[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120557-en":3,"doc-seo-120557-105":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},120557,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","Random Forest Machine Learning Analysis of Generative AI’s Impact on Learning Effectiveness in Indonesian Higher Education - Research Article","Generative Artificial Intelligence (GenAI) is reshaping Indonesian higher education by enabling learning innovation while challenging assumptions about learning effectiveness and academic integrity. This study builds a machine learning–based quantitative model to evaluate how GenAI usage affects learning effectiveness, emphasizing Informatics students as key digital literacy stakeholders. Survey data from 300 students capture demographics, GPA, exam scores, GenAI usage, digital literacy, motivation, self-efficacy, academic integrity, and institutional support. Random Forest achieves the best results with 87% accuracy and AUC above 0.90.","Random Forest Machine Learning Analysis of Generative AI’s Impact on Learning Effectiveness in Indonesian Higher Education  \nSulfikar Sallu*1, Hendriadi2  \n1Fakultas Keguruan dan Ilmu Pendidikan, Universitas Sulawesi Tenggara, Indonesia 2Fakultas Ekonomi dan Bisnis, Universitas Sulawesi Tenggara, Indonesia  \n[Email:](Email:1sulfikar.sallu@gmail.com)[1](Email:1sulfikar.sallu@gmail.com)[sulfikar.sallu@gmail.com](Email:1sulfikar.sallu@gmail.com)  \nReceived : Oct 1, 2025; Revised : Oct 3, 2025; Accepted : Oct 13, 2025; Published : Oct 21, 2025  \nAbstract  \n\n| Generative Artificial Intelligence (GenAI) has rapidly penetrated Indonesian higher education, creating opportunities for learning innovation while raising concerns about effectiveness and academic integrity. This study develops a machine learning–based quantitative model to analyze the impact of GenAI usage on learning effectiveness, with a particular focus on Informatics students as key digital literacy stakeholders. Data were collected from a simulated survey of 300 students, covering demographics, GPA, exam scores, GenAI usage patterns, digital literacy, motivation, self-efficacy, academic integrity, and institutional support. Preprocessing steps included normalization of continuous variables, one-hot encoding of categorical variables, and feature selection using Recursive Feature Elimination (RFE) . Six machine learning algorithms—Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), XGBoost, and Artificial Neural Network—were compared to identify the best predictive model. Results show that Random Forest achieved the highest performance, with 87% accuracy and an AUC greater than 0.90, significantly outperforming other algorithms. The most influential predictors were digital literacy, institutional policies, and frequency of GenAI usage, while demographic variables contributed minimally. These findings suggest that GenAI can enhance learning effectiveness in Informatics education when supported by critical digital literacy and ethical awareness. The novelty of this study lies in integrating survey-based educational data with Random Forest machine learning to empirically model GenAI’s role in Indonesian higher education. The results provide practical implications for policymakers, educators, and institutions to design AI-integrated learning strategies that maximize innovation while safeguarding academic integrity.\u003Cbr>Keywords : Generative AI, Higher Education, Indonesia, Informatics, Learning Effectiveness, Machine Learning, Random Forest. |\n| --- |\n| This work is an open access article and licensed under a Creative Commons Attribution-Non Commercial\u003Cbr>4.0 International License\u003Cbr> |\n\n1. INTRODUCTION  \nThe rapid development of Generative Artificial Intelligence (GenAI) has fundamentally transformed higher education worldwide, including Indonesia [1], [2], [3], [4] . Tools such as ChatGPT, Copilot, and Gemini are increasingly adopted by university students as digital assistants for learning, writing, coding, and academic discussions [5], [6], [7]. The presence of GenAI introduces unprecedented opportunities to enhance learning effectiveness [8] by offering personalized feedback, accelerating access to information, and improving task efficiency. In the Indonesian higher education context, where the integration of digital technologies is prioritized through government policies such as Kampus Merdeka and digital literacy programs, GenAI holds significant potential to support academic innovation [9] .  \nThe rapid advancement of Generative Artificial Intelligence (GenAI) has transformed higher education globally, including Indonesia, by reshaping how students access information, complete assignments, and engage in academic discussions. Tools such as ChatGPT, Copilot, and Gemini are increasingly used as digital assistants that offer personalized feedback, accelerate information retrieval,  \nand improve task efficiency. In the Indonesian context, the i","cbCaitWd9lC3tXB8","https://ap.wps.com/l/cbCaitWd9lC3tXB8","pdf",574209,1,17,"English","en",105,"# Introduction\n# Related Work\n# Methodology and Data\n## Data Collection and Variables\n## Preprocessing and Feature Selection\n# Machine Learning Models\n## Model Comparison\n# Results and Discussion\n## Key Predictors\n# Conclusion and Implications","[{\"question\":\"What is the main goal of the study on GenAI in Indonesian higher education?\",\"answer\":\"The study quantifies how GenAI usage influences learning effectiveness, using a machine learning predictive modeling approach focused on Informatics students.\"},{\"question\":\"Which machine learning algorithm performs best in predicting learning effectiveness?\",\"answer\":\"Random Forest achieves the highest performance, reaching 87% accuracy and an AUC greater than 0.90, outperforming other compared algorithms.\"},{\"question\":\"What factors most strongly affect learning effectiveness in the results?\",\"answer\":\"Digital literacy, institutional policies, and the frequency of GenAI usage are identified as the most influential predictors, while demographic variables contribute minimally.\"}]","Random Forest Machine Learning Analysis of Generative AI’s Impact on Learning Effectiveness in Indonesian Higher Education - 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