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All Journal Information Technology Education Journal Journal of Embedded Systems, Security and Intelligent Systems Journal of Vocational, Informatics and Computer Education Journal of Education for Creativity and Innovation Journal of Educational Studies in Science, Technology, Engineering, Arts and Humanities
Andi Dio Nurul Awalia
Universitas Negeri Makassar
Computer Science & IT Education Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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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.
Pengaruh Kolaborasi dengan AI terhadap Pengembangan Pola Pikir Desain dan Keterampilan Reflektif Mahasiswa Nur Azzahra; Agus Salim; Andi Dio Nurul Awalia
Journal of Education for Creativity and Innovation Vol. 1 No. 1 (2025): Agustus
Publisher : PT. Global Research Collaboration

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Abstract

Artificial Intelligence (AI) telah menjadi alat penting dalam pendidikan desain, membantu meningkatkan kreativitas, efisiensi, dan eksplorasi ide. Penelitian ini bertujuan mengevaluasi pengaruh kolaborasi dengan AI, khususnya melalui ChatGPT dan Midjourney, terhadap pola pikir desain, kreativitas, dan keterampilan reflektif mahasiswa. Metode kuantitatif digunakan dengan 76 mahasiswa sebagai responden yang menilai tiga aspek: pelatihan dan dukungan pengguna, kreativitas dan fleksibilitas, serta pengembangan keterampilan reflektif dan kreatif. Hasil menunjukkan bahwa persepsi mahasiswa berada pada kategori netral, dengan skor rata-rata 2,82, 2,75, dan 3,07. Mahasiswa melaporkan bahwa pelatihan dan panduan masih kurang memadai, AI terkadang kesulitan memahami konteks kreatif dan menjaga kesinambungan narasi, serta umpan balik reflektif yang diberikan terbatas. Temuan ini menegaskan bahwa meskipun AI mendukung percepatan proses kreatif, peran manusia tetap krusial untuk memastikan hasil desain yang orisinal, kontekstual, dan estetis. Penelitian ini menekankan perlunya peningkatan modul pelatihan, panduan lengkap, dan sistem umpan balik berkelanjutan agar penggunaan AI lebih efektif. Studi ini juga menunjukkan pentingnya lingkungan belajar kolaboratif di mana AI berfungsi sebagai pendukung kreativitas mahasiswa, sehingga mendorong pola pikir desain yang inovatif, adaptif, dan reflektif, serta kesiapan profesional di masa depan.
Analisis Pengaruh Faktor Keamanan Informasi dan Privasi terhadap Niat Berkelanjutan Penggunaan Asisten Kecerdasan Buatan Muhammad Agung Darmawan; Jalaluddin Basir; Andi Dio Nurul Awalia
Journal of Educational Studies in Science, Technology, Engineering, Arts and Humanities Vol.1 No.1 (2025): September 2025
Publisher : PT. Global Research Collaboration

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Abstract

Penelitian ini bertujuan untuk mengkaji pengaruh keamanan informasi dan privasi terhadap niat mahasiswa dalam menggunakan asisten Artificial Inteligence (AI) secara berkelanjutan. Fokus utama penelitian ini adalah kekhawatiran pengguna terhadap perlindungan data pribadi serta tingkat kepercayaan mereka terhadap sistem AI, yang dianggap berperan penting dalam menentukan keberlanjutan penggunaan teknologi ini. Dengan menggunakan metode kuantitatif berdesain deskriptif, data diperoleh melalui survei daring menggunakan kuesioner skala likert 5 poin, yang mencakup 20 pernyataan terkait empat aspek utama: penilaian penggunaan, kekhawatiran privasi terhadap AI, kepercayaan, dan kekhawatiran privasi di lingkungan sekitar. Sebanyak 90 mahasiswa aktif menjadi responden, dipilih melalui teknik purposive sampling. Hasil penelitian menunjukkan bahwa kekhawatiran privasi, terutama yang terkait dengan ruang publik, memiliki pengaruh signifikan terhadap niat pengguna, sementara pengaruh kepercayaan dan keamanan informasi cenderung lebih rendah. Penelitian ini menyimpulkan bahwa perlindungan privasi merupakan prioritas utama pengguna, sekaligus tantangan yang perlu diatasi oleh pengembang teknologi. Rekomendasi yang diberikan meliputi peningkatan transparansi pengelolaan data dan edukasi terkait keamanan informasi untuk memperkuat kepercayaan pengguna. Langkah-langkah tersebut diharapkan dapat mendorong penggunaan asisten AI yang lebih luas dan berkelanjutan di masa mendatang.
Penggunaan ChatGPT sebagai Media Pembelajaran Interaktif dalam Dunia Pendidikan: Analisis Pengaruh Kualitas Sistem Pendidikan dan Prestasi Akademik Peserta Didik M Nur Eqhy Putra Pratama; Aqil Ananda; Andi Dio Nurul Awalia; Rosidah
Journal of Educational Studies in Science, Technology, Engineering, Arts and Humanities Vol.1 No.1 (2025): September 2025
Publisher : PT. Global Research Collaboration

