International Journal of Electrical and Computer Engineering (IJECE) Vol. 11, No. 6, December 2021, pp. 5549~5557 ISSN: 2088-8708, DOI: 10.11591/ijece.v11i6.pp5549-5557  5549 Journal homepage: http://ijece.iaescore.com Company bankruptcy prediction framework based on the most influential features using XGBoost and stacking ensemble learning Much Aziz Muslim 1 , Yosza Dasril 2 1 Faculty of Technology Management and Business, Universiti Tun Hussein Onn Malaysia, Malaysia 1 Department of Computer Science, Universitas Negeri Semarang, Indonesia 2 Faculty of Technology Management and Business, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia Article Info ABSTRACT Article history: Received Nov 16, 2020 Revised Apr 8, 2021 Accepted May 11, 2021 Company bankruptcy is often a very big problem for companies. The impact of bankruptcy can cause losses to elements of the company such as owners, investors, employees, and consumers. One way to prevent bankruptcy is to predict the possibility of bankruptcy based on the company's financial data. Therefore, this study aims to find the best predictive model or method to predict company bankruptcy using the dataset from Polish companies bankruptcy. The prediction analysis process uses the best feature selection and ensemble learning. The best feature selection is selected using feature importance to XGBoost with a weight value filter of 10. The ensemble learning method used is stacking. Stacking is composed of the base model and meta learner. The base model consists of K-nearest neighbor, decision tree, support vector machines (SVM), and random forest, while the meta learner used is LightGBM. The stacking model accuracy results can outperform the base model accuracy with an accuracy rate of 97%. Keywords: Banckruptcy prediction Ensemble learning Feature importance Stacking XGBoost This is an open access article under the CC BY-SA license. Corresponding Author: Much Aziz Muslim Faculty of Technology Management Universiti Tun Hussein Onn Malaysia Batu Pahat, Johor, Malaysia Email: a212muslim@mail.unnes.ac.id 1. INTRODUCTION Predicting company bankruptcy is one of the most important parts of management science problems. The main purpose of this prediction is to categorize companies that are safe and unsafe or bankrupt [1]. In addition, the wrong decision-making in financial institutions that are in financial difficulty or distress is experienced by many social costs such as owners or shareholders, managers, government and others. Therefore, the prediction of company bankruptcy has become a special concern among industrial practitioners as well as academics or researchers [2]-[5]. Nowadays, machine learning techniques [6] and artificial intelligence [7] computation have been widely used by researchers to solve bankruptcy prediction problems such as support vector machines (SVM) [8]-[16], decision trees [17]-[23], artificial neural networks (ANN) [24]-[31] and discussion with systematic literature review technique [32]-[37]. Meanwhile, improvement in machine learning techniques through various strategies has also been carried out such as boosting improvement based on feature selection known as FS-Boosting is proven to have good performance as a learner and has higher accuracy and diversity based on two selected company bankruptcy data sets [38]. The combination of SVM and ANN integrated with dropout, auto-encoder proved to produce better accuracy than logistic regression, genetic algorithm and