Optimization of Artificial Intelligence (AI) Dependency Level Classification Among Students Using Random Forest with SMOTE Oversampling Technique to Address Data Imbalance - Hermanto; Riza Fahlapi; Antonius Yadi Kuntoro; Khoirul Rista Abidin (Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya, 2026)
The development of generative artificial intelligence (AI) has significantly transformed the way students complete academic tasks. Excessive reliance on AI has the potential to diminish students' creativity and independent thinking abilities. This study aims to classify the level of students' dependency on AI in relation to academic creativity using the Random Forest algorithm optimized with the Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance in the data. Data were collected through a structured questionnaire using a Likert scale of 1–5 consisting of 24 statement items from 74 student respondents at Politeknik 'Aisyiyah Pontianak, covering variables of AI usage intensity, dependency behavior, creativity, independent thinking, and academic motivation. Respondents were classified into three classes: Low (n=30), Moderate (n=19), and High (n=25). The results indicate that the Random Forest + SMOTE model evaluated with Cross Validation achieved an accuracy of 97.14% (±6.02%), a precision of 97.06% (micro average), and recall values of 100% for the Low class, 95.83% for the Moderate class, and 95.00% for the High class. The most dominant feature was the tendency to think of AI-generated answers when questioned by lecturers (B4, importance=0.1097), followed by AI usage intensity of more than 3 hours per day (A2, importance=0.0872). These findings contribute to the development of an early detection system for AI dependency among students in higher education institutions.