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All Journal Information Technology Education Journal Journal of Embedded Systems, Security and Intelligent Systems Artificial Intelligence in Educational Decision Sciences Journal of Vocational, Informatics and Computer Education Journal of Educational Studies in Science, Technology, Engineering, Arts and Humanities
Pramudya Asoka Syukur
Universitas Negeri Makassar
Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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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.
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.
Mengukur Kompetensi Pengguna Dalam Menggunakan Kecerdasan Buatan: Validasi dan Reliabilitas Skala Literasi Kecerdasan Buatan Andi Sarifa Safitri Bachmid; Annisa Syah Shadira; Andhika Dwi iswanto; Pramudya Asoka Syukur
Journal of Educational Studies in Science, Technology, Engineering, Arts and Humanities Vol.1 No.1 (2025): September 2025
Publisher : PT. Global Research Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Penelitian ini bertujuan untuk mengembangkan dan memvalidasi sebuah skala literasi kecerdasan buatan (AI) yang dapat digunakan untuk mengukur kompetensi pengguna dalam menggunakan teknologi AI. Skala ini dirancang untuk membantu menilai sejauh mana individu dapat memahami, mengintegrasikan, dan mengoptimalkan kecerdasan buatan dalam berbagai konteks. Metode penelitian dilakukan melalui serangkaian tahapan yang mencakup pengembangan item skala, uji coba awal, uji validitas konten, dan analisis faktor eksploratori. Hasil dari penelitian ini menunjukkan bahwa skala literasi kecerdasan buatan ini memiliki validitas dan reliabilitas yang tinggi. Skala ini dapat digunakan untuk mengukur pemahaman dan keterampilan pengguna dalam berinteraksi dengan AI, serta memberikan dasar yang kuat untuk mengidentifikasi area pengembangan potensial. Temuan ini memiliki implikasi penting dalam pengembangan pendidikan dan pelatihan terkait kecerdasan buatan, serta dalam pengukuran kemampuan pengguna dalam mengadopsi teknologi AI dalam berbagai aspek kehidupan mereka. Skala literasi kecerdasan buatan ini dapat menjadi alat yang berharga dalam mengukur tingkat kesiapan dan kompetensi individu dalam menghadapi era AI yang semakin berkembang pesat.
Agile Development of PressAI: Enhancing Educational Efficiency through QR-Based Attendance and AI-Powered Essay Scoring M. Miftach Fakhri; Pramudya Asoka Syukur; Muh Idul Akbar Pratama; A.M Yusran Mazidan; Alya Olivia; Rosidah
Information Technology Education Journal Vol. 3, No. 3, September (2024)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v3i3.5092

Abstract

In learning activities, common issues such as time-consuming and manipulation-prone attendance processes, as well as subjective and inconsistent essay grading, often arise. This study aims to develop PressAI, an AI-based application that automates attendance using QR code technology and provides objective essay grading powered by the OpenAI API. The application was developed using the Agile methodology integrated within the Software Development Life Cycle (SDLC) framework, through iterative stages including planning, requirements analysis, system design, implementation, and testing. The results demonstrate that PressAI enables instant and accurate attendance recording via QR scanning, provides a seamless login experience without repeated authentication, and delivers fast and objective essay assessments. With its features, PressAI is expected to enhance efficiency and transparency in the learning process while offering opportunities for further innovative feature development.
Development of Cloud-Based Taskify Application For Time Management Nur Fadhylah As; Muh. Rahmat Wahyudi JY; Annajmi Rauf; Pramudya Asoka Syukur; Andi Dio Nurul Awalia; M. Miftach Fakhri
Journal of Embedded Systems, Security and Intelligent Systems Vol 5, No 2 (2024): July 2024
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v5i2.5041

Abstract

In the 21st century digital era, advances in technology and information have developed rapidly, one of which is cloud computing. This study aims to design a cloud-based task management application called TaskIfy using firebase technology and agile methods. TaskIfy was designed to help users manage their time and daily activities more effectively. The Agile method was used in two sprint cycles to ensure iterative development and responsiveness to user feedback. The main features implemented include authentication, task management, search, and calendars. Black box testing was conducted to ensure the functionality of the application. The results showed that TaskIfy successfully improved user efficiency and productivity in managing schedules and completing tasks. However, some additional features have not been developed, such as better calendar integration and collaboration features. Future research can focus on developing these features to optimize the user experience. The main contribution of this research is the implementation of TaskIfy as a practical tool for effective time management, combining cloud computing technology and agile methodology to improve efficiency in everyday life.
StudySync Mobile Application Design for Student Academic Activity Management Based on SQLite Database Arsyanda; M. Miftach Fakhri; Pramudya Asoka Syukur; Devi Miftahul Jannah; Elma Nur Jannah; Annajmi Rauf
Journal of Embedded Systems, Security and Intelligent Systems Vol 5, No 3 (2024): November 2024
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v5i3.5042

Abstract

The development of information and communication technology has made significant contributions in various sectors, including education. Mobile applications have become an effective solution in improving time management and reducing academic procrastination among students. This research aims to design and develop a mobile application called StudySync that utilizes SQLite database to assist students in organizing their academic activities. The development method used is the Agile method with three sprint cycles, ensuring incremental improvements and continuous validation of features. The application offers task management features, note-taking, reminders, and a search system to facilitate the management of academic information. SQLite was chosen as the main database due to its self-contained, serverless, zero-configuration, and transactional nature, suitable for mobile applications that require fast and reliable database access. The test results show that the StudySync application successfully meets the needs of users in organizing academic assignments and notes and improving student time management.
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.
Co-Authors A.M Yusran Mazidan Al Haytsam Mappaita Alya Olivia Aminuddin Andhika Dwi iswanto Andi Baso Kaswar Andi Dio Nurul Awalia Andi Sarifa Safitri Bachmid Annajmi Rauf Annisa Syah Shadira Aprilianti Nirmala S Arsyanda Asmi Ulfiah Devi Miftahul Jannah Elma Nur Jannah M. Miftach Fakhri Marwan Ramdhany Edy Muh Idul Akbar Pratama Muh. Rahmat Wahyudi JY Nur Fadhylah As Nurmila Nurul Islamia Riyama Ambarwati Rosidah Rosidah Sardar Faroq Ahmd Khan