The Comparison of Machine Learning Model to Predict Bankruptcy: Indonesian Stock Exchange Data

Abstract

This study aims to determine the Machine Learning Model used to predict bankruptcy. The data was conducted from the financial statements of two public companies reported by the Indonesia Stock Exchange from 2009 to 2015. This research method uses an analysis feature in which the accounting ratios are used in statistical analysis of financial statements that handle missing values, choose the correlation feature related to class, and dealing with unbalanced datasets. This problem was resolved at the beginning of the pre-processing phase. The training process uses pre-processing results to fit the data with the prediction model. Accuracy is used to measure the performance of the model in predicting bankruptcy. The result is Sequential Minimal Optimization (SMO) with linear kernel function that works best to predict 1 year before bankruptcy with an accuracy of 91.57% and SMO with Radial Basis Function (RBF) works well to predict 2 years before bankruptcy; the accuracy is 93.8%. This study shows the effect of feature selection and normalization process in making correct predictions using the SMO method.