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All Journal Jurnal Pengolahan Hasil Perikanan Indonesia Pendas : Jurnah Ilmiah Pendidikan Dasar Perdikan: Journal of Community Engagement Inspiring: English Education Journal Jurnal Pengabdian kepada Masyarakat Nusantara Window of Public Health Journal Abdimas: Papua Journal Of Community Service GOVERNANCE: Jurnal Ilmiah Kajian Politik Lokal dan Pembangunan Jurnal Guru Pencerah Semesta (JGPS) Information Technology Education Journal Jurnal Teknologi Kimia Mineral Jurnal Abdimas Indonesia Al Ushuliy An-Nafis: Jurnal Ilmiah Keislaman dan Kemasyarakatan PEDAMAS (Pengabdian Kepada Masyarakat) Journal of Applied Linguistics and Literature Ahsan: Jurnal Ilmiah Keislaman dan Kemasyarakatan El-Madaniyah: Jurnal Pengabdian Masyarakat JPT : Jurnal Pendidikan Tematik Jurnal Ilmu Hukum
Nurhikma
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Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Public Health Social Sciences Other

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Journal : Information Technology Education Journal

 1 
Comparison of Project-Based Learning and Lecture-Based Learning in Machine Learning Courses on Model Implementation Skills A. Sultan Agung; Hamzah Pagarra; Nur Asima; Nur Azizah; Nurhikma; Nurilmi Amalia Marda; Nurlisah
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.v4i4.11192

Abstract

The aim of this study was to compare the effectiveness of Project-Based Learning (PBL) and Lecture-Based Learning (LBL) in enhancing students’ ability to implement machine learning models. While both teaching methodologies are widely used, the impact of PBL on practical machine learning skills has not been sufficiently explored. This study investigates whether a hands-on, project-based approach leads to better performance in real-world machine learning applications compared to a traditional lecture-based approach. This experimental study involved 60 undergraduate students from a machine learning course, randomly divided into two groups: PBL and LBL. The PBL group worked on real-world machine learning projects, while the LBL group followed traditional lectures and individual assignments. Data were collected using pre- and post-test questionnaires, project performance rubrics, and observational notes. Statistical analyses were conducted to compare the two groups’ performance on machine learning tasks. The results revealed that the PBL group outperformed the LBL group in model accuracy, code quality, problem-solving, and debugging skills. The PBL group also demonstrated greater motivation and engagement, with statistically significant differences in performance (p = 0.001 for model optimization and p = 0.027 for problem-solving). The LBL group showed improvements, but the gains were less substantial. The findings suggest that PBL is more effective for developing practical skills in machine learning. However, the study's limitations include a small sample size and short duration, which may limit the generalizability of the results. This study provides novel insights into the benefits of PBL in machine learning education, offering valuable implications for curriculum design. Future research could explore long-term outcomes and the potential of hybrid teaching methods that combine PBL and LBL
Analysis of the Acceptance of Generative AI Use in Academic Tasks Using the UTAUT Model Nurhikma; Rabiatul Adawiah; Rachmat Hidayat Bachtiar; Rahma Agustini Putri; Rahmadinar Kadir; Rahmat Hidayat
Information Technology Education Journal Vol. 4, No. 1, February (2025)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v4i1.2503

Abstract

This study aims to examine students’ acceptance of Generative Artificial Intelligence (AI) in academic tasks using the Unified Theory of Acceptance and Use of Technology (UTAUT). The rapid integration of Generative AI tools in higher education raises important questions regarding the determinants of students’ behavioral intention and actual usage. This study argues that performance-related perceptions are the primary drivers of adoption. Design/methods/approach – A quantitative explanatory design was employed using a survey of 210 undergraduate students who had experience using Generative AI for academic purposes. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Measurement evaluation included outer loadings, Cronbach’s Alpha, Composite Reliability, and Average Variance Extracted (AVE), while structural relationships were tested using bootstrapping with 5000 resamples. Findings – Performance Expectancy significantly influenced Behavioral Intention (β = 0.41, p < 0.001), followed by Effort Expectancy (β = 0.27, p < 0.001) and Social Influence (β = 0.18, p = 0.003). Behavioral Intention strongly affected Use Behavior (β = 0.53, p < 0.001), and Facilitating Conditions also had a significant direct effect (β = 0.29, p < 0.001). The model explained 62% of the variance in Behavioral Intention and 58% in Use Behavior. Research implications/limitations – The study was limited to a single institution and relied on self-reported cross-sectional data, which may restrict generalizability and causal inference. Originality/value – This study extends UTAUT to the context of Generative AI in academic assignments and provides empirical evidence of its predictive power in emerging AI-based educational technologies.
Co-Authors A. Ainun Fadillah A. Sultan Agung Abdul Hakim Adi Saputra Adnan, Andi Astinah Ainun Mardiah Al Mar Atu Soleha Anggita, Devi Arni Rizqiani Rusydi Astri Ramadani Astrid Fhatia Hamzah Butsainah, Aidah Citra Prasiska Puspita Tohamba, Citra Prasiska Puspita Diah Anggraini Wulandari Ediansyah Eko Nursalim Elisa Rani Erfina Fahrezi, Mohamad Egi Faisal Eka Mahendra fatonah, ratih Ferani, Tifani Haeruddin Hajrah Hamzah Pagarra Hasan Hilmi Hambali, Hilmi Idris Alfarizi Irfandi Idris Ishwanda Ramadani Ismail Latief Jaladara Bintara, Paksi Jenro P. Sijabat K, Nur Rahma Kurniati Ladahi, Megananda Lilis Aliska Maryam, Andi Maulana, Raja Fatur Miranda Mirsa Muh Fasral Hafid Muhammad Abdul Basir Muhammad Fatur Anggara Muhammad Wahyu Hidayat, Muhammad Wahyu Mulhama Tahir Murtafiah Mustikawati Mutmainnah S. Nanning Nur Asima Nur Aulia Rahma Nur Azizah Nurfadini Nurhayat Lestari Putri Nurilmi Amalia Marda Nurlisah Nursafitri Amin Nurul Ainun Azisah Padhilah Padhilah Putra, Rizky Anugrah Rabiatul Adawiah Rachmat Hidayat Bachtiar Radiah Rahma Agustini Putri Rahmadinar Kadir Rahmah, Auliyatur Rahmat Hidayat Rahmawati, Lili Rahmi, Nailur Reny, Elsy Rezki Amaliyah AR, Rezki Amaliyah Rizky Ekawaty Ahmad Sairul Satria, Irwan Siti Rahma Sri Diana Sri Yunita Sulkipli M. Supriadi Suriadi Suriadi Syafaatullah, Achmad Qodim Syamsudduha Syarifuddin, Hairul Totok Adhi Prasetyo Tri Velyna Triyan Triyan Yuniarti, Hera Zanuar Anwari