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Abstract

Kemajuan teknologi informasi, khususnya Artificial Intelligence (AI), telah membuka jalan baru untuk pendidikan. Studi ini menyelidiki cara ChatGPT, sebuah media pembelajaran interaktif, dapat meningkatkan sistem pendidikan dan meningkatkan prestasi akademik siswa. Dalam penelitian ini, data dikumpulkan dari 86 siswa melalui kuesioner online yang mengukur tiga variabel utama: kualitas media pembelajaran, pengaruh terhadap prestasi akademik, dan kualitas sistem pendidikan. Penelitian ini menggunakan analisis statistik deskriptif untuk menunjukkan tren dalam data yang dikumpulkan. Setiap variabel dihitung distribusi mean dan sumnya. Hasil penelitian menunjukkan bahwa ChatGPT meningkatkan kualitas media pembelajaran dan sistem pendidikan, tetapi dampak pada prestasi akademik tidak sebesar dua variabel lainnya. Studi ini menunjukkan bahwa ChatGPT adalah alat bantu pembelajaran yang efektif; namun, untuk memasukkannya ke dalam kurikulum, pelatihan dan pengembangan personal diperlukan. Hasil ini memberikan wawasan penting tentang penggunaan Artificial Intelligence (AI) dalam pendidikan dan rekomendasi tentang cara memanfaatkannya di masa depan. Penelitian ini membantu mendapatkan pemahaman yang lebih dalam tentang potensi ChatGPT untuk mendukung pembelajaran dan menemukan komponen yang mempengaruhi penerimaannya dan seberapa efektifnya dalam pendidikan.
Utilizing AI in Digital Learning: The Role of Metacognitive Reasoning, Organizational Support, and Socioeconomic Status in Enhancing Academic Performance through Intrinsic Motivation Andi Dio Nurul Awalia; Rosidah; M.Miftach Fakhri; Della Fadhilatunisa
Information Technology Education Journal Vol. 5, No. 1, February (2026)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v5i1.261

Abstract

Purpose – This study examines how metacognitive reasoning, organizational support, and socioeconomic status shape academic performance through the mediating role of intrinsic motivation in an artificial intelligence (AI) integrated learning environment. Design – A quantitative, cross-sectional design was employed. Data from 373 university students were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to evaluate relationships among the constructs. Findings – Metacognitive reasoning and organizational support significantly increased intrinsic motivation, which in turn positively affected academic performance. By contrast, socioeconomic status did not significantly influence intrinsic motivation or academic performance. Research implications – The results underscore the importance of cognitive and contextual supports in fostering student motivation and achievement. The nonsignificant effects of socioeconomic status warrant further investigation; future studies should explore additional individual and environmental factors that may shape academic outcomes in AI-enabled learning settings. Originality –  This research advances an inclusive and effective framework for digital learning in higher education by demonstrating the central role of metacognitive skills and institutional support in designing AI-enhanced strategies that cultivate students intrinsic motivation and academic success.  
A PLS-SEM Analysis of Basic Psychological Needs on Self-Regulation in Digital Learning: Insights from Self-Determination Theory Ahmad Faris Al Faruq; Muhammad Fardan; Andi Dio Nurul Awalia; Nurrahmah Agusnaya; M.Miftach Fakhri
Information Technology Education Journal Vol. 4, No. 4, November (2025)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v4i4.10813

