[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118441-en":3,"doc-seo-118441-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118441,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Genetic Programming based Machine Learning in Classifying Public-Private Partnerships Investor Intention - Study Report","Machine learning prediction models are evaluated for classifying private investor intention in public-private partnerships (PPP) programs, addressing a persistent policy challenge: insufficient private sector engagement. The study analyzes PPP data from Indonesia using Genetic Programming alongside conventional machine learning approaches. Results show all models achieving high accuracy above 80%, with Genetic Programming outperforming conventional methods. The findings support using machine learning to anticipate investor intent, manage political risk exposure, and strengthen strategies for greater private participation in infrastructure projects.","Genetic Programming based Machine Learning in Classifying Public-Private Partnerships Investor Intention  \nAhmad Amin  \nFaculty of Economics and Business, Universitas Gadjah Mada, Yogjakarta, Indonesia  \n[amien@ugm.ac.id](amien@ugm.ac.id)  \nRahmawaty  \nFaculty of Economics and Business, Universitas Syiah Kuala, Acheh, Indonesia  \n[rahmawaty@unsyiah.ac.id](rahmawaty@unsyiah.ac.id)  \nMaya Febrianty Lautania  \nFaculty of Economics and Business, Universitas Syiah Kuala, Acheh, Indonesia  \n[mayahaidar@unsyiah.ac.id](mayahaidar@unsyiah.ac.id)  \nRahayu Abdul Rahman  \nFaculty of Accounting, Universiti Teknologi MARA, Perak Branch Tapah Campus, Perak, Malaysia  \n[rahay916@uitm.edu.my](rahay916@uitm.edu.my)  \nArticle Info ABSTRACT  \n\n| Article history:\u003Cbr>Received Feb 18, 2023 Revised Mac 25, 2023 Accepted Apr 22, 2023 | 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. |\n| --- | --- |\n| Keywords (minimum 5):\u003Cbr>Genetic Programming Machine Learning Public-private Partnership Investor Intention Classification |  |\n\nCorresponding Author:  \nAhmad Amin  \nFaculty of Economics and Business, Universitas Gadjah Mada, Yogjakarta, Indonesia email: [amien@ugm.ac.id](amien@ugm.ac.id)  \n1. Introduction  \nPublic-private partnerships (PPP) have emerged as a popular mechanism for accelerating public infrastructure development globally. Despite its potential benefits, PPP schemes pose various risks to private sector participants, including political risks, which can adversely affect their financial performance and reporting[1],[2] . One of the challenges facing governments is the lack of private sector engagement in PPP arrangements, particularly in Indonesia, where there is a higher risk due to economic volatility and the possibility of natural disasters[3],[4] . The possibility of natural disastersin Indonesia creates a higher risk environment for PPP arrangements, as these events can result in project delays, cost overruns, and disruptions in revenue streams, making it more challenging to attract private sector participation in PPP projects. As a result, identifying potential private sector investors' interests in PPPs becomes critical to ensure the successful implementation of infrastructure development projects[5] .  \nGiven the risks associated with PPPs, it is essential to identify potential private sector investors' interests in participating in these arrangements. Previous studies have attempted to predict private sector interest in PPPs using machine learning algorithms, but limited research has been conducted on Indonesia. Furthermore, these studies suggest that more advanced approaches are necessary to improve the accuracy of predictions.  \nTherefore, this research aims to fill this gap by introducing a Genetic Programming (GP) approach to predict private investor interest in PPP programs in Indonesia, and compare its efficacy with ","cbCaiblHsvBkpxYe","https://ap.wps.com/l/cbCaiblHsvBkpxYe","pdf",664193,1,10,"English","en",105,"# Abstract\n# Introduction\n# Literature Review","[{\"question\":\"What problem does the study address in PPP arrangements?\",\"answer\":\"The study targets the difficulty governments face in attracting sufficient private sector engagement in PPPs, especially where political and other risks can harm participating firms’ performance and reporting.\"},{\"question\":\"Which machine learning methods are compared?\",\"answer\":\"The research compares Genetic Programming with a conventional machine learning approach, including AutoModel RapidMiner as the commonly used baseline mentioned in the introduction.\"},{\"question\":\"What is the main finding on model performance?\",\"answer\":\"All tested algorithms achieve accuracy rates over 80%, and Genetic Programming delivers stronger results than the conventional approach.\"}]","Genetic Programming based Machine Learning in Classifying Public-Private Partnerships Investor Intention - 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