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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 305 Documents
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Analysis of User Visual Preferences for an Agricultural Website Interface Using User-Centered Design: A Case Study of Ndaru Farm Qois Ari Kurnia; Yulison Chrinanto; Wina Witanti
Information Technology Education Journal Vol. 5, No. 2, May (2026)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v5i2.12230

Abstract

Purpose – This study examines users' visual interface preferences for the Ndaru Farm agricultural website. It addresses the need for agricultural digital platforms that are readable, accessible, and suitable for demographically diverse users, particularly adult and older users. Design/methods/approach – A quantitative descriptive survey was conducted using a structured Google Forms questionnaire distributed through farming communities. Thirty-three respondents participated through accidental sampling. The questionnaire measured preferences for four visual interface elements: color, typography, iconography, and layout. Data were analyzed using frequency percentages, while a Pearson Chi-Square test was used as an exploratory analysis of the relationship between age group and layout preference. Findings – Respondents were dominated by users above 40 years old (57.6%). All respondents preferred green as the main interface color (100%), mainly because it looked natural (57.6%). Medium-sized typography was preferred by 90.9% of respondents, while realistic icons were selected by 54.5%. For layout, 63.6% preferred top navigation and 51.5% preferred a single-column structure. The Pearson Chi-Square test suggested an exploratory association between age group and layout preference, chi-square = 10.712, df = 4, p = 0.030, but the expected-cell assumption was not met. Research implications/limitations – The findings provide preliminary design input for improving agricultural website interfaces, but the small accidental sample and sparse expected-cell frequencies limit generalizability. Further usability testing and exact statistical testing with representative users are required. Originality/value – This study extends User-Centered Design discussion to Indonesian agricultural website design by linking visual interface preferences with age-related accessibility considerations.
Generative Artificial Intelligence for teachers’ Efficacy in Teaching Computational Thinking Bangun Panduko Johan; Cucuk Budiyanto; Jamaludin Salim
Information Technology Education Journal Vol. 5, No. 2, May (2026)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v5i2.281

Abstract

Purpose – This study aims to investigate the development and application of GenAI in CT education, and to analyse the challenges of using GenAI for CT learning. Design/methods/approach – Systematic Literature Review (SLR) was adopted in this research The keywords “Generative AI,” “educators,” and “Computational Thinking” were used to nominate 22 articles from Scopus and DOAJ published between 2022 to 2026. Under PRISMA procedure, the data were organised in a concept matrix and analysed with thematic analysis to identify recurring patterns, development approaches, implementation practices and challenges. Findings – The results show that GenAI applications support CT education by integrating with Learning Management Systems, generating quizzes and lesson content through APIs, using prompt engineering, providing coding assistance, giving automated feedback, decomposing problems, enabling adaptive interaction, and supporting personalised learning. Thematic analysis revealed three major categories of challenges: inaccuracies and biases in AI-generated output; pedagogical limitations in terms of teacher competence and over-reliance by students; and ethical and equity issues involving plagiarism, data privacy, unequal access, and responsible use. Research implications/limitations – GenAI facilitate CT education when used as a pedagogical adjunct, not a replacement for educators. To be effective, it needs to be supervised by teachers, validated by humans, driven by ethical awareness, supported by better AI literacy, and guided by sound pedagogical principles. Originality/value – Computational Thinking (CT) has been introduced as the core competency to improve students’ problem-solving skills. The implementation of CT, however, remains challenging due to limited educator readiness, difficulties in designing CT-based learning activities, misconceptions that CT is mainly related to programming, and the need for adequate pedagogical support. Generative Artificial Intelligence (GenAI) has emerged as a promising technology to help educators in instructional-material development, feedback generation, programming assistance and problem-solving activities . But its use raises questions about output’s accuracy, dependency, plagiarism, data privacy, and unequal access concerns.
Utilization of RiceVision as Intelligent Learning Media to Support Smart Vocational Education in Quality Control Muhammad Zainal Altim; Abdullah Basalamah Abdi; Fadly Kasim Fadly
Information Technology Education Journal Vol. 5, No. 3, August (2026)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v5i3.12298

