A Hybrid Model to Enhance The Performance of Classifier in Financial Distress Prediction - Mukti Ratna Dewi; Destri Susilaningrum (Universitas Sebelas Maret, 2024)
Accurately predicting financial distress is a critical issue in financial decision-making. Financial distress must be detected as early as possible as an important determining factor in decision-making for internal companies and financial institutions related to financing or loan decisions. Various studies on financial distress prediction in Indonesia have been carried out, ranging from traditional statistical approaches to machine learning. However, the performance of the two methods is still not optimal. Therefore, this study tries to develop machine learning techniques by combining cluster analysis and classification in a hybrid model to improve the prediction model’s performance. The case study adopted in this study is the prediction of financial distress in non-financial companies listed on the IDX from 2018-2021 by combining k-means clustering and Support Vector Machine. The analysis results show that the hybrid classifier has an accuracy value of 92.7%, which is higher than the accuracy of the single classifier, which is 88.6%.