p–ISSN: 2723 - 6609 e-ISSN: 2745-5254
Vol. 5, No. 8 August 2024 http://jist.publikasiindonesia.id/
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)
Citra Yustika Pratiwi
1*
, Siti Nurwahyuningsih Harahap
2
Universitas Indonesia, Indonesia
Email:
[email protected]
*Correspondence
ABSTRACT
Keywords: bankruptcy,
machine learning,
manufacturing companies.
This study aims to analyze bankruptcy prediction for
manufacturing companies using machine learning. Financial
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
Machine Learning Analysis in Predicting Bankruptcy in Companies (Case Study of
Manufacturing Companies Listed on the Stock Exchange)
Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 8, August 2024 2955
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. This study suggests combining traditional models with machine
learning algorithms as well as evaluation with various metrics to provide a more in-depth
picture (Almas, 2023).
Based on the background above, the main objective of this research is to evaluate
machine learning models that can produce the best bankruptcy predictions and models
that have the highest prediction accuracy.
Research Methods
This research uses an archival study research strategy with a focus on quantitative
comparative analysis. The method applied is predictive analysis using financial report
data from manufacturing companies listed on the Indonesia Stock Exchange (BEI). The
main data is obtained from financial reports submitted by these companies via the official
IDX website. This research selected companies that have published annual reports from
2013 to 2023 as samples, using a purposive sampling method to ensure relevance to the
research objectives. The variables analyzed include various financial ratios adopted from
the Altman and Ohlson model to predict potential bankruptcy. Data analysis was carried
out through a preprocessing process which included removing outliers using a Z-score,
dividing the dataset into training and validation data with a ratio of 80:20, as well as
feature scaling using StandardScaler to ensure variable scale consistency. The creation of
a machine learning model is based on the reputation and effectiveness of the Altman and
Ohlson model in predicting corporate bankruptcy. This analysis aims to produce an
accurate predictive model to support decision making regarding financial risk
management of manufacturing companies in Indonesia.
Research Approach
This study uses a quantitative methodology as a framework for comparative
analysis. (Creswell et al., 2018) define quantitative research as a research method that
tests theory by measuring variables and analyzing numerical data using statistical
procedures. This approach aims to determine relationships between variables, test
Citra Yustika Pratiwi, Siti Nurwahyuningsih Harahap
Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 8, August 2024 2956
hypotheses, and make predictions. The quantitative approach in this research is in order
to obtain an in-depth and comprehensive understanding of the use of machine learning in
predicting bankruptcy in manufacturing companies.
The technique applied in this study is predictive analysis. Predictive analytics is a data
analysis technique used to predict future outcomes based on historical data (Qi & Tao,
2018). Predictive analytics can be used to predict corporate bankruptcy, so that companies
can take preventative action and strategic adjustments before experiencing significant
financial difficulties
Data Source
The information used in this study is sourced from shortage reports of
manufacturing companies that are registered on the Indonesian Stock Exchange (BErI).
The main data is obtained from shortfall reports submitted by terrsburt companies which
can be accessed through the official BERI website. Apart from that, other relevant data is
GNP (Gross National Product) which can be obtained from trusted sources such as
financial institutions, government institutions or economic research institutions.
Sample Determination Method
The sample in this research consists of manufacturing companies that are registered with
BERI and have published financial statements in the time period 2013 to 2023.The sample
selection process is carried out by using a purposive sampling method. This method
selection allows selecting samples that are relevant to the research objectives. The criteria
for sample selection are various:
1. Manufacturing companies that are registered with Burrsa Erferk Indonesia and have
published financial reports for the period 2013 to 2023.
2. The company has complete data regarding the relevant variables used in the research.
Research Variables
The variables analyzed in this study are divided into two types, namely independent
variables and dependent variables. Independent variables include financial ratios such as
liquidity, profitability, solvency, activity and market dimensions. Meanwhile, the
dependent variable is the company's financial health status, which is represented by a
binary variable where the number 1 indicates bankruptcy and 0 indicates non-bankruptcy.
