Genetic Programming based Machine Learning in Classifying Public-Private Partnerships Investor Intention
Abstract
To accelerate the growth of public infrastructure development, the government employs public private partnerships (PPP). However, this scheme exposes the private sector to various risks, including political risks, which can negatively impact the financial performance and reporting of participating firms. A significant challenge for the government is the insufficient private sector engagement in PPP arrangements. Hence, the purpose of this study is to evaluate the effectiveness of machine learning prediction models in categorizing private investor interest in PPP programs based on Indonesia evidences. The PPP data was analyzed in this study using two machine learning approaches, Genetic Programming and conventional machine learning, with testing results showing that all machine learning algorithms from both approaches achieved high accuracy rates of over 80%, with the Genetic Programming machine learning outperformed the conventional approach. This study highlights the potential of machine learning algorithms in predicting private investor interest in PPP programs, providing a tool for managing political risks and encouraging greater private sector participation.
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[3] E. Endri, Z. Abidin, T. P. Simanjuntak, I. Nurhayati, and others, “Indonesian stock market volatility: GARCH model,” Montenegrin J. Econ., vol. 16, no. 2, pp. 7–17, 2020.
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[7] T. Bäck, D. B. Fogel, and Z. Michalewicz, Evolutionary computation 1: Basic algorithms and operators. CRC press, 2018.
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[9] L. W. Santoso, B. Singh, S. S. Rajest, R. Regin, and K. H. Kadhim, “A genetic programming approach to binary classification problem,” EAI Endorsed Trans. Energy Web, vol. 8, no. 31, pp. e11--e11, 2021.
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Published
2023-05-05
How to Cite
AMIN, Ahmad et al.
Genetic Programming based Machine Learning in Classifying Public-Private Partnerships Investor Intention.
Mathematical Sciences and Informatics Journal, [S.l.], v. 4, n. 1, p. 33-41, may 2023.
ISSN 2735-0703.
Available at: <https://myjms.mohe.gov.my/index.php/mij/article/view/21910>. Date accessed: 10 june 2023.
doi: https://doi.org/10.24191/mij.v4i1.21910.
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