Credit Scoring Model for Farmers using Random Forest

Kharida Aulia Bahri; Yeni Herdiyeni; Suprehatin Suprehatin

doi:10.29207/resti.v7i1.4583

Kharida Aulia Bahri IPB University
Yeni Herdiyeni IPB University
Suprehatin Suprehatin IPB University

DOI: https://doi.org/10.29207/resti.v7i1.4583

Keywords: agriculture, credit scoring, farmer, land productivity, random forest

Abstract

One of the problems faced by farmers in Indonesia is capital. Based on Indonesian Central Statistics Agency survey results, the number of farmers who borrow capital from formal institutions such as banks is still small. This is because the process of applying for loans at banks is lengthy, farmers are considered high-risk and unbankable, and the rating of the agricultural sector is unattractive to banks. This study aims to determine the attributes and design a model of agricultural credit assessment. This study uses secondary data related to bank credit ratings and land productivity from banks in the Telagasari sub-district in 2018–2020 and Cipayung sub-district in 2020. Data were analyzed using random forests. The research process includes four stages: data collection, data pre-processing, model building, and model analysis and evaluation. This study produced five important variables that are relevant to farmers: planting costs, sales, land productivity, total production, and land area. The model built produces the most optimal accuracy of 83% with an AUC score of 81%. Based on the AUC performance classification, it can be concluded that the model that has been made is good at predicting the credit status of farmers because the AUC value is included in the good classification predicate.

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