Penerapan Metode K-Means Dan Harmony Search Untuk Seleksi Fitur Pada Penentuan Credit Score

Puspitasari, Anita (2025) Penerapan Metode K-Means Dan Harmony Search Untuk Seleksi Fitur Pada Penentuan Credit Score. Undergraduate thesis, UPN Veteran Jawa Timur.

[img] Text (Cover)
20081010015.-cover.pdf

Download (2MB)
[img] Text (Bab 1)
20081010015.-bab1.pdf

Download (208kB)
[img] Text (Bab 2)
20081010015.-bab2.pdf
Restricted to Repository staff only until 5 January 2028.

Download (357kB)
[img] Text (Bab 3)
20081010015.-bab3.pdf
Restricted to Repository staff only until 5 January 2028.

Download (666kB)
[img] Text (Bab 4)
20081010015.-bab4.pdf
Restricted to Repository staff only until 5 January 2028.

Download (2MB)
[img] Text (Bab 5)
20081010015.-bab5.pdf

Download (285kB)
[img] Text (Daftar Pustaka)
20081010015.-daftarpustaka.pdf

Download (171kB)
[img] Text (Lampiran)
20081010015.-lampiran.pdf
Restricted to Repository staff only

Download (454kB)

Abstract

Credit is a payment mechanism provided by financial institutions in the form of loan funds. Creditworthiness assessment, commonly referred to as a credit score, plays a crucial role in estimating lending risks to customers. The main challenge in developing a credit scoring model lies in the large number of transaction features that are irrelevant and redundant, which leads to high computational costs. This study proposes a feature selection method using the K-Means and Harmony Search algorithms as a solution to this problem. K-Means is employed to analyze distance based to identify representative features, while Harmony Search is utilized to determine the most relevant feature combinations. The results of this research indicate that the proposed method not only improves prediction accuracy but also enhances computational efficiency. The model achieved a performance of MAPE = 3.2%, RMSE = 25, and R² = 0.86, with a training computation time of 0.0165 seconds and prediction time of 0.0041 seconds. These results demonstrate the effectiveness of combining K-Means and Harmony Search methods in simplifying data for credit scoring models without compromising data quality. Keywords: Credit Score, Feature Selection, K-Means, Harmony Search.

Item Type: Thesis (Undergraduate)
Contributors:
ContributionContributorsNIDN/NIDKEmail
Thesis advisorMandyartha, Eka PrakarsaNIP19880525 201803 1 001eka_prakarsa.fik@upnjatim.ac.id
Thesis advisorHaromainy, Muhammad Muharrom AlNIP19950601 202203 1 006muhammad.muharrom.if@upnjatim.ac.id
Subjects: T Technology > T Technology (General) > T58.6-58.62 Management Information Systems
Divisions: Faculty of Computer Science > Departemen of Informatics
Depositing User: Anita Anita Anita Puspitasari
Date Deposited: 05 Jan 2026 07:28
Last Modified: 05 Jan 2026 07:28
URI: https://repository.upnjatim.ac.id/id/eprint/48554

Actions (login required)

View Item View Item