Implementasi Metode K-Means Untuk Memprediksi Status Kredit Macet
DOI:
https://doi.org/10.30865/json.v4i3.5953Keywords:
Data Mining, Clustering, Default Credit, Repeat Order, K-MeansAbstract
A credit card is one of the legal payment media owned by a bank in making a payment transaction within the agreed timeframe. In particular, credit services are provided by institutions or bodies that have the authority to distribute funds in the form of financial assistance to individuals and groups. However, in practice there are bound to be obstacles, especially during payback periods that often occur, such as when a customer wants to submit a Repeat Order or apply for funds again. Obstacles that are usually encountered in the process of granting credit are substandard credit and bad credit payments. Before PT Esta Dana Ventura wants to decide to approve applications for re-granting credit cards from prospective repeat order customers, a classification of assessment criteria is needed to determine the feasibility of granting credit to prospective repeat order customers. This study made the decision to use Data mining clustering classification with Rapidminer tools as a tool to obtain accurate results by processing data using the K-Means clustering method to help PT. Esta Dana Ventura in analyzing potential non-performing loans. By comparing survey data for Repeat Order candidates with previous credit granting data and classifying them in the form of bad or non-bad credit classifications.From the results of research using the k-means method it can produce grouping data into 3 criteria, namely (C0) 69 data with current customers, (C1) 3 data with very current customers, and (C2) 52 data with Bad customers..
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