PREDICTION OF HEALTH INSURANCE PRODUCT PURCHASE ALLOCATION IN VARIOUS INDUSTRIES IN INDONESIA USING THE RANDOM FOREST METHOD

Authors

  • Hendra Achmadi Fakultas Ekonomi dan Bisnis, Universitas Pelita Harapan
  • Eduard Ary Binsar Naibaho Fakultas Ekonomi dan Bisnis, Universitas Pelita Harapan, Indonesia
  • Sandra Sembel (SINTA ID: 6807168) Faculty of Economics and Business, Universitas Pelita Harapan
  • Herlina Lusmeida Fakultas Ekonomi dan Bisnis, Universitas Pelita Harapan

DOI:

https://doi.org/10.19166/ms.v4i2.8752

Keywords:

Premium Prediction, CRIPS-DM, Random Forest

Abstract

The objective of this research is identifying which industry can absorb the product of wealth management such as health insurance. Secondly is to identify what the most factors important to determine closing the health insurance premium. The life insurance penetration and density in Indonesia is the lowest level among the Asian country, so the data population in this research is from 38 different companies from different types of industries with 143 data sample, by using the purposive sampling. Most factors which influence the purchasing of health insurance are Listrik, Industry, domicile, age and position, whether the industry that the most contribution for the health insurance sales is banking and education industry. The methodology that is used in this research 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 continue to data preparation, 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 58%. From the result of the projection closing health insurance from each industry, it can be concluded that the most industry that closed the health insurance is Banking Industry, the second is from insurance and the third is education and the next is education, retail, health, manufacturing and finance, hospitality, legal, publishing, technology and government and service industries.

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Published

2024-09-26

Issue

Vol. 4 No 2 September 2024

Section

Articles

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