Abstract

Purpose – This study aimed to develop RiceVision, an intelligent learning media based on computer vision and machine learning, to support the implementation of Smart Vocational Education in the Quality Control Department at SMK SMTI Makassar. Design/methods/approach – The study employed a research and development framework using the Plomp model, which consisted of design, validity, practicality, and effectiveness testing stages. In the effectiveness testing phase, a quasi-experimental design was implemented involving control and experimental groups. The control group consisted of 36 students who learned through conventional methods, while the experimental group consisted of 36 students who learned using RiceVision. Findings – The validity test by four experts resulted in a score of 0.94, categorized as valid, while practicality tests achieved 90.75% and 93.75% scores at the one-to-one and small-group stages. The effectiveness test showed significantly higher posttest scores in the experimental group (84.86 ± 6.74) compared to the control group (71.31 ± 8.21), with a significant difference (t(70)=7.64, p<.001) and a very large effect size (Cohen's d=1.80), indicating that RiceVision effectively improved students’ conceptual understanding, digital literacy, and practical competencies in rice quality identification and analysis. Research implications/limitations – These findings indicate that RiceVision effectively facilitates contextual and technology-enhanced vocational learning. Overall, the RiceVision-based intelligent learning media was proven to be valid, practical, and effective, indicating its potential as an innovative solution for integrating artificial intelligence into vocational learning environments. The implementation of RiceVision contributes to strengthening digital literacy, improving students’ learning engagement, and supporting the realization of Smart Vocational Education in vocational high schools. Originality/value – This study presents RiceVision as an innovative solution that integrates artificial intelligence, computer vision, and machine learning into vocational education, particularly in quality control learning. The development of intelligent visual recognition features combined with interactive learning modules and project-based activities demonstrates its potential value in supporting contextual technology-enhanced learning and strengthening Smart Vocational Education implementation.
Development of Autoplay-Based Interactive Multimedia to Enhance Critical Thinking Skills and Mathematics Learning Outcomes on Decimal Fractions and Percentages for Grade IV Elementary School Students Nuzulia Fatimatur Rohmah; Nursiwi Nugraheni
Information Technology Education Journal Vol. 5, No. 3, August (2026)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v5i3.12467

Abstract

Purpose – The goal of this project is to develop a multimedia learning product called “Mainpec” (Multimedia Interaktif Pecahan) based on the Autoplay in order to enhance the critical thinking abilities and mathematical learning of fourth-grade elementary school students. Furthermore, the implementation of "Mainpec" is expected to enhance pretest and posttest results. Design/methods/approach – The Borg and Gall model was used to create the research and development product during the eight (8) stages of the process. Purposive sampling was used to choose nine students from class IV-B for the small group trial and thirty-one students from class IV-A for the large group trial at SDN Tugu Utara 14 Pagi Jakarta in Indonesia. Observation, interviews, a five-point Likert scale expert validation questionnaire, and pretest and posttest tools created using the Facione critical thinking framework were among the methods utilized to gather data (Facione, 2015). The validity, reliability, and normality of the instruments were tested prior to implementation in the educational environment. The reliability of the instruments was tested using Cronbach’s alpha, with the results showing a coefficient of 0.702 for the critical thinking skills instrument and 0.925 for the learning outcomes instrument. This value indicates that the instrument has acceptable internal consistency and is considered reliable. The data analysis techniques used included the normality test (Shapiro-Wilk), paired sample t-test, and the N-gain measure. Findings – The media, the content, and the language all scored 92.5%, 92.5%, and 93.75%, respectively, with an average of 93% of “Highly Feasible,” according to the expert validation results. With p-values of less than 0.001 and a gain score of 0.60 for both variables, this indicates an improvement from pretest to posttest results after using the media. Research implications/limitations – These findings show that utilizing Autoplay-based interactive multimedia with PBL syntax can enhance primary school students’ critical thinking and mathematical learning outcomes. Nevertheless, this study’s shortcomings include the use of a single school, the lack of a control group, the short duration of each group’s two class meetings, and the use of a single mathematics topic. Originality/value – The integration of PBL syntax with Autoplay-based multimedia is the primary contribution of this study. Particularly when it comes to calculating decimal fractions and percentage, this branch of fundamental mathematics has not gotten much attention. This product was developed for offline implementation in classroom learning. This allows schools with limited internet access to still integrate it into their learning activities.
Generative Artificial Intelligence Use, AI Self-Efficacy, and Higher Order Thinking Skills Among Education Students in Indonesia Erlangga Erlangga; Dedi Rohendi; Eki Nugraha; M. Miftach Fakhri
Information Technology Education Journal Vol. 5, No. 3, August (2026)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v5i3.12693

