Genetic Programming based Machine Learning in Classifying Public-Private Partnerships Investor Intention

  • Ahmad Amin
  • Rahmawaty -
  • Maya Febrianty Lautania
  • Rahayu Abdul Rahman


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.


[1] C. B. Casady, K. Eriksson, R. E. Levitt, and W. R. Scott, “(Re) defining public-private partnerships (PPPs) in the new public governance (NPG) paradigm: an institutional maturity perspective,” Public Manag. Rev., vol. 22, no. 2, pp. 161–183, 2020.
[2] G. Y. Debela, “Critical success factors (CSFs) of public--private partnership (PPP) road projects in Ethiopia,” Int. J. Constr. Manag., vol. 22, no. 3, pp. 489–500, 2022.
[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.
[4] R. Djalante, “A systematic literature review of research trends and authorships on natural hazards, disasters, risk reduction and climate change in Indonesia,” Nat. Hazards Earth Syst. Sci., vol. 18, no. 6, pp. 1785–1810, 2018.
[5] H. Yurdakul, R. Kamacsak, and T. Y. Öztürk, “Macroeconomic drivers of Public Private Partnership (PPP) projects in low income and developing countries: A panel data analysis,” Borsa Istanbul Rev., vol. 22, no. 1, pp. 37–46, 2022.
[6] J. C. Bansal, P. K. Singh, N. R. Pal, and others, Evolutionary and swarm intelligence algorithms, vol. 779. Springer, 2019.
[7] T. Bäck, D. B. Fogel, and Z. Michalewicz, Evolutionary computation 1: Basic algorithms and operators. CRC press, 2018.
[8] B. Tran, B. Xue, and M. Zhang, “Genetic programming for multiple-feature construction on high-dimensional classification,” Pattern Recognit., vol. 93, pp. 404–417, 2019.
[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.
[10] M. A. U. H. Tahir, S. Asghar, A. Manzoor, and M. A. Noor, “A classification model for class imbalance dataset using genetic programming,” IEEE Access, vol. 7, pp. 71013–71037, 2019.
[11] A. Priyadarshini, S. Mishra, D. P. Mishra, S. R. Salkuti, and R. Mohanty, “Fraudulent credit card transaction detection using soft computing techniques,” Indones. J. Electr. Eng. Comput. Sci., vol. 23, no. 3, pp. 1634–1642, Sep. 2021, doi: 10.11591/ijeecs.v23.i3.pp1634-1642.
[12] R. A. Rahman, S. Masrom, and N. Omar, “Tax Avoidance Detection Based on Machine Learning of Malaysian Government-Linked Companies,” no. 2, pp. 535–541, 2019, doi: 10.35940/ijrte.B1083.0982S1119.
[13] S. Jamil, T. Mohd, S. Masrom, and N. A. Rahim, “Machine Learning Price Prediction on Green Building Prices,” in 2020 IEEE Symposium on Industrial Electronics and Applications, ISIEA 2020, 2020. doi: 10.1109/ISIEA49364.2020.9188114.
[14] A. Muneer, R. F. Ali, A. Alghamdi, S. M. Taib, A. Almaghthawi, and E. A. Abdullah Ghaleb, “Predicting customers churning in banking industry: A machine learning approach,” Indones. J. Electr. Eng. Comput. Sci., vol. 26, no. 1, pp. 539–549, 2022, doi: 10.11591/ijeecs.v26.i1.pp539-549.
[15] N. L. Saad and R. Ibrahim, “Context-Aware Recommender System based on Machine Learning in Tourist Mobile Application,” vol. 3, no. 1, pp. 19–28, 2022.
[16] S. Masrom, R. A. Rahman, N. Baharun, S. Redzwan, and S. Rohani, “Machine learning with task-technology fit theory factors for predicting students ’ adoption in video -based learning,” vol. 12, no. 3, pp. 1666–1673, 2023, doi: 10.11591/eei.v12i3.5037.
[17] S. Ghorbany, S. Yousefi, and E. Noorzai, “Evaluating and optimizing performance of public--private partnership projects using copula Bayesian network,” Eng. Constr. Archit. Manag., no. ahead-of-print, 2022.
[18] Y. Wang and R. L. K. Tiong, “Public--Private Partnership Contract Failure Prediction Using Example-Dependent Cost-Sensitive Models,” J. Manag. Eng., vol. 38, no. 1, p. 4021079, 2022.
[19] M. Eskandari, M. Taghavifard, I. R. Vanani, and S. S. G. Noori, “An Intelligent Hybrid Model for Determining Public-Private Partnership in Iranian Water and Wastewater Industry Based on Collective Tree Algorithms,” J. Water Wastewater, vol. 32, no. 1, pp. 69–90, 2021.
[20] Y. Wang, Z. Shao, and R. L. K. Tiong, “Data-driven prediction of contract failure of public-private partnership projects,” J. Constr. Eng. Manag., vol. 147, no. 8, p. 4021089, 2021.
[21] R. S. Olson and J. H. Moore, “TPOT: A Tree-Based Pipeline Optimization Tool for Automating Machine Learning,” Automated Machine Learning: Methods, Systems, Challenges. Springer International Publishing, pp. 151–160, 2019. doi: 10.1007/978-3-030-05318-5_8.
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: <>. Date accessed: 21 may 2024. doi:

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