Abstract
Insurance is a mechanism of protection or protection from the risk of loss by transferring the risk to another party. Sometimes a product that has just emerged becomes a product that is superior in terms of sales, so that interest in a product is not absolutely measured from the year the product was released. The constraint factors include the marketing of the product when it was launched. Offering products with low premiums along with the benefits that customers want. However, insurance companies still have difficulty in classifying superior products that are in great demand by prospective customers. For this reason, a technique for grouping insurance products is needed to make it easier for companies to see superior products and choose products that suit the needs of their customers. Analyzing and processing data using the K-Means method in the clustering of insurance products is the aim of this study. The application of the K-Means algorithm is to help calculate the purity value from the results of the clustering carried out so that the clustering of insurance products is in accordance with the needs of its customers. The application of the K-Means method with clustering techniques for data mining produces information on insurance products that are more attractive to potential customers. This is very appropriate in grouping data types because it is easier to implement and its application can filter quickly and precisely. Calculations using the K-Means method with a data sample of 55 customers obtained 3 clusters, namely cluster 1 for fire insurance which has 30 customers, cluster 2 for accident insurance 24 people and cluster 3 for health insurance 1 person.
Highlights
Abstrak−Asuransi merupakan mekanisme proteksi atau perlindungan dari resiko kerugian dengan cara mengalihkan resiko pada pihak lain
−Insurance is a mechanism of protection or protection from the risk
just emerged becomes a product that is superior in terms of sales
Summary
Asuransi adalah lembaga ekonomi dengan tujuan mengurangi resiko, menggabungkan unit-unit yang mempunyai resiko sama atau hampir sama dalam jumlah memadai supaya probabilitasnya dapat diramalkan dan disalurkan ke unit yang mengalami resiko[1]. Oleh karena itu pemanfaatan analisis big data dapat membantu perusahaan menjadi lebih efisien dan meningkatkan kepuasan nasabah terhadap layanan perusahaan asuransi. Melakukan analisis dan mengolah data dengan metode K-Means dalam klasterisasi produk asuransi merupakan tujuan penelitian ini. Penerapan algoritma K-Means ini untuk membantu perhitungan nilai kemurniannya dari hasil clustering yang dilakukan sehingga klasterisasi produk asuransi sesuai dengan kebutuhan nasabahnya. Penerapan metode K-Means dengan teknik clustering untuk data mining akan menghasilkan informasi produk asuransi apa yang lebih diminati para calon nasabah. Metode algoritma K-Means dapat di terapkan untuk mengelompokan data nilai pertanggungan, premi dan klaim berdasarkan clustering dengan nilai terendah sedang dan tertinggi berdasarkan ketentuan perusahaan. Penawaran dari perusahaan asuransi menjadi daya tarik bagi calon nasabah yang ingin menggunakan jasa asuransi diantaranya asuransi jiwa, kesehatan, kendaraan, properti/bangunan dan masih banyak lagi. Sehingga nasabah menganggap asuransi itu tidak bermanfaat dan bahkan tidak melanjutkan pembayaran/berhenti ditengah jalan [3]
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