Implementasi SMOTE dan Under Sampling pada Imbalanced Dataset untuk Prediksi Kebangkrutan Perusahaan

Wilda Imama Sabilla; Candra  Bella Vista

doi:10.35143/jkt.v7i2.5027

Authors

Wilda Imama Sabilla Jurusan Teknologi Informasi Politeknik Negeri Malang
Candra Bella Vista Jurusan Teknologi Informasi Politeknik Negeri Malang

DOI:

https://doi.org/10.35143/jkt.v7i2.5027

Keywords:

imbalanced dataset, bankruptcy prediction, smote, under sampling, multilayer perceptron, complement naive bayes

Abstract

Company bankruptcy becomes a serious problem because it can cause economic damage and other social consequences. Itâ€™s very important to predict bankruptcy as early as possible because prediction can be useful for evaluation and planning to avoid bankruptcy. Bankruptcy prediction is one of the imbalanced classification problems because the data with the bankrupt class is far less than the non-bankrupt class. This study aims to produce a good classification model for predicting bankruptcy. Resampling used a combination of SMOTE and under sampling, is applied to the training data to produce more optimal classification model. The classification method used for prediction is multilayer perceptron and complement naÃ¯ve bayes. Predictive performance was calculated using recall, ROC AUC, and PR AUC. Based on the test, using SMOTE and under sampling is quite significant in improving the classification model on the multilayer perceptron. Resampling in complement naÃ¯ve bayes also increased. recall and PR AUC scores The best recall obtained was 95.45% with the complement naÃ¯ve bayes method. The highest ROC AUC with resampling was also obtained using complement naÃ¯ve bayes of 87.80%. Therefore, itâ€™s concluded that bankruptcy prediction using resampling with SMOTE and under sampling, can produce good performance for detecting bankruptcy.

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