PREDICTING INDONESIA SOVEREIGN DEBT MANAGEMENT’S RISK OCCURRENCE USING MACHINE LEARNING ALGORITHMS

Abstract

The purpose of developing this predictive model is to predict the potential occurrence of risks in the implementation of projects financed through government loans. It uses data on Indonesian sovereign loans from the MoF DMFAS in the period between 1998 and 2019, consisting of 1930 government loans. Government loan performance is represented by two qualities: timeliness of loan disbursement and rate of loan realization. In the development of the model, the ensemble learning methodology was applied with the voting classifier algorithm. The algorithms used to make predictions for voting classifier include k-NN Classifier, Random Forest, and Logistic Regression. The predictive model developed can accurately predict 73.14% of observations on the rate of disbursement of government loans and is able to predict numerous amendments to drawing limit correctly as much as 89.69% of observations.