Open AccessKlasifikasi Data Aging Tunggakan Nasabah Menggunakan Metode Decision Tree Pada ULaMM Unit Kolaka
Author(s) -
Sarimuddin Sarimuddin,
Jayanti Yusmah Sari,
Muhammad Mail,
Muh. Ariyandhi Masalu,
Reski Surya Aristika,
Nurfagra Nurfagra
Publication year - 2020
Publication title -
informal
Language(s) - English
Resource type - Journals
ISSN - 2503-250X
DOI - 10.19184/isj.v5i1.16964
Subject(s) - decision tree , decision tree learning , debtor , statistics , computer science , tree (set theory) , mathematics , data mining , business , finance , debt , mathematical analysis , creditor
This study aims to classify aging of loan data using the decision tree method based on plafond, outstanding principal, and the amount of loan. The subjects in this study were the debtor of ULaMM (Unit Layanan Modal Mikro), unit of Kolaka, PT. PNM (Persero) Kendari Branch. The number of samples used is 100 data debtors. Based on the results of the research conducted, it was found that the classification analysis using the Decision Tree has an accuracy rate of 95.00%, while the classification analysis using the Gradient Boosted Tree has an accuracy level of 90.00%. From the results of the analysis that has been done, it can be concluded that for the data in this study, the classification method using the Decision Tree is better than the Gradient Boosted Tree method.