APPLICATION OF K-MEANS CLUSTERING ALGORITHM TO ANALYZE INSURANCE COMPANY BUSINESS (CASE STUDY: PT. JASINDO INSURANCE)

Authors

  • Endang Elni Arbaeti STMIK Kaputama, Binjai, Sumatera Utara
  • Akim Manaor Hara Pardede STMIK Kaputama, Binjai, Sumatera Utara
  • Lina Arliana Nur Kadim STMIK Kaputama, Binjai, Sumatera Utara

Keywords:

Datamining, Algoritma K-mens, Asuransi

Abstract

Asuransi Jasindo is an insurance company that accepts insurance coverage, both directly and indirectly, with the ownership of 1 series A dwiwarna share owned by the Republic of Indonesia and 424,999 Series B shares owned by PT Bahana Pembinaan Usaha Indonesia (Persero). PT Asuransi Jasa Indonesia or known as Asuransi Jasindo, has a qualified, long and mature experience in the field of general insurance even since the colonial era. This experience provides its own pioneering value for the existence and growth of Asuransi Jasindo's performance to date, so that it has succeeded in gaining public trust both at home and abroad. PT Asuransi Jasa Indonesia has several products and options in choosing which insurance is needed by customers, both agriculture, health, education and many more. Due to the large amount of insurance data, it is difficult for companies to process existing data and information. Therefore the author wants to create an application that can help companies process and classify existing insurance user data to produce information that can make it easier for insurers to provide better service to meet insurance user satisfaction using the K-Means Algorithm method. Of the 1089 data analyzed, the results that were most widely used were insurance data with ages 26-35 years, located in the Medan city sub-district with the type of insurance used, namely Jasindo Micro insurance.

References

Agneresa, Lia Hananto, A., Shofiah Hilabi, S., Hananto, A., & Tukino. (2022). Strategi Promosi Penerapan Data Mining Mahasiswa Baru Dengan Metode K-Means Clustering. Jurnal Manajemen Dan Sistem Informasi, 02(02), 25–34.

Depkes RI. 2009. Profil Kesehatan Tahun 2008. Departemen Kesehatan RI. Jakarta

Janner Lubis, D., & Tamam, M. B. (2022). Penerapan K-Means Untuk Pengelompokan Beasiswa Santri di Pondok Pesantren Miftahul Huda Bogor. Jurnal Ilmiah Tenologi - Informasi & Sains (TEKNOIS), 12, 7–20. https://doi.org/10.36350/jbs.v12i1

Marsellus oton Kadang, . S.Kom., M.T. (2021). Algoritma dan Pemrograman (A. K. Muzakir, Ed.; 1st ed.). Humanities GEnius.

Muslihudin, M., & Oktafianto. (2016). Analisis dan Perancangan Sistem Informasi (1st ed., Vol. 1). CV. Andi Offset.

Relita Buaton, Zarlis, M., & Yasin, V. (2021). Konsep Data Mining Dalam Implementasi (1st ed., Vol. 1). Mitra WacanaMedia.

Sonang S, Purba AT, Pardede FOI. Pengelompokan Jumlah Penduduk Berdasarkan Kategori Usia dengan Metode K-Means. J Tek Inf dan Komput. 2019;2(2):166.

Sulistiyawati, A., & Supriyanto, E. (2021). Implementasi Algoritma K-means Clustring dalam Penetuan Siswa Kelas Unggulan. Jurnal TEKNO KOMPAK, 15(2).

Tjolleng, A., & Nusantara, B. (2017). Pengantar pemrograman MATLAB (1st ed., Vol. 1). PT. Alex Media Komputindo. https://www.researchgate.net/publication/334945947

Additional Files

Published

2023-11-23

How to Cite

Elni Arbaeti, E., Hara Pardede, A. M. ., & Nur Kadim, L. A. . (2023). APPLICATION OF K-MEANS CLUSTERING ALGORITHM TO ANALYZE INSURANCE COMPANY BUSINESS (CASE STUDY: PT. JASINDO INSURANCE). Journal of Mathematics and Technology (MATECH), 2(2), 173–192. Retrieved from http://journal.binainternusa.org/index.php/matech/article/view/161