Results and Discussion
The population in this study is manufacturing companies listed on the Indonesia
Stock Exchange (IDX) in 2013-2023. This study uses two types of datasets, namely the
Ohlson and Altman Z model datasets. Each dataset has different attributes because it
adapts its respective model and predefined labels to the model's calculations.
The dataset used in this study is the financial report data of 161 manufacturing
companies which is secondary data obtained from data sources on the www.idx.co.id
website.
Table 1
Research Sample Selection Procedure
It
Criterion
Sum
Machine Learning Analysis in Predicting Bankruptcy in Companies (Case Study of
Manufacturing Companies Listed on the Stock Exchange)
Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 8, August 2024 2957
1
Manufacturing Companies listed on the IDX in the period 2013-2023
166
2
Companies that have not submitted financial statements
5
Total research observations
161
Table 1 shows that during the period 2013-2023, there were a total of 166
manufacturing companies. Of these, 5 companies did not publish financial statements
during the period. Thus, 161 banking companies meet the sample criteria for this study.
Furthermore, companies that meet the sample criteria are grouped into two categories:
Category 1 for companies that are experiencing financial distress or bankruptcy, and
Category 0 for companies that are not experiencing financial distress or not bankrupt
(Ariyanto, 2017).
Model Formation
The process of forming a classification model aims to create a classification model.
The model will be used to classify the labels for both datasets. The model formation
process uses the scikit-learn library and the Python programming language. 4 models will
be formed in this study, including Support Vector Machine (SVM), Random Forest,
XGBoost, and long short term memory (LSTM).
Support Vector Machine (SVM)
The SVM model formation process uses hyperparameter tuning techniques to
determine the best parameters to be used on the model. This technique uses the Grid
Search CV function derived from the scikit-learn library in the python programming
language. For each model training process with training data with certain parameters, the
model will be evaluated with K-Fold cross-validation with a cv value equal to 5 (Wibowo,
2012).
The Support Vector Machine (SVM) is divided into two different datasets, namely
Ohlson data and Altman data. In the first part, SVM is applied to Ohlson data with
hyperparameter settings through Grid Search Cross-Validation. After getting the best
model, predictions are made on the test data and calculation of evaluation metrics such as
accuracy, precision, recall, F1 score and specificity.
Random Forest
The Random Forest model formation process uses hyperparameter tuning
techniques to determine the best parameters to be used in the model. This technique uses
the GridSearchCV() function derived from the scikit-learn¬ library in the Python
programming language. For each process of training a model with training data with
certain parameters, the model will be evaluated with K-Fold cross-validation with cv=5.
The modelling uses the Random Forest algorithm with a variety of predefined parameters.
First, the best parameter search was carried out using the GridSearchCV method with
cross-validation 5 times. The best results of the model along with the parameters used and
the best score are displayed. Then, predictions were made on the test dataset using the
best model obtained, followed by the calculation and printing of evaluation metrics such
as precision, recall, specificity and F1-score to evaluate the model's performance on the
test data. This process is repeated for two different data sets, "Houston" and "Saltzman",
with the same steps.
Citra Yustika Pratiwi, Siti Nurwahyuningsih Harahap
Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 8, August 2024 2958
XGBoost
The XGBoost model formation process uses hyperparameter tuning techniques to
determine the best parameters to be used on the model. This technique uses the
GridSearchCV() function derived from the scikit-learn¬ library in the Python
programming language. For each model training process with training data with certain
parameters, the model will be evaluated with K-Fold cross-validation with a value of
cv=5.
GridSearchCV along with XGBClassifier is used to optimize key parameters such
as max_depth, learning_rate, and subsamples to improve the accuracy of the classification
model. param_grid explicitly defines a range of values for each parameter, which allows
XGBClassifier to be tested in a variety of configurations through cross-validation five
times by GridSearchCV.
Long short term Memory (LSTM)
Data training needs to be reshaped to change the dimensions before forming the
LSTM model. The model has an epoch parameter of 20 and a hidden_units of 64.
Each model is arranged sequentially with an LSTM layer that has 64 units, followed
by sigmoid activation and a Dense layer. The data is rearranged to meet the LSTM input
format, and the model is compiled with the Adam optimizer and the mean squared error
loss function. The training was carried out for 20 epochs.
