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Information Technology Education Journal
Published by Universitas Negeri Makassar
ISSN : 28097971     EISSN : 2809798X     DOI : -
Core Subject : Science, Education,
Computer Science & IT Education
INTEC Journal is published by the Informatics and Computer Engineering Education Study Program at Makassar State University. INTEC Journal is published periodically three times a year, containing articles on research results and / or critical studies in the field of Informatics and Computer Engineering Education from students, lecturers, and practitioners from universities or research institutions. The INTEC journal already has a print version ISSN with the number 2809-798X in 2022 and an online version ISSN with the number 2809-7971. INTEC Journal contains articles on informatics and computer engineering education in particular: learning multimedia e-learning/blended learning, information system, artificial intelligence and robotics, embedded expert system, big data and machine learning, software and network engineering
Arjuna Subject : Ilmu Sosial - Pendidikan
Articles 250 Documents
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The Effectiveness of Capture The Flag as a Network Security Practical Intervention on Students' Self-Efficacy and Learning Outcomes A. St.Khadijah Ridwan; Ummu Radiyah; Muhammad Ihksan Malik; Muhammad Nauval Pattiroi; Muslika; Nining Ulul Fajri; Nirwana Amir
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.v4i4.11191

Abstract

This study aimed to evaluate the effectiveness of Capture the Flag (CTF) competitions in improving self-efficacy and learning outcomes in network security education. Specifically, it explored whether participation in CTF competitions enhances students' confidence in their cybersecurity abilities and their understanding of network security concepts compared to traditional practical exercises. A quasi-experimental design was used with 60 undergraduate students enrolled in a network security course. Participants were randomly assigned to an experimental group (CTF competition) and a control group (traditional practical exercises). Data were collected through pre-test and post-test measures of self-efficacy, a final exam, and a practical skills assessment. The experimental group participated in a two-week CTF competition, while the control group engaged in structured, instructor-guided exercises. The results indicated that the experimental group showed a significant increase in self-efficacy (from 3.2 to 4.0) and performed better on both the final exam (85% vs. 75%) and the practical skills assessment (88% vs. 78%) compared to the control group. These findings suggest that CTF competitions positively impact students’ confidence and technical skills in network security. The study’s quasi-experimental design limits the ability to draw causal conclusions. Additionally, the relatively small sample size and single-institution setting reduce the generalizability of the findings. Future studies with larger and more diverse samples are needed to confirm these results. This study contributes to the growing body of research on gamification in cybersecurity education, showing that CTF competitions can effectively enhance both self-efficacy and learning outcomes. It highlights the value of integrating such competitions into curricula to improve student engagement and preparedness for real-world cybersecurity challenges
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.
Optimization of SVM Parameters Using PSO for Sensor-Based Water Quality Classification and Monitoring Dashboard Nurmawati Musri; Alimuddin; Rahmat Hidayat, T; Amar Maruf; Rahmat Ramdani; Ramadhani Nur Yasfi
Information Technology Education Journal Vol. 3, No. 2, Mei (2024)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v3i2.2402