Abstract

In the rapidly evolving digital age, technology-based learning has become integral to modern education, offering flexibility and accessibility while introducing challenges in student engagement and motivation. This study explores the relationship between basic psychological needs: autonomy, competence, and relatedness. Outlined in Self-Determination Theory (SDT) and self-regulated learning in digital environments. Utilizing Partial Least Squares Structural Equation Modeling (PLS-SEM), data was collected from 737 students to examine how these needs impact self-regulation in digital learning. The findings reveal that fulfilling these psychological needs significantly enhances students' self-regulation, leading to improved learning outcomes. Autonomy, particularly when supported by digital tools, and competence, bolstered by immediate feedback and digital literacy, are crucial for fostering effective self-regulation. Relatedness, although less influential, remains important in maintaining motivation through social connections in online learning. The study contributes to the growing body of literature on SDT by highlighting the importance of creating digital learning environments that cater to students' psychological needs, thereby enhancing motivation and academic success.
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.
Analysis of Naive Bayes and Support Vector Machine Algorithms in Classification of Diabetes Cases Based on Lifestyle Factors Andi Dio Nurul Awalia; Muhammad Fadhil Hani; Dewi Fatmarani Surianto
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 3 (2025): September 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i3.9783

Abstract

The increase in diabetes mellitus cases globally, including in Indonesia, demands a more adaptive lifestyle-based risk prediction strategy. This study aims to evaluate and compare the efficiency of Support Vector Machine (SVM) and Naive Bayes in the diabetes risk classification process using a Hybrid clustering-classification approach . The data analyzed in this study were obtained from the Kaggle platform , with 8,500 data of diabetes patients analyzed based on the attributes of age, gender, and smoking history. The Clustering process was carried out using K-Means (k=3), resulting in three main groups with different lifestyle characteristics. The classification results showed that Naive Bayes provided stable performance with an F1-score of 0.975. Meanwhile, SVM excelled in terms of F1-score 98.3% and perfect AUC (1,000), and was able to classify all data in cluster C3 without error. However, SVM recorded a higher classification error in the majority cluster . This study concluded that SVM was superior by 0.8% over Naive Bayes . Naive Bayes is more suitable for balanced data, while SVM is effective for detecting patterns in minority groups. These findings support the use of a hybrid approach in lifestyle data-based diabetes early detection systems. Future research directions include integrating additional variables and ensemble techniques to improve model generalization.
Digital Ethics and Learning Autonomy in Artificial Intelligence in Education: The Mediating Role of Trust in AI Nabilah Rahman; Elsa Natasya; Andi Dio Nurul Awalia; Muh. Yusril Anam; Della Fadhilatunisa
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.v3i2.262

Abstract

The rapid advancement of Artificial Intelligence in Education (AIED) has transformed digital learning practices while simultaneously raising critical concerns related to ethics, privacy, and user trust, which increasingly influence students’ ability to develop autonomous learning behaviors in AI-driven environments. This study aims to examine the relationships among Technology Readiness, Digital Learning Motivation, Digital Privacy Awareness, and Digital Ethics on Learning Autonomy, with Trust in AI serving as a mediating variable. A quantitative cross-sectional research design was employed involving 105 undergraduate students from Universitas Negeri Makassar, and data were analyzed using Partial Least Squares–Structural Equation Modeling (PLS-SEM). The results indicate that the proposed model explains 78.8% of the variance in Trust in AI and 84.3% of the variance in Learning Autonomy. Digital Learning Motivation shows a significant positive effect on Trust in AI and Learning Autonomy, while Digital Ethics also significantly influences both constructs; however, Technology Readiness and Digital Privacy Awareness do not significantly predict Trust in AI. Mediation analysis reveals that Trust in AI partially mediates the relationships between Digital Learning Motivation and Digital Ethics with Learning Autonomy. These findings demonstrate that psychological and ethical factors play a more decisive role than technical readiness in fostering trust and supporting autonomous learning in AIED contexts, highlighting the practical importance of integrating digital ethics education and motivational support into AI-based learning systems. Future research should employ longitudinal designs, broader samples, and additional variables such as AI literacy to further explore learning autonomy in AI-driven education.
Co-Authors Agus Salim Ahmad Faris Al Faruq Aminuddin Annajmi Rauf Aqil Ananda Della Fadhilatunisa Della Fadhilatunisa Dewi Fatmarani Surianto Elsa Natasya Jalaluddin Basir M Nur Eqhy Putra Pratama M. Miftach Fakhri M.Miftach Fakhri M.Miftach Fakhri Muh. Rahmat Wahyudi JY Muh. Yusril Anam Muhammad Agung Darmawan Muhammad Fadhil Hani Muhammad Fardan Nabilah Rahman Nur Azzahra Nur Fadhylah As Nurmila Nurrahmah Agusnaya Nurul Islamia Pramudya Asoka Syukur Rosidah Rosidah