Credit Scoring Model for Farmers using Random Forest
Dublin Core
Title
Credit Scoring Model for Farmers using Random Forest
Subject
agriculture; credit scoring; farmer; land productivity; random forest
Description
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.
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.
Creator
Kharida Aulia Bahri1
, Yeni Herdiyeni2
, Suprehatin Suprehatin3
, Yeni Herdiyeni2
, Suprehatin Suprehatin3
Publisher
, IPB University
Date
03-02-2023
Contributor
Fajar bagus W
Format
PDF
Language
Indonesia
Type
Text
Files
Collection
Citation
Kharida Aulia Bahri1
, Yeni Herdiyeni2
, Suprehatin Suprehatin3, “Credit Scoring Model for Farmers using Random Forest,” Repository Horizon University Indonesia, accessed June 6, 2025, https://repository.horizon.ac.id/items/show/9342.