Ensemble Learning in Predicting Financial Distress of Indonesian Public Company

Dyah Sulistyowati Rahayu, Heru Suhartanto

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Predicting financial distress can avoid firm bankruptcy. That is an important issue in matters of company sustainability and the economic growth in general. Indonesia as a developing country needs a reliable system that is able to predict the bankruptcy of a company because it can affect the overall economic condition at different levels. The ensemble learning which is built to achieve better performance of prediction can be implemented to forecast the unhealthy company conditions. Random forest ensemble learning and AdaBoost have been proven superior to the single one. Both methods are applied to Indonesia Public Company data with 6 variables based on Altman Z-Score and one additional variable. The accuracy, precision, recall, and f1-score have an average of 91% regardless of the data imbalance. The ensemble score determines its superiority to the single machine learning.

Original languageEnglish
Title of host publication2020 8th International Conference on Information and Communication Technology, ICoICT 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728161426
DOIs
Publication statusPublished - Jun 2020
Event8th International Conference on Information and Communication Technology, ICoICT 2020 - Yogyakarta, Indonesia
Duration: 24 Jun 202026 Jun 2020

Publication series

Name2020 8th International Conference on Information and Communication Technology, ICoICT 2020

Conference

Conference8th International Conference on Information and Communication Technology, ICoICT 2020
CountryIndonesia
CityYogyakarta
Period24/06/2026/06/20

Keywords

  • AdaBoost
  • ensemble learning
  • financial distress
  • prediction
  • random forest

Fingerprint Dive into the research topics of 'Ensemble Learning in Predicting Financial Distress of Indonesian Public Company'. Together they form a unique fingerprint.

Cite this