Abstract

Purpose – This study aims to optimize Support Vector Machine (SVM) parameters using Particle Swarm Optimization (PSO) for sensor-based water quality classification and to integrate the optimized model into a real-time monitoring dashboard. The performance of SVM strongly depends on hyperparameter selection, and improper tuning reduces classification accuracy and generalization. This research argues that PSO-based optimization significantly improves classification performance while supporting operational environmental monitoring. Design/methods/approach – An experimental design was employed using 2,400 sensor observations collected from three river locations over 30 days, measuring pH, turbidity, dissolved oxygen, temperature, and total dissolved solids. After preprocessing, 2,320 valid records were analyzed. Standard SVM (RBF kernel) was compared with PSO-SVM using 5-fold cross-validation. PSO used 30 particles and 50 iterations to optimize C and γ within predefined search ranges. Performance metrics included accuracy, precision, recall, F1-score, confusion matrix, and paired-samples t-test (α = 0.05). Findings – PSO-SVM achieved 93.12% accuracy compared to 83.76% for standard SVM, reducing error rate from 16.24% to 6.88%. The mean accuracy improvement of 9.36% was statistically significant (t = 8.74, p = 0.001; Cohen’s d = 1.82). Optimal parameters were C = 47.83 and γ = 0.023. The dashboard demonstrated 0.12-second classification latency and 99.2% uptime. Research implications/limitations – Data were collected from only three locations and five sensor parameters, limiting generalizability. Originality/value – This study integrates PSO-optimized SVM with IoT-based dashboard monitoring, offering a replicable framework for intelligent and real-time environmental monitoring systems
Design of a VR-Based Virtual Laboratory for Network Practicum: The Effect of Immersion on Learning Outcomes Nurpadhillah Junaid; Ramdan Muaqib; Ramdani; Ratu Balgis Nur Latif; Rezki Aulia
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.v3i3.2401

Abstract

Purpose – This study aims to design and develop a Virtual Reality (VR)-based virtual laboratory for networking practicum and examine the effect of immersion on students’ learning outcomes. Traditional networking laboratories face limitations in infrastructure, accessibility, and scalability, while conventional desktop-based simulations lack immersive interaction. This study argues that immersive VR environments significantly enhance learning performance in networking practicum. Design/methods/approach – A quasi-experimental non-equivalent control group pretest–posttest design was employed involving 62 undergraduate students (32 experimental, 30 control). The experimental group used a VR-based networking lab developed using Unity and Meta Quest 2, while the control group used a conventional 2D simulator. Learning outcomes were measured using a validated 25-item test. Immersion was assessed using the Igroup Presence Questionnaire (IPQ). Data were analyzed using paired and independent samples t-tests, Pearson correlation, and regression analysis (α = 0.05). Findings – The experimental group achieved a significantly higher posttest mean score (M = 82.63, SD = 6.84) compared to the control group (M = 72.14, SD = 7.26), t(60) = 6.02, p < 0.001, with a large effect size (d = 1.48). Immersion showed a strong positive correlation with learning outcomes (r = 0.67, p < 0.001) and explained 45% of variance in posttest scores (R² = 0.45). Research implications/limitations – The study was limited to one institution, a moderate sample size, and short intervention duration, which may affect generalizability. Originality/value – This study provides empirical evidence of the pedagogical effectiveness of immersive VR in networking practicum and demonstrates the significant role of immersion in improving learning outcomes
The Effectiveness of Mastery Learning Supported by Adaptive Quizzes on Backpropagation Material in Improving Learning Outcomes Abd Akbar Sutiawan; Tangsi; Nurul Nikma Salsabila; Nailul Hajar B; Neli Agustin; Noor Edy Wijaya Sari; Nur Fadhillah M. Aripa
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.v4i4.11193

Abstract

This study investigates the effectiveness of Mastery Learning supported by adaptive quizzes in improving students’ understanding of backpropagation, a key concept in machine learning. The problem addressed is the difficulty students face in mastering complex topics like backpropagation and the limitations of traditional lecture-based teaching methods in facilitating deep learning. A pretest-posttest experimental design was employed with 60 undergraduate students. The experimental group (n=30) used Mastery Learning and adaptive quizzes, while the control group (n=30) followed traditional lecture-based instruction. Data was collected via pretests, posttests, and a survey assessing perceived learning. Paired t-tests and independent t-tests were used for statistical analysis. The experimental group showed a significant improvement in posttest scores (mean = 85.4) compared to the control group (mean = 65.3). The effect size was large (Cohen’s d = 1.2), indicating substantial improvements in learning outcomes. Survey results showed that 94% of students in the experimental group found the adaptive quizzes helpful in understanding backpropagation and expressed a preference for continuing with this method. The study demonstrates the potential of combining Mastery Learning with adaptive quizzes in enhancing learning in machine learning courses. Limitations include the small sample size and the focus on a single topic (backpropagation). The results may not be generalized to other topics or populations. This study contributes to the literature by showing the effectiveness of adaptive learning systems in technical subjects like machine learning. Future research could explore the scalability of this method and its applicability to other machine learning concepts or fields.
Development of an Android-Based Educational Game with Gamification for Algorithms and Data Structures Gita Damayanti; Miraekel Lebang Malik; Firdayani Syarifuddin; Ibnu Hajar Manippi; Eva Ulfiani; Era Fasira
Information Technology Education Journal Vol. 3, No. 2, Mei (2024)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v3i2.2401

