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All Journal Jurnal INKOM Jurnal Edukasi dan Penelitian Informatika (JEPIN) Scientific Journal of Informatics Jurnal Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Informatika Universitas Pamulang JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat)
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Balai Pengkajian Teknologi Pertanian Kalimantan Barat Jalan Budi Utomo No. 45, Siantan Hulu, Kotak Pos 6150, Pontianak 78061, Kalimantan Barat, Indonesia
Humanities Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Public Health Other

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Journal : Jurnal%20Informatika%20Universitas%20Pamulang

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Perbandingan Algoritma Klasifikasi K-Nearest Neighbor, Random Forest dan Gradient Boosting untuk Memprediksi Ketertarikan Nasabah pada Polis Asuransi Kendaraan Diantika, Sri; Subekti, Agus; Nalatissifa, Hiya; Lase, Mareanus
Jurnal Informatika Universitas Pamulang Vol 6, No 3 (2021): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v6i3.9419

Abstract

An insurance policy provides coverage for compensation for specified loss, damage, illness, or death in exchange for premium payments. Likewise for vehicle insurance, every year the customer needs to pay a premium to the insurance company so that if an accident occurs that is not profitable for the vehicle, the insurance company provides compensation to the customer. The purpose of this research is to classify the health insurance cross-sell prediction dataset so that certain patterns or relationships can be found between the data to become valuable information and build a model to predict whether policyholders (customers) from the previous year will also be interested in insurance. Vehicles provided by the company. The researcher uses the K-nearest neighbor classification algorithm, Random Forest, and gradient boosting classifier as well as Python data mining tools. After doing the research, it was found that the K-nearest neighbor classification algorithm produces a higher accuracy of 91%, when compared to the Random Forest algorithm which is 87% and the boosting classifier algorithm is 88% in classifying customer interest in taking a vehicle insurance policy.
Co-Authors A.A. Ketut Agung Cahyawan W Akhmad Syukron Ari Setiawan Aryono Adhi, Aryono Diantika, Sri Faisal, Anas Hidayat Teguh Wiyono Hiya Nalatissifa Kahar Muzakhar Khultsum, Umi Kusumah, Maulana S. Lase, Mareanus Lukman, Arif Nurul Priyantari Supriyadi Supriyadi Suryanto, Wiwit Suyoto Suyoto Wahyudi Wahyudi Winarsa, Rudju