Abstract

Purpose – This study examined the association between generative artificial intelligence (GenAI) use and Higher Order Thinking Skills (HOTS) among education students. It also investigated whether students' confidence in using AI, operationalized as AI self-efficacy,  significantly predicts the probability of high HOTS attainment. Design/methods/approach – A quantitative explanatory research design was employed. Data were collected through a five-point Likert-scale questionnaire administered to education students at a single Indonesian university. Binary logistic regression was applied to model the probability of high HOTS attainment, with HOTS dichotomized at the median composite score into two categories: high HOTS (coded 1) and low HOTS (coded 0), using AI use frequency (X1) and AI self-efficacy (X2) as predictors. Findings – The binary logistic regression model (logit(π) = −0.741 − 0.134X1 + 1.891X2) reveals that AI use frequency (X1) is not a statistically significant predictor of high HOTS attainment (Wald = 0.073, p = 0.787). In contrast, AI self-efficacy (X2) emerges as the sole significant predictor, with students reporting higher AI self-efficacy showing significantly greater odds of being classified in the high-HOTS category (OR = 6.623, p = 0.001). With an overall classification accuracy of 73.3% and a balanced accuracy of 61.6%, the Hosmer-Lemeshow goodness-of-fit test confirms adequate model fit, though the model's limited specificity for low-HOTS cases reflects a class imbalance in the sample. Research implications/limitations – The findings suggest that higher education institutions should focus on developing students' AI self-efficacy rather than merely expanding access to GenAI tools, since self-efficacy not access alone is the factor associated with higher-order thinking. Lecturers are encouraged to design learning activities that promote critical and reflective engagement with AI rather than passive use. However, reliance on self-report data introduces the risk of perceptual bias, as students' self-assessed HOTS may not fully reflect actual cognitive performance. Further, because the design is cross-sectional, the findings cannot be used to infer long-term causal relationships. Special attention should also be paid to the generalization of the results as it is narrowed to a limited number of students of education at a single institution. Originality/value – This study is original in its application of binary logistic regression to probabilistically model high HOTS attainment in the Indonesian AI-education context, an approach that has not been widely adopted in prior research in this setting to predict HOTS on the scenario of AI-based higher education in Indonesia.
Improving IPAS Learning Outcomes Through STEAM-Based Scratch Media with the Suroboyoan Dialect Context for Fifth-Grade Elementary School Students Safriana Ika Pratiwi; Suryanti Suryanti; Putri Rachmadyanti; Neni Mariana
Information Technology Education Journal Vol. 5, No. 3, August (2026)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v5i3.12820

Abstract

Purpose - The primary objective of this empirical inquiry centers on evaluating how digital materials utilizing a specific local linguistic framework relate to variations in the educational achievements of fifth-grade pupils regarding regional historical traditions. Methodologically, a non-randomized group approach under a non-experimental framework was deployed, involving forty elementary school pupils who were distributed equally into two distinct cohorts. Information gathering relied on pre-intervention and post-intervention evaluations alongside structured behavioral rubrics. Analytical procedures incorporated exploratory indicators, distribution verification through mathematical non-parametric tests, variance consistency evaluations, progress metrics, and a comparative mean assessment. Findings: metrics indicated that the cohort exposed to the interactive framework achieved an average final evaluation of 78.03 alongside a moderate progress index of 0.588. Conversely, the traditional cohort reached an average final score of 55.00 with a minimal progress index of 0.188. A directional comparison confirmed a statistically meaningful divergence between the two groups, t(38) = -8.470, p < 0.001, proving that the digital intervention co-occurred with distinct variations in the educational metrics of the participants. Classroom observations indicated that the integration of the local Suroboyoan dialect functioned as a contextual scaffold that supported active student participation and natural communication. Implications - The integration of Scratch programming, interdisciplinary STEAM design parameters, and local vernacular may provide a responsive instructional alternative under the Merdeka Curriculum framework. However, generalized claims of effectiveness are limited due to a small, localized sample from a single school and a relatively short intervention timeframe. Originality - This study contributes to elementary educational technology models by combining digital visual block programming, STEAM frameworks, and regional dialects into a single contextual scaffold for IPAS learning.
Customer Segmentation Using RFM Analysis and the K-Means Clustering Algorithm to Support Data-Driven Marketing Strategies Rifat Naufal; Alamsyah
Information Technology Education Journal Vol. 5, No. 3, August (2026)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v5i3.12821

Abstract

Purpose – Customer segmentation plays an important role in supporting data-driven marketing decision-making. This study aimed to analyze and classify customers according to their buying pattern characteristics by implementing the RFM (Recency, Frequency, Monetary) technique in the K-Means Clustering algorithm.  Design/methods/approach– This study used the public Marketing Campaign dataset from Kaggle through several phases, covering data preprocessing, RFM model construction, logarithmic conversion, information standardization, identification of the most suitable number of clusters through the elbow technique and assessment through the Silhouette Score. Findings – The results