Abstract

This study aims to develop and evaluate an Android-based educational game incorporating gamification elements for teaching Algorithms and Data Structures (ADS). ADS is recognized as a fundamental yet challenging subject in computer science education due to its abstract concepts and dynamic processes. Traditional lecture-based instruction often results in low engagement and limited conceptual understanding. Therefore, this study proposes a gamified mobile learning solution to enhance student motivation and learning outcomes. A Research and Development (R&D) approach using the ADDIE model was employed to design and develop the application, followed by a quasi-experimental non-equivalent control group pretest–posttest design to evaluate its effectiveness. Participants consisted of 74 undergraduate students divided into an experimental group (n = 38) using the Android-based gamified application and a control group (n = 36) receiving conventional instruction. Data were analyzed using paired and independent samples t-tests with a significance level of 0.05. Results indicated a significant difference in posttest scores (t = 6.64, p < 0.001), with the experimental group achieving a higher mean (M = 82.47) compared to the control group (M = 71.28). The effect size was large (Cohen’s d = 1.54). Motivation scores were also higher in the experimental group (M = 4.31), and usability evaluation yielded an excellent System Usability Scale (SUS) score of 81.45. The study is limited to a single institution and short intervention duration. This research contributes empirical evidence that gamified Android-based learning can significantly improve cognitive achievement and motivation in ADS courses.
The Effect of Generative AI as a Coding Assistant in Deep Learning Practicum on Code Quality and Conceptual Understanding Nurhidayah; Alimin; Ohfit Rijei; Owentianus Nouvic; Putra Langlang Buana; Putra Rajawijaya
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.v4i1.2502

Abstract

The rapid adoption of Generative AI as a coding assistant in programming education raises critical pedagogical questions regarding its impact on learning quality. This study investigates whether the use of Generative AI in a deep learning practicum enhances students’ code quality and conceptual understanding or merely improves productivity without meaningful comprehension. A quasi-experimental pretest–posttest control group design was employed involving 60 undergraduate students enrolled in a Deep Learning course. The experimental group (n = 30) used Generative AI tools (ChatGPT/GitHub Copilot) during practicum sessions, while the control group (n = 30) relied on conventional resources. Instruments included a validated conceptual understanding test (α = 0.87) and an analytic code quality rubric based on ISO/IEC 25010 standards (κ = 0.82). Data were analyzed using independent samples t-tests and MANOVA at α = 0.05. Results show that the experimental group achieved significantly higher posttest conceptual scores (M = 78.63) than the control group (M = 72.10), t(58) = 3.34, p = 0.001, d = 0.86. Code quality scores were also significantly higher (20.77 vs. 18.12 out of 25), t(58) = 4.57, p < 0.001, d = 1.18. MANOVA confirmed a significant combined effect (Wilks’ Λ = 0.71, p < 0.001). The study was limited to a single institution and a six-week intervention period, which may restrict generalizability and long-term interpretation. This research provides controlled experimental evidence demonstrating that Generative AI can enhance both technical code quality and conceptual mastery in deep learning education, contributing empirical guidance for responsible AI integration in computing curricula
Developing a Therapeutic Serious Game Based on Play Therapy to Intervene Verbal and Non‑Verbal Bullying Victims: An Experimental Study in School Students Dini Nur Saniyah; Almira Maharani; Andi Renny Nuraeni; Dewi Andrianisari; Almah Fitriah; Anugrah Azis
Information Technology Education Journal Vol. 3, No. 1, Januari (2024)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v3i1.02425