Model Analysis
Model analysis is carried out to obtain a classification model with parameters that
have the highest accuracy value. The model analysis will be carried out on both datasets.
Table 2 is a comparison of the accuracy values of the classification model along with the
best parameters.
Table 2
Comparison of Classification Models
Dataset
Model
Precision
(%)
Recall
(%)
F1-Score
(%)
Ohlson
LSTM
82.35
43.75
57.14
SVM
96.87
96.87
96.87
Random
Forest
93.54
90.62
92.06
XGBoost
88.23
93.75
90.90
Altman
LSTM
89.65
91.76
90.69
SVM
100
96.47
98.20
Random
Forest
93.82
89.41
91.56
XGBoost
97.31
88.23
93.75
Based on the results from the performance table, there are five algorithm options to
consider:
1. High Accuracy: Choose a machine learning technique with high accuracy4 if the most
important thing is how accurate the system is in classifying data correctly. Accuracy
is the ratio of correct predictions (both positive and negative) to the accuracy of the
Machine Learning Analysis in Predicting Bankruptcy in Companies (Case Study of
Manufacturing Companies Listed on the Stock Exchange)
Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 8, August 2024 2959
data. From the table, it can be seen that the machine learning technique with the highest
accuracy in Modern Ohlson and Modern Altman is SVM.
2. High Recall: Choose a machine learning technique with high recall if the error
calculation is more likely to cause Falser Positive than Falser Nergative. In this study,
it is better for the model to incorrectly predict a company that is actually not bankrupt
as bankrupt than to incorrectly predict a company that is actually bankrupt as not
bankrupt. From the table, it can be seen that the machine learning technique with the
highest frequency of calls on Model Ohlson and Model Altman is SVM.
3. High Precision: Choose a machine learning technique with high precision if you prefer
to take truer positives and avoid false positives. In this study, it is better for the model
to incorrectly predict a bankrupt company that is not actually bankrupt than to
incorrectly predict a non-bankrupt company that is actually bankrupt. From the table,
it can be seen that the algorithm with the highest precision in Model Ohlson and Model
Altman is SVM.
4. High Specificity: Choose a machine learning technique with high specificity if taking
errors does not really want a Falser Positive to occur. The model should avoid falsely
detecting bankruptcy in companies that are not actually bankrupt. From the table, it
can be seen that the algorithm with the highest specificity in Model Ohlson is Random
Forest and Model Altman is SVM.
5. High F1 Scorer: Choose a machine learning technique with high F1 Scorer if the
calculation of the error is more concerned with the balance between recall and
precision. This means that the chosen algorithm must have small Falser Positive and
Falser Negative values. From the table, it can be seen that the highest recall algorithm
in Model Ohlson and Model Altman is SVM
Taking into account the metrics that best suit the distress analysis needs, SVM
appears to be a consistent and superior choice for the most important metrics based on
the results of the performance table.
Performance information is presented in numerical form only. To display the
performance information of the classification algorithm graphically, the Receiver
Operating Characteristic (ROC) or Precision-Recall Curve can be used. The ROC curve
is made based on the value of the confusion matrix, which is to compare the False Positive
Rate with the True Positive Rate. To assess and compare the performance of each
algorithm, we can look at the area under the curve or AUC (Area Under Curve).
Here are the results of the testing of the 4 Algorima classifications.
Model Ohlson
1. Results of Confusion Matrix and ROC Curve and AUC Long Short Term Memory
(LSTM)
Citra Yustika Pratiwi, Siti Nurwahyuningsih Harahap
Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 8, August 2024 2960
Figure 1 Confusion Metrix and ROC-AUC curve of LSTM Model
Based on the test results presented in Figure 1, the LSTM model shows quite good
performance with an accuracy of 91.77%, precision 82.35%, recall 43.75%, F1-score
57.14% and specificity 98.66%. The confusion matrix value shows 14 True Positive,
221 True Negative, 3 False Positive, and 18 False Negative results. The ROC curve
with AUC 0.90 indicated excellent discrimination ability. Even though this model
shows high accuracy and precision, the relatively low recall value shows that the
LSTM model has several weaknesses in detecting all positive cases.
