Sistem Klasifikasi Kelayakan Debitur Lembaga Perkreditan Desa Menggunakan Algoritma C4.5 dan Bagging
| Main Authors: | Ardiyanti, Ni Putu Novia, Raharja, Made Agung, Astuti, Luh Gede, Mogi, I Komang Ari, Putri, Luh Arida Ayu Rahning, Darmawan, I Dewa Made Bayu Atmaja |
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| Format: | Article info application/pdf eJournal |
| Bahasa: | ind |
| Terbitan: |
Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University
, 2022
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| Online Access: |
https://ojs.unud.ac.id/index.php/JLK/article/view/89014 https://ojs.unud.ac.id/index.php/JLK/article/view/89014/48838 |
Daftar Isi:
- LPD Penarukan is one of the financial institutions that provides credit loans to people in the Penarukan traditional village area, Bali Province. This study aims to assist credit officers in classifying loans submitted by prospective debtors based on analysis of character, capacity, capital, collateral, and economic conditions by developing a credit classification system with a case study on LPD Penarukan using the C4.5 algorithm with bagging technique. Based on the results of research and testing that have been carried out, the system built is able to carry out the process of classifying credit data indicated by the results of usability testing giving the learnability component has an average value of 78.2%, the memorability component has an average value of 74.07%. The efficiency component gets an average value of 83.70%, the error component gets an average of 82.22%, the satisfaction component gets an average value of 83.89%. Tests were also carried out by calculating the accuracy and F1 score using the C4.5 algorithm with the bagging technique which resulted in an accuracy value of 81.87% and an F1 score of 89.62%.
- LPD Penarukan merupakan salah satu lembaga keuangan yang memberikan pinjaman kredit kepada masyarakat di kawasan desa adat Penarukan, Provinsi Bali. Penelitian ini bertujuan untuk membantu petugas kredit dalam mengklasifikasikan kredit yang diajukan oleh calon debitur berdasarkan analisis character, capacity, capital, collateral, dan condition of economic dengan mengembangkan sistem klasifikasi kredit dengan studi kasus pada LPD Penarukan menggunakan algoritma C4.5 dengan bagging teknik. Berdasarkan hasil penelitian dan pengujian yang telah dilakukan, sistem yang dibangun mampu melakukan proses pengklasifikasian data kredit yang ditunjukkan dengan hasil pengujian usability memberikan komponen learnability memiliki nilai rata-rata 78,2%, komponen memoriability memiliki nilai rata-rata 74,07%. Komponen efisiensi mendapat nilai rata-rata sebesar 83,70%, komponen error mendapat rata-rata 82,22%, komponen kepuasan mendapat nilai rata-rata 83,89%. Pengujian juga dilakukan dengan menghitung akurasi dan skor F1 menggunakan algoritma C4.5 dengan teknik bagging yang menghasilkan nilai akurasi 81,87% dan skor F1 89,62%.