Machine Learning Analysis in Predicting Bankruptcy in Companies - Case Study of Manufacturing Companies Listed on the Stock Exchange

Aug 6, 2026

Bankruptcy prediction in manufacturing firms is examined through machine learning to support earlier financial risk identification and decision-making. Financial data from manufacturing companies listed on the Indonesia Stock Exchange covering 2013–2023 is analyzed using LSTM, SVM, Random Forest, and XGBoost. Preprocessing includes outlier removal with Z-score, an 80:20 training-validation split, and StandardScaler for consistent feature scaling. Findings highlight SVM’s stable performance on historical data and LSTM’s advantage in capturing variations and patterns in new data.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages14

Reading Time35 min

AccessFree to download

Introduction

Background of manufacturing industry and financial distress

Importance of bankruptcy prediction and existing models

Research Methods

Research design and data source (BEI annual reports)

Sampling and variables

Preprocessing and model development

p–ISSN: 2723-6609 e-ISSN: 2745-5254 Vol. 5, No. 8 August 2024 [http://jist.publikasiindonesia.id/](http://jist.publikasiindonesia.id/) | Machine Learning Analysis in Predicting Bankruptcy in Companies (Case Study of Manufacturing Companies Listed
on the Stock Exchange)
Citra Yustika Pratiwi1*, Siti Nurwahyuningsih Harahap2
Universitas Indonesia, Indonesia
Email: [citrayustika08@gmail.com](citrayustika08@gmail.com)
*Correspondence | | --- | | ABSTRACT | | Keywords: bankruptcy, This study aims to analyze bankruptcy prediction for
machine learning, manufacturing companies using machine learning. Financial
[manufacturing companies. data](manufacturing companies. data) from manufacturing companies listed on the Indonesia Stock Exchange for the period from 2013 to 2023 are used in this study. The analytical methods employed include Long Short-Term Memory (LSTM), Support Vector Machine (SVM), Random Forest, and Extreme Gradient Boosting
(XGBoost) . The results of this study are expected to provide benefits to various stakeholders: manufacturing companies in identifying early signs of bankruptcy, creditors in evaluating the feasibility of extending credit, investors in making investment decisions, academics in advancing research in bankruptcy prediction, and market regulators (OJK) in enhancing the efficiency of supervision over manufacturing companies. The results indicate that SVM is effective in predicting historical data with consistent performance, while LSTM excels in handling variations and patterns in new data. | | | Introduction The manufacturing industry has a crucial role in the Indonesian economy, as evidenced by its significant contribution to Gross Domestic Product (GDP) since the 1980s (Madjid, Mahdi, Lukito, Nofri, & Prasvita, 2021). This sector continues to develop rapidly, showing stable growth with GDP in the manufacturing sector in 2021 reaching IDR 2,946.9 trillion and investment reaching IDR 325.4 trillion, as well as being a source of employment for 1.2 million new people (Ministry of Industry, 2022) . Indicators such as the Purchasing Managers Index (PMI) also recorded record highs, reflecting the sector's strong expansion and its role as a key pillar in national economic growth (Joshi, Ramesh, & Tahsildar, 2018) . Even though the manufacturing industry shows positive growth, economic challenges remain an important factor influencing the performance of companies in this sector. Economic fluctuations can trigger financial difficulties, which is a critical phase before the risk of bankruptcy (Swari & Pristiana, 2020) . This phenomenon, known as Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 8, August 2024 2954 Machine Learning Analysis in Predicting Bankruptcy in Companies (Case Study of Manufacturing Companies Listed on the Stock Exchange) financial distress, is characterized by decreased income, negative cash flow, and increased debt that can threaten long-term business continuity (Siswoyo, 2020) . Bankruptcy prediction is crucial in managing a company's financial risk. By applying machine learning techniques such as the Altman Model and Ohlson Model, companies can identify and manage risks more effectively (Muta’ali, 2019) . This model uses historical financial data to produce accurate bankruptcy scores, assisting companies in making strategic decisions to maintain financial stability and business sustainability (Shetty & Kellarai, 2022) . (Kothuru et al., 2022), this study suggests that Random Forest is effective in handling large and complex datasets and provides estimates of the importance of variables in bankruptcy prediction. They suggest evaluating traditional models with various machine learning techniques to provide a more comprehensive and relevant picture.(Sulastri, 2014), they compared the Ohlson and Altman models in bankruptcy prediction, with Altman proving to be more effective in the context of bankruptcy prediction for large and small companies. Th

FAQs

Which machine learning models are used for bankruptcy prediction in the study?

The study applies LSTM, Support Vector Machine (SVM), Random Forest, and XGBoost to predict corporate bankruptcy using financial indicators.

What data period and source are used for the analysis?

The research uses financial report data from manufacturing companies listed on the Indonesia Stock Exchange (BEI) for the period 2013 to 2023, sourced from companies’ submissions via the official IDX website.

What preprocessing steps are performed before training the models?

Outliers are removed using Z-score, the dataset is split into training and validation sets with an 80:20 ratio, and feature scaling is performed with StandardScaler to maintain consistent variable scales.

Aug 6, 2026

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