Abstract

This study aimed to develop and evaluate a therapeutic serious game integrating play therapy principles to improve emotional well-being, enhance coping skills, and reduce bullying-related distress among school-aged children. While prior research has focused on preventive serious games, interventions addressing the therapeutic needs of bullying victims remain limited. A randomized pretest–posttest experimental design was employed with 60 participants (aged 10–14 years) from two schools in Makassar, Indonesia. Participants were randomly assigned to an intervention group (n = 30) receiving the therapeutic serious game over four weeks or a control group (n = 30) receiving routine school support. Measures included the Bullying Victimization Scale, Emotional Well-Being Questionnaire, and Coping Skills Inventory for Children. Statistical analyses included paired-sample t-tests, independent t-tests, and ANCOVA to compare pre-post changes and between-group differences. The intervention group demonstrated significant improvements in emotional well-being (Δ = +15.6, p < 0.001), enhanced coping strategies (Δ = +13.2, p < 0.001), and reduced bullying-related distress (Δ = -15.5, p < 0.001) compared to the control group. Effect sizes were large, indicating strong therapeutic impact. The control group showed negligible changes. The findings confirm that therapeutic serious games can effectively support victims of verbal and non-verbal bullying by combining emotional expression, skill rehearsal, and scenario-based practice in an engaging digital format. This approach provides a scalable and evidence-based alternative to traditional play therapy, with potential integration into school counseling programs. Future research should examine long-term effects and cross-cultural adaptations.
Developing Microlearning and Counseling Chatbots for Preventing Online Doxing and Shaming among University Students: Feasibility and Usability Study Andi Nurjanna; Aan Saputra; Dewi Marlin; Ayu Qanita; A. Ratna; Indah Puteri
Information Technology Education Journal Vol. 3, No. 2, Mei (2024)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v3i2.2404

Abstract

The rapid proliferation of online harassment, particularly doxing and shaming, poses significant psychological and social risks for university students. Despite growing awareness campaigns, preventive interventions remain limited. This study aims to develop and evaluate a combined microlearning and chatbot counseling intervention to enhance students’ digital safety knowledge, preventive behavioral intentions, and engagement in higher education contexts. The key argument is that interactive, concise digital learning combined with AI-driven counseling can effectively reduce risks of online harassment. Design/methods/approach – A feasibility and usability study was conducted with 60 undergraduate students. Participants completed pre- and post-intervention assessments of knowledge and behavioral intentions, alongside a System Usability Scale (SUS) survey for chatbot evaluation. The intervention comprised bite-sized microlearning modules addressing online safety, reinforced with scenario-based interactions through a chatbot counseling system. Data were analyzed using paired t-tests, descriptive statistics, and correlation analyses to assess knowledge gains, behavioral intention changes, and engagement metrics. The intervention significantly improved participants’ knowledge scores (pretest mean = 54.2, posttest mean = 81.5, p < 0.001, Cohen’s d = 2.28) and preventive behavioral intentions (all dimensions p < 0.001). SUS scores averaged 82.3, indicating high usability, while engagement metrics revealed near-complete module completion (94%) and frequent chatbot interactions (mean 12.4 interactions per participant). Knowledge gains were positively correlated with module engagement (r = 0.61, p < 0.001). The study demonstrates the feasibility and efficacy of integrated microlearning-chatbot interventions, offering scalable preventive education strategies. Limitations include single-institution sampling, short intervention duration, and reliance on self-reported measures, which constrain generalizability. This study provides empirical evidence for combining microlearning and AI counseling in digital safety education, with potential for adaptation to other forms of online harassment or digital literacy initiatives. Future research should explore longitudinal impacts and multi-institution trials.

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