PIKSEL (Penelitian Ilmu Komputer Sistem Embedded dan Logic)
Vol 1, No 2 (2013): PIKSEL (Penelitian Ilmu Komputer Sistem Embedded dan Logic)

PREDIKSI PINJAMAN KREDIT DENGAN SUPPORT VECTOR MACHINE DAN K-NEAREST NEIGHBORS PADA KOPERASI SERBA USAHA

Iriadi, Nandang (Unknown)
Leidiyana, Henny (Unknown)



Article Info

Publish Date
29 Nov 2013

Abstract

Cooperatives as a form of organization that are important in promoting economic growth . Cooperatives be an alternative for people to get funds in an effort to improve their quality of life , day-to- day needs and develop the business . No doubt , lend funds to member cooperatives will surely emerge problems , such as members of the borrower paying the overdue installment of funds , misuse of funds for other purposes , the customer fails to develop its business so as to result in cooperative funds do not flow or it can lead to bad credit . In this research will be carried out loans prediction using data mining classification Support Vector Machine and k - Nearest Neighbors were then conducted a comparison of both methods . From the test results to measure the performance of both methods using cross validation , confusion matrix and ROC curves is known that Support Vector Machine has an accuracy value of 92.67 % followed by k -Nearest Neighbors, which has a value of 88.67 % accuracy . Thus the Support Vector Machine method is included in Verry Good Clasification because it has the accuracy of 92.67 % .Keywords: comparative, Support Vector Machines, k-Nearest Neighbors, Credit Analysis Cooperatives as a form of organization that are important in promoting economic growth . Cooperatives be an alternative for people to get funds in an effort to improve their quality of life , day-to- day needs and develop the business . No doubt , lend funds to member cooperatives will surely emerge problems , such as members of the borrower paying the overdue installment of funds , misuse of funds for other purposes , the customer fails to develop its business so as to result in cooperative funds do not flow or it can lead to bad credit . In this research will be carried out loans prediction using data mining classification Support Vector Machine and k - Nearest Neighbors were then conducted a comparison of both methods . From the test results to measure the performance of both methods using cross validation , confusion matrix and ROC curves is known that Support Vector Machine has an accuracy value of 92.67 % followed by k -Nearest Neighbors, which has a value of 88.67 % accuracy . Thus the Support Vector Machine method is included in Verry Good Clasification because it has the accuracy of 92.67 % .Keywords: comparative, Support Vector Machines, k-Nearest Neighbors, Credit Analysis

Copyrights © 2013






Journal Info

Abbrev

piksel

Publisher

Subject

Computer Science & IT Decision Sciences, Operations Research & Management

Description

Jurnal PIKSEL diterbitkan oleh Universitas Islam 45 Bekasi untuk mewadahi hasil penelitian di bidang komputer dan informatika. Jurnal ini pertama kali diterbitkan pada tahun 2013 dengan masa terbit 2 kali dalam setahun yaitu pada bulan Januari dan September. Mulai tahun 2014, Jurnal PIKSEL ...