Prediction of Bond Planning Based on Customer Characteristics in Indonesia Using the Random Forest Algorithm
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Bond Prediction##common.commaListSeparator## CRIPS-DM##common.commaListSeparator## Random Forest要旨
This research aims to identify industries receptive to wealth management products like bond insurance and to pinpoint the key factors influencing bond insurance premium closures. Given Indonesia's low life insurance penetration and density compared to other Asian countries, the study uses a purposive sample of 199 data points from 38 companies across various industries. This research found that the most influential factors in bond purchases are domicile, the second car, industry type, job position , housing type . The methodology that is used in thisresearch is called CRIPS-DM (Cross Industrial Standards Program Data Mining). The first steps what is the purpose of the organization, and the second is what data that needed, and then continue to data preparation, after that modeling, after modeling, it will make an interpretation of the result, and the final steps is deployment, it will plan how it will be implemented in the real world, and the accuracy score from this model is 98 %. From the result of the projection closing bond insurance from each industry, it can be concluded that the most industry that closed the bond insurance is Banking Industry, the second is from insurance and the third is education and the next is education, retail, bond, manufacturing and finance, hospitality, legal, publishing, technology and government and service industries.
##submission.citations##
Academy, R. (2024). Data Science. Rapidminer Academy. https://academy.rapidminer.com/learning-paths/data-science-professional-with-rapidminer
Charbuty, B., & Abdulazeez, A. (2021). Classification Based on Decision Tree Algorithm for Machine Learning. Journal of Applied Science and Technology Trends, 2(01), 20–28. https://doi.org/10.38094/jastt20165
Duarte, V., Zuñiga-Jara, S., & Contreras, S. (2019). Machine Learning and Marketing: a literature review. https://ssrn.com/abstract=4006436
Gkikas, D. C., Theodoridis, P. K., & Beligiannis, G. N. (2022). Enhanced Marketing Decision Making for Consumer Behaviour Classification Using Binary Decision Trees and a Genetic Algorithm Wrapper. Informatics, 9(2). https://doi.org/10.3390/informatics9020045
Lopus, J. S., Amidjono, D. S., & Grimes, P. W. (2019). Improving financial literacy of the poor and vulnerable in Indonesia: An empirical analysis. International Review of Economics Education, 32(May), 100168. https://doi.org/10.1016/j.iree.2019.100168
Muhajir, M., & Widiastuti, J. (2022). Random Forest Method Approach to Customer Classification Based on Non-Performing Loan in Micro Business. Jurnal Online Informatika, 7(2), 177–183. https://doi.org/10.15575/join.v7i2.842
Müller, A. C., & Guido, S. (2017). Introduction to Machine Learning with Python A Guide For Data Scientists Introduction to Machine Learning with Python.
OJK, O. J. K. (2023). Draft Roadmap Pengembangan Perasuransian Indonesia. Departemen Pengaturan Dan Pengembangan IKNB, OJK, 1–63. www.ojk.go.id.
Ong, A. K. S., Cordova, L. N. Z., Longanilla, F. A. B., Caprecho, N. L., Javier, R. A. V., Borres, R. D., & German, J. D. (2023). Purchasing Intentions Analysis of Hybrid Cars Using Random Forest Classifier and Deep Learning. World Electric Vehicle Journal, 14(8). https://doi.org/10.3390/wevj14080227
Singgalen, Y. A. (2024). Implementation of CRISP-DM for Social Network Analysis ( SNA ) of Tourism and Travel Vlog Content Reviews. Jurnal Media Informatika Budidarma, 2024, 8, 572–583. https://doi.org/10.30865/mib.v8i1.7323
Winston, P. H. (2017). On Computing Machinery and Intelligence. Boston Studies in the Philosophy and History of Science, 324, 265–278. https://doi.org/10.1007/978-3-319-53280-6_11
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