Classification of Payment Patterns for Toyota Car Sales Using the Decision Tree Algorithm
Keywords:
Decision Tree, Classification, Car Sales, Payment Patterns, Sales StrategyAbstract
Car sales represent a business sector highly dependent on the implementation of appropriate payment strategies to enhance customer satisfaction and operational efficiency. This study aims to classify payment patterns in Toyota car sales using the Decision Tree algorithm. Historical sales data were utilized to identify various attributes influencing payment methods, such as cash, credit, or leasing.
Through processes of preprocessing, feature selection, and model training, the Decision Tree algorithm successfully established clear classification patterns based on variables such as payment type, gender, car type, and car category. The research findings indicate that the Decision Tree method not only provides a high level of accuracy in classifying payment patterns but also produces models that are easily interpretable by business decision-makers. Thus, the implementation of this classification technique is expected to assist companies in designing more effective and targeted sales and promotional strategies.
Downloads
References
Atsila Imanda, R., Suroso, S., Fauzi, A., Simanjuntak, H. F., Azizah, Z., Destianty, A., Mutmainnah, A. Z., & Zarka Zahira Shaffa, G. (2024). Pengaruh Data Warehouse Terhadap Pengambilan Keputusan. Jurnal Portofolio : Jurnal Manajemen Dan Bisnis, 3(1), 31–39. https://www.jurnalprisanicendekia.com/index.php/portofolio/article/view/282
Bhaskara, M. R. S., & Wasesa, M. (2023). Enhancing Car Showroom Profitability through K-Means Clustering for Customer Segmentation. International Journal of Current Science Research and Review, 6.
Handika, I. P. S. (2020). Perancangan datawarehouse dan teknologi business intelligence untuk analisa penjualan pada perusahaan retail PT. ABC. Rabit : Jurnal Teknologi Dan Sistem Informasi Univrab, 5(2), 76–85. https://doi.org/10.36341/rabit.v5i2.1309
Hidayat, M. K., & Putri, D. (2024). Business Intelligence Untuk Memantau Perkembangan Harga Pangan Provinsi DKI Jakarta. Jurnal Infortech, 6(1), 30–36. https://doi.org/10.31294/infortech.v6i1.21832
Jaras, V. (2024). A model for determining the market price of a car (Doctoral dissertation, Vilniaus universitetas).
Jayadeera, T. P., & Jayamanne, D. J. (2019, November). Fair price prediction system for used cars in Sri Lanka using machine learning and robotic process automation. In 2019 International Conference On Business Innovation (ICOBI), Colombo.
Maulida, R., Haryadi, D., Ratu Counsela, S., & Rahmania Az Zahra, N. (2024). Implementasi Algoritma K-Means dan Rapidminer untuk Clustering Data Kemiskinan Provinsi Banten. Journal of Informatics and Communications Technology, 6(1), 17–32.
Msiza, Z. A., & Owolawi, P. A. (2023, December). Advancing Used Car Price Prediction in South Africa: An Empirical Examination of Machine Learning Techniques. In International Conference on Artificial Intelligence and its Applications (pp. 189-196).
Pradhana, A. H., Irfa, M., Ali, A., Ristyawan, A., & Daniati, E. (2024). Penerapan Regresi Linear Menggunakan RapidMiner Untuk Memprediksi Penjualan dan Persediaan. 8, 291–297.
Pratama, A. A., Pitoyo, B. S., Fauzi, A., Rivaldi, A., & Andini, D. A. (2024). Peran Implementasi Intelijen Bisnis Dalam Menentukan Peramalan Bisnis. Jurnal Portofolio: Jurnal Manajemen Dan Bisnis, 3(1), 60–70.
Pudaruth, S. (2014). Predicting the price of used cars using machine learning techniques. Int. J. Inf. Comput. Technol, 4(7), 753-764.
Rusmee, K., & Chumuang, N. (2019, October). Predicting system for the behavior of consumer buying personal car decision by using SMO. In 2019 14th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) (pp. 1-6). IEEE.
Tucker, C. S., Hoyle, C., Kim, H. M., & Chen, W. (2009, January). A comparative study of data-intensive demand modeling techniques in relation to product design and development. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. 49026, pp. 371-383).
Varol, S. (2016). Analyzing brand loyalty in automotive sector using the hidden Markov model and support vector machine. Lamar University-Beaumont.
Wahono, S., & Ali, H. (2021). Peranan Data Warehouse, Software Dan Brainware Terhadap Pengambilan Keputusan (Literature Review Executive Support Sistem for Business). Jurnal Ekonomi Manajemen Sistem Informasi, 3(2), 225–239. https://doi.org/10.31933/jemsi.v3i2.781
