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All Journal Artificial Intelligence in Educational Decision Sciences Journal of Vocational, Informatics and Computer Education
Pramudya Asoka Syukur
Universitas Negeri Makassar
Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Social Sciences

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Comparison of Dataset Proportions in SVM and Random Forest Algorithms in Detecting Student Dependence on AI in Learning Sardar Faroq Ahmd Khan; Pramudya Asoka Syukur; Andi Baso Kaswar; Marwan Ramdhany Edy
Artificial Intelligence in Educational Decision Sciences Vol 1 No 1 (2026): Artificial Intelligence in Educational Decision Sciences
Publisher : PT. Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/aieds.v1i1.6

Abstract

Purpose – The rapid integration of artificial intelligence (AI) in education has raised concerns about excessive student dependence, potentially undermining critical thinking and learning autonomy. This study aims to identify the most effective machine learning algorithm for detecting AI dependency in learning activities and to examine the impact of training–testing data proportion on predictive performance.Methods - This study employs the CRISP-DM framework and applies two supervised classification algorithms, Random Forest and Support Vector Machine (SVM), to a synthetic dataset of 10,000 AI-assisted learning sessions. The target variable, perceived AI assistance level, was discretised into three categories (low, medium, and high). Model performance was evaluated under four dataset split scenarios (60:40, 70:30, 80:20, and 90:10) using accuracy, AUC, precision, recall, and F1-score.Findings - The results show that Random Forest consistently outperforms SVM across all dataset proportions and evaluation metrics. The highest performance was achieved by Random Forest with a 60:40 split, yielding an accuracy of 67.6% and an AUC of 80.8%. Although SVM demonstrated stable performance, it required larger training datasets and remained inferior to Random Forest.Research limitations - The use of synthetic data and limited behavioural features restricts the generalisability of the findings. The moderate accuracy indicates that AI dependency is a complex construct not fully captured by the current model. Originality - This study provides empirical evidence on the combined influence of algorithm selection and dataset proportion in detecting AI dependency, offering practical guidance for developing early-warning systems to support responsible AI use in education.
The Role of Anthropomorphism in Shaping Students’ Emotional Attachment to AIED: A Triangular Theory of Love Approach Asmi Ulfiah; Al Haytsam Mappaita; Aprilianti Nirmala S; Pramudya Asoka Syukur; Andi Baso Kaswar; Riyama Ambarwati
Journal of Vocational, Informatics and Computer Education Vol 3, No 2 (2025): December 2025
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/voice.v3i2.263

Abstract

In the digital learning era, Artificial Intelligence in Education (AIED) functions not only as an academic support tool but is also becoming an object of emotional attachment among students. While such attachment may enhance learning motivation, it also raises concerns about emotional dependence and its implications for students’ social and emotional well-being. This study investigates the effects of commitment, enthusiasm, emotional closeness, and anthropomorphic perceptions on students’ emotional dependence on AIED. A quantitative cross-sectional survey was conducted with 109 university students in Makassar using a 1–5 Likert-scale questionnaire. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The structural model explained 62.7% of the variance in emotional dependence on AI (R² = 0.627), indicating moderate to strong explanatory power. Emotional closeness (β = 0.324; t = 2.893; p = 0.004) and anthropomorphic perception (β = 0.440; t = 4.871; p < 0.001) significantly increased emotional dependence, whereas commitment to continued AI use (β = 0.092; t = 0.883; p = 0.377) and enthusiasm toward AI (β = 0.081; t = 0.901; p = 0.367) were not significant predictors. These findings suggest that emotional dependence is driven more by affective engagement and the perception of AI as socially human-like than by cognitive motivation or usage intention. AIED interaction therefore extends beyond functional support into a relational experience resembling interpersonal connection. Given the limited geographic scope, future studies should involve broader populations and employ mixed-method approaches to deepen understanding of emotional dynamics in AIED use.
Integrasi ChatGPT dalam Blended Learning dalam Mengoptimalkan Pemahaman Materi Pembelajaran Aminuddin; Nurmila; Pramudya Asoka Syukur; Nurul Islamia; Andi Dio Nurul Awalia
Journal of Vocational, Informatics and Computer Education Vol 2, No 2 (2024): December 2024
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61220/voice.v2i2.20246

Abstract

Kemajuan teknologi menghadirkan tantangan bagi perguruan tinggi untuk menghasilkan lulusan yang tidak hanya menguasai ilmu pengetahuan, tetapi juga mampu memanfaatkan teknologi digital dalam mendukung produktivitas dan daya saing. Penelitian ini bertujuan untuk menganalisis pengaruh integrasi blended learning dan ChatGPT terhadap pemahaman materi, efisiensi pembelajaran, dan pengalaman penggunaan di perguruan tinggi. Metode yang digunakan adalah pendekatan kuantitatif dengan desain penelitian cross-sectional dan pengumpulan data melalui kuesioner menggunakan skala Likert. Hasil penelitian menunjukkan bahwa blended learning secara efektif meningkatkan pemahaman materi dengan partisipasi aktif dalam diskusi, ChatGPT mendukung motivasi dan kreativitas mahasiswa dalam belajar, serta kombinasi keduanya meningkatkan efisiensi pembelajaran melalui penghematan waktu dan akses informasi yang lebih baik. Mayoritas responden memberikan tanggapan positif terhadap penerapan model ini, mencerminkan keberhasilan integrasi teknologi dalam pembelajaran. Hasil ini juga mendukung relevansi blended learning dan ChatGPT sebagai solusi inovatif dalam memenuhi kebutuhan pembelajaran modern. Penelitian ini mengimplikasikan bahwa integrasi teknologi dalam pendidikan dapat mempercepat transformasi pembelajaran yang lebih efektif dan fleksibel.
Co-Authors Al Haytsam Mappaita Aminuddin Andi Baso Kaswar Andi Dio Nurul Awalia Aprilianti Nirmala S Asmi Ulfiah Marwan Ramdhany Edy Nurmila Nurul Islamia Riyama Ambarwati Sardar Faroq Ahmd Khan