Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Akademika
Cendana, Gina
Unknown Affiliation
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
KLASIFIKASI DATA NASABAH KREDIT PINJAMAN MENGGUNAKAN DATA MINING DENGAN METODE K-MEANS PADA MEGA CENTRAL FINANCE: Active;Data Mining;K-Means;Passive;Repeat Order;Rapid Miner. Limia Budiarti, Rike; Cendana, Gina
JURNAL AKADEMIKA Vol 14 No 2 (2022): Jurnal Akademika
Publisher : LP2M Universitas Nurdin Hamzah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53564/akademika.v14i2.866

Abstract

Mega Central Finance (MCF) Group is a company under CT.Corpora. The Mega Central Finance (MCF) Group company functions as a company engaged in financing and credit, located in Muara Bulian. The purpose of this study using the "K-means" data mining method is to obtain data reports on customers who are entitled to receive loans from the Mega Central Finance (MCF) Group. Clustering includes inputting data from customers who apply for loans, then entering the registration process to enter the customer's name, the calculation process using RapidMiner. It takes several variables used in clustering, namely the variable "Amount of Loans, Term, Income, Number of Pickup Vehicles". The results of clustering obtained three clusters, namely cluster 1 there are 7 active customer data which has a very small number of clusters. Cluster 2 contains 93 passive customer data which has a cluster number with the highest number of customer data from cluster 1 and cluster 3. Cluster 3 contains 50 repeat order customer data which has a moderate number of clusters. Finally, the results from the three clusters above are obtained.
Co-Authors Limia Budiarti, Rike