Penerapan Data Mining Pada Prediksi Kelayakan Pemohon Kredit Mobil Dengan K-Medoids Clustering
DOI:
https://doi.org/10.30865/klik.v1i4.153Keywords:
Credit; Cars; Data Mining; Clustering; K-MedoidsAbstract
K-Medoids is included in partitioning clustering, where each data must be included in a certain cluster and it is possible for each data included in a particular cluster at one stage of the process, at the next stage it moves to another cluster. The definition of credit is the ability to carry out a purchase or make a loan with a promise, payment will be made at the agreed period. The definition of credit is the ability to carry out a purchase or make a loan with a promise, payment will be made at the agreed time. According to a survey conducted by Gaikindo (Association of Motor Vehicle Industries) in 2015, car purchases on credit reached 85%. This is due to decreased purchasing power, thus encouraging more consumers to buy cars using installments, aka credit. Therefore it is necessary to measure the feasibility of a customer in crediting a car. The goal is to determine the customer's ability to credit a car so that there are no losses on both parties, and reduce credit errors that have not been optimal in permitting credit cars. The policies taken must of course have relevance and be supported by the knowledge that comes from the available data and Data Mining is one of the good methods in providing a clustering pattern model and good for grouping. It is hoped that this research can provide benefits to companies in reducing errors in making decisions on credit applicants at the credit evaluation stage which greatly affects the company's cash flow.
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