2. Support Vector Machines (SVM) Confusion Matrix and ROC and AUC Curve Results
Figure 2 Confusion Metrix and ROC-AUC curve of SVM Model
Based on the test results as presented in Figure 2, the Support Vector Machine (SVM)
model shows excellent performance with an accuracy of 99.22%, precision and recall of 96.88%
respectively, and an F1 Score of 96.88%. With only 1 error for each False Positive and False
Negative, and Specificity 99.55%. It can be concluded that this model is very effective in
classifying data. The ROC curve showed an AUC of 0.98, indicating almost perfect
discrimination ability. Overall, this model is very reliable in classification with very minimal
prediction errors.
3. Results of Confusion Matrix and ROC Curve and AUC Random Forest
Machine Learning Analysis in Predicting Bankruptcy in Companies (Case Study of
Manufacturing Companies Listed on the Stock Exchange)
Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 8, August 2024 2961
Figure 3 Confusion Metrix and ROC-AUC curve of Random Forest Model
Based on the test results as presented in Figure 3, the Random Forest model shows very
good performance with an accuracy of 98.04%, precision of 93.55%, and recall of 90.62%. The
confusion matrix value shows 29 True Positive, 222 True Negative, 2 False Positive, and 3 False
Negative. F1-Score is 92.06% and specificity reaches 99.11%. The ROC curve with AUC 0.90
indicated excellent discrimination ability.
4. Results of Confusion Matrix and ROC Curve and AUC XGBoost
Figure 4 Confusion Metrix and ROC-AUC curve of the XGBoost Model
Based on the testing results presented in Figure 4, the XGBoost model shows good
performance with an accuracy of 97.57%, precision of 88.24%, and recall of 93.75%. The
confusion matrix value shows 30 True Positive, 220 True Negative, 4 False Positive and
2 False Negative. F1-Score is 90.09% and specificity reaches 98.21%. The ROC curve
with an AUC of 0.91 indicates excellent discrimination ability although it performs
slightly below the SVM model.
Model Altman
5. Results of Confusion Matrix and LSTM ROC and AUC Curves
Citra Yustika Pratiwi, Siti Nurwahyuningsih Harahap
Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 8, August 2024 2962
Figure 5 Confusion Metrix and ROC-AUC curve of LSTM Model
Based on the test results in Figure 4.5, the LSTM model shows very good
performance with an accuracy of 93.86%, precision 89.65%, and recall 91.76%. The
confusion matrix value shows 78 True Positive, 167 True Negative, 9 False Positive, and
7 False Negative. F1-Score is 90.69% and specificity reaches 94.88%. The ROC curve
with AUC 0.94 indicated excellent discrimination ability. This model proved to be very
reliable in classification with little prediction error which shows that the LSTM model
has excellent performance in detecting positive and negative cases.
6. Results of Confusion Matrix and SVM ROC and AUC Curves
Figure 6 Confusion Metrix and ROC-AUC curve Model SVM
Based on the results of the examination in Figure 6, the SVM model shows very good
performance with an accuracy of 98.85%, precision of 100% and recall of 96.47%. The confusion
matrix value shows 82 True Positive, 176 True Negative, 0 False Positive, and 3 False Negative.
F1-Score is 98.20%, and specificity reaches 100%. The ROC curve with AUC 0.98 indicated
excellent discrimination ability. This model proved to be very reliable in classification with little
prediction error, which indicates that the SVM model has excellent performance in detecting
positive and negative cases.
7. Results of Confusion Matrix and ROC Curve and AUC Random Forest
Machine Learning Analysis in Predicting Bankruptcy in Companies (Case Study of
Manufacturing Companies Listed on the Stock Exchange)
Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 8, August 2024 2963
Figure 7 Confusion Metrix and ROC-AUC curve of the Random Forest Model
Based on the test results in Figure 7, the Random Forest model shows very good
performance with an accuracy of 94.63%, precision 93.82%, and recall 89.41%. The confusion
matrix value shows 76 True Positive, 171 True Negative, 5 False Positive, and 9 False Negative.
F1-Score is 91.56% and specificity reaches 97.15%. AUrC Random Forest serbersar 0.99. AUrC
= 0.99 means that the True Positive Rater result is always close to 1 compared to the Falser
Positive Rater value. This shows that the SVM classifier can very well differentiate between all
positive and negative classes correctly. The higher the AUC, the better the model performance in
distinguishing positive and negative classes
8. Results of Confusion Matrix and ROC Curve and XGBoost
Figure 8 Confusion Metrix and ROC-AUC curve of the XGBoost Model
Based on the testing results as presented in Figure 8, the XGBoost model shows
excellent performance with an accuracy of 96.16%, precision of 97.31%, and recall of
88.23%. The confusion matrix value shows 75 True Positive, 176 True Negative, 0 False
Positive, and 10 False Negative. F1-Score is 93.75% and specificity reaches 100%. The
ROC curve with AUC 0.94 indicated excellent discrimination ability. This model is very
reliable in classification tasks with little prediction error showing excellent performance
in detecting positive and negative cases with very low error rate.
Table 3 AUC Evaluation Results
Algoritma
AUC
Ohlson
(%)
Altman
(%)
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Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 8, August 2024 2964
LSTM
0.90
0.94
Support Vector Machine (SVM)
0.98
0.98
Random Forest
0.90
0.99
XGBoost
0.91
0.94
By using 2023 data as new data as many as 147 samples, the accuracy of each model
in predicting the Oshlon model and the Altzman Model is obtained as follows:
Table 4
Comparison of Model Prediction Accuracy
Model
Label
Prediction Accuracy
Distress
No
Distres
s
Total
SVM
%
XGb
oost
%
Rando
m
Forest
%
LSTM
%
Oshlo
n
17
130
147
130
88
132
90
130
88
134
91
Altma
n
44
103
147
112
76
111
75
109
74
113
77
Ohlson Model: Of the 147 companies tested, the SVM and Random Forest machine
learning techniques predicted 130 companies correctly (88% accuracy), XGBoost
achieved 90%, and LSTM performed best with 91%.
Altman Model: SVM had 76% accuracy, XGBoost 75%, Random Forest 74%, and
LSTM best with 77%.
Based on Table, LSTM has the best performance in predicting bankruptcy on 2023
data with an accuracy of 91% for the Ohlson Model and 77% for the Altman Model. This
result is different from the 2013-2022 data, where SVM is considered the best. Causes of
these differences include differences in sample sizes, overfitting to old data, model
complexity and learning capabilities, and changing economic conditions.
Conclusion
Based on the analysis, several main conclusions are as follows. Using data from
2013-2022, Support Vector Machine (SVM) produces the best bankruptcy prediction
model based on accuracy, precision, specificity, F1-Score, and recall for the Altman
Model and Ohlson Model, demonstrating the effectiveness of SVM in predicting old data.
Using new data from 2023, Long Short Term Memory (LSTM) shows the best
performance with the highest prediction accuracy of 91% for the Ohlson Model and 77%
for the Altman Model, demonstrating the ability of LSTM to handle variations and
patterns in new data. Accurate prediction models help stakeholders make better decisions,
reduce financial risks and optimize company profits, and create a stable and responsive
business environment. This research has several limitations. Since the required GDP
(Gross Domestic Product) price index data is not available on the Statistics Agency
website, the GDP values for each year are calculated independently using the 2010 GDP
values as the base year. The number of research samples is also limited during the 2013-
Machine Learning Analysis in Predicting Bankruptcy in Companies (Case Study of
Manufacturing Companies Listed on the Stock Exchange)
Jurnal Indonesia Sosial Teknologi, Vol. 5, No. 8, August 2024 2965
2023 period. Suggestions for future research include selecting variables that are more
relevant and informative in predicting corporate bankruptcy, as well as adding a longer
annual deficiency reporting period for a more in-depth and accurate analysis.
Citra Yustika Pratiwi, Siti Nurwahyuningsih Harahap
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