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All Journal JSI: Jurnal Sistem Informasi (E-Journal) CommIT (Communication & Information Technology) Telematika JUSIFO : Jurnal Sistem Informasi POSITIF InfoTekJar (Jurnal Nasional Informatika dan Teknologi Jaringan) Jurnal TAM (Technology Acceptance Model) Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Teknologi Informasi MURA Jurnal Informatika Global Jurnal Tekno Kompak Journal of Information Systems and Informatics Jurnal Pengembangan Sistem Informasi dan Informatika Jurnal Bina Komputer Jurnal Pengabdian Masyarakat Information Technology (JPM ITech) Jurnal Teknologi Informasi Mura
Dedy Syamsuar
Universitas Bina Darma
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Library & Information Science Social Sciences Other

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Journal : Jurnal Sisfokom (Sistem Informasi dan Komputer)

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Prediksi Customer Retention Perusahaan Asuransi Menggunakan Machine Learning Riyan Pratama; Muhammad Izman Herdiansyah; Dedy Syamsuar; Ahmad Syazili
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 12, No 1 (2023): MARET
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i1.1507

Abstract

The application of data mining technique currently is widely used in supporting business activity, especially for insurance company which has to analyze a big number of customer data. The insurance company has to predict its new customer acquisition as well as maintain its existing customers. This paper is focused on how we support insurance companies, especially PT. XYZ, to analyze their customers’ characteristics data using the best data mining algorithm technique. The research aims to analyze existing customer data and to predict as well as find optimal patterns of how many of their customers will extend their insurance policies, and how many will not. We also explore the customer retention rate discovering the optimal solution for the company. We applied 4 different algorithms were applied, i.e. support vector machine algorithm, decision tree, k-NN, and random forest algorithm, comparing the results and finding a better solution. From the analysis, we found that the random forest algorithm provides better results in predicting the status of the insurance policy extension of current customers, with an accuracy rate of 91.08% and AUC value of 0.962. This result is quite good for PT XYZ, and could be enhanced in the future by applying a good strategy to increase their customer renewal ratio.
Co-Authors Agus Setiawan Ahmad Syazili Andryani, Ria Beni Brahara Darius Antoni Dendi dendi Irawan Dendi Triadi Edi Surya Negara Endang Etriyanti Enggi Ardius Fitrianto Puja Kesuma Hermizahadiwidastra Hermizahadiwidastra Indri Endang Lestari Linda Atika M. Izman Herdiansyah Masroni Dedi Kiswanto Mery Sintia Muhamad Akbar Muhammad Akbar Muhammad Izman Herdiansyah Muhammad Wahyudi Netti Herawati Nopalia Nopalia Rahma Fitriyani Riyan Pratama Tri Basuki Kurniawan Veradilla Amalia Widya Cholil Yayuk Ike Meilani Yepi Kusmeta Yesi Novaria Kunang Zulkifli Harahap