, A. P. (2015). Penerapan Metode CRISP-DM untuk Prediksi Kelulusan Studi Mahasiswa Menempuh Mata Kuliah (Studi Kasus Universitas XYZ). Jurnal Teknik Informatika Dan Sistem Informasi, 1(3), 260–270. https://doi.org/10.28932/jutisi.v1i3.406
[3] Heliyanti Susana. (2022). Penerapan Model Klasifikasi Metode Naive Bayes Terhadap Penggunaan Akses Internet. Jurnal Riset Sistem Informasi Dan Teknologi Informasi (JURSISTEKNI), 4(1), 1–8. https://doi.org/10.52005/jursistekni.v4i1.96
[4] Irmayani, W. (2021). Visualisasi Data Pada Data Mining Menggunakan Metode Klasifikasi. Jurnal Khatulistiwa Informatika, IX(I), 68–72.
[5] Juwita, Safii, M., & Efendi Damanik, B. (2022). Algoritma Naïve Bayes Untuk Memprediksi Penjualan Pada Toko VJCakes Pematang Siantar. Journal of Machine Learning and Artificial Intelligence, 1(4), 337–346. https://doi.org/10.55123/jomlai.v1i4.1674
[6] Leonardi, M., Emilda, R., Katrin, I., & Yulianto, A. (2021). Prediksi Penjualan Produk Rokok Pada PT. Indomarco Prismatama Menggunakan Algoritma C4.5. Paradigma - Jurnal Komputer Dan Informatika, 23(2), 182–190. https://doi.org/10.31294/p.v23i2.11151
[7] Nopianti, Y., Ruliansyah, R., & Fadilah, E. (2017). Sistem Informasi Pemesanan Tiket Travel Berbasis Web (Studi Kasus: Lantra Wisata Travel PO. Sejahtera). Jusifo (Jurnal Sistem Informasi), 3(2), 143–150. https://doi.org/10.19109/jusifo.v3i2.3869
[8] Permadi, M., Febriani, F., & Panggabean, M. J. (2023). Application of the K-Nearest Neighbor ( KNN ) Algorithm for the Classification of Indihome Products Sales in the Riau Region Penerapan Algoritma K-Nearest Neighbor ( KNN ) untuk Klasifikasi Penjualan Produk Indihome Daerah Riau. 105–111.
[9] Putra, V., Tri Pranoto, G., & Eko Putra, F. (2023). Klasifikasi Kebutuhan Sparepart Dengan Algoritma K-Nearest Neighbor Untuk Meningkatkan Penjualan Sparepart. Bulletin of Information Technology (BIT), 4(2), 287–293.
[10] Rismala, R., Ali, I., & Rizki Rinaldi, A. (2023). Penerapan Metode K-Nearest Neighbor Untuk Prediksi Penjualan Sepeda Motor Terlaris. JATI (Jurnal Mahasiswa Teknik Informatika), 7(1), 585–590. https://doi.org/10.36040/jati.v7i1.6419
[11] Robianto, Sitorus, S. H., & Ristian, U. (2021). Penerapan Metode Decision Tree Untuk Mengklasifikasikan Mutu Buah Jeruk Berdasarkan Fitur Warna Dan Ukuran. Jurnal Komputer Dan Aplikasi, 09(01), 76–86.
[12] Suci, W., & Samsudin, S. (2022). Algoritma K-Nearest Neighbors dan Synthetic Minority Oversampling Technique dalam Prediksi Pemesanan Tiket Pesawat. Jurnal Media Informatika Budidarma, 6(3), 1775. https://doi.org/10.30865/mib.v6i3.4374
[13] Suhanda, Y., Kurniati, I., & Norma, S. (2020). Penerapan Metode Crisp-DM Dengan Algoritma K-Means Clustering Untuk Segmentasi Mahasiswa Berdasarkan Kualitas Akademik. Jurnal Teknologi Informatika Dan Komputer, 6(2), 12–20. https://doi.org/10.37012/jtik.v6i2.299
[14] Suliztia, M. L., & Fauzan, A. (2019). Comparing Naive Bayes, K-Nearest Neighbor, and Neural Network Classification Methods of Seat Load Factor in Lombok Outbound Flights. Jurnal Matematika, Statistika Dan Komputasi, 16(2), 187. https://doi.org/10.20956/jmsk.v16i2.7864
[15] Suryadewiansyah, M. K., & Tju, T. E. E. (2022). Naïve Bayes dan Confusion Matrix untuk Efisiensi Analisa Intrusion Detection System Alert. Jurnal Nasional Teknologi Dan Sistem Informasi, 8(2), 81–88. https://doi.org/10.25077/teknosi.v8i2.2022.81-88
[16] Wardhana, A. W., Patimah, E., Shafarindu, A. I., Siahaan, Y. M., Haekal, B. V., & Prasvita, D. S. (2021). Klasifikasi Data Penjualan pada Supermarket dengan Metode Decision Tree. Senamika, 2(1), 660–667. https://conference.upnvj.ac.id/index.php/senamika/article/view/1389
[17] Winantu, A., & Khatimah, C. (2023). Perbandingan Metode Klasifikasi Naive Bayes Dan K-Nearest Neighbor Dalam Memprediksi Prestasi Siswa. INTEK : Jurnal Informatika Dan Teknologi Informasi, 6(1), 58–64. https://doi.org/10.37729/intek.v6i1.3006
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Syarifuddin
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
