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

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

 

 

Ketua : EDNAWATI RAINARLI S.Si, M.Si

Department of Informatics Engineering

Universitas Komputer Indonesia

Jl Dipatiukur 112 -116
Bandung, Indonesia

 

Email: ednawati.rainarli@email.unikom.ac.id

 

 

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 preprocessing
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.