PREDICTION MODEL OF AGRICULTURE LOAN REPAYMENT BY FARMERS USING LOGISTIC REGRESSION AND DECISION TREE

• Main Author: Kirana Prima Satyasanti, Maria
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/77558
• Institution: Institut Teknologi Bandung

Agriculture sector in Indonesia holds significant potential. Recognizing this potential, several companies in Indonesia have begun offering agricultural loans to farmers. However, many farmers still struggle to repay their loans. This has a negative impact on the financial health of these companies. One effective strategy to enhance the loan distribution system's effectiveness is to develop a predictive model for farmers' repayment ability. This model can be constructed using machine learning techniques to assess farmers' repayment capacity based on their individual characteristics. The objective of this study is to create logistic regression and decision tree model by using data related to farmers' loan repayment status and their characteristics. The best model will be selected based on its accuracy, Receiver Operating Characteristic - Area Under Curve score, and other advantages. This study will determine the most suitabel model to predict farmers' repayment ability that can improve the loan distribution system in the agricultural sector.