Predicting bankruptcy using ant colony optimization / Nur Syafiqah Abdul Ghani

Abdul Ghani, Nur Syafiqah (2021) Predicting bankruptcy using ant colony optimization / Nur Syafiqah Abdul Ghani. [Student Project] (Unpublished)


Personal bankruptcy is a process in which a debtor is declared bankrupt in compliance with the Adjudication Order issued by the High Court against a debtor if they are unable to pay at least RM30,000 in their debts. Once a person's assets have been declared bankrupt, they will all be put under the administration of DGI. This study will minimize concerns or firms from bankruptcy if it is possible to detect and fight the bankruptcy tendency at an early stage. This study developed a classification model using Ant Colony Optimization for predicting bankruptcy. Ant colony optimization has been inspired by the action of the actual ant colony and is used to solve discrete optimization issues. Ant Colony Optimization is suitable for predictive rules simplicity, precision, specificity, and sensitivity. The data set was collected by involving 250 respondents. This study focussed on Industrial Risk, Financial Flexibility, Credibility, Management Risk, Operating Risk, and Competitiveness. To achieve the set objectives, this research is conducted through three-phase of research activities which are Data pre-processing, Model Development, and Model Validation. Data pre-processing method carries out certain computations such as data transformation (normalization, aggregation) to improve data quality. In model development, Ant-Miner was used which consists of three steps. In model validation, to quantify accuracy by approving the informational collection, Ant Colony Optimization Algorithm was used and it was compared with the J48 algorithm. The accuracy of the model for J48 is 98% while the accuracy of Ant-Miner is 99.6%. The results have shown that the Ant Colony Optimization Algorithm produced a better predictive accuracy. Therefore, it is confirmed that the Ant Colony Optimization Algorithm produces the most accurate result in predicting bankruptcy. This study also has shown that Ant Colony Optimization was a suitable technique in developing the classification model.


Item Type: Student Project
Email / ID Num.
Abdul Ghani, Nur Syafiqah
Subjects: H Social Sciences > HG Finance > Personal finance. Financial literacy
H Social Sciences > HG Finance > Credit. Debt. Loans
Q Science > QA Mathematics > Mathematical statistics. Probabilities
Divisions: Universiti Teknologi MARA, Perlis > Arau Campus > Faculty of Computer and Mathematical Sciences
Programme: Management Mathematics
Keywords: Bankruptcy Prediction ; Ant Colony Optimization ; Ant-Miner ; Data Mining ; J48
Date: 30 March 2021
Edit Item
Edit Item


[thumbnail of 45539.pdf] Text

Download (327kB)

Digital Copy

Digital (fulltext) is available at:

Physical Copy

Physical status and holdings:
Item Status:

ID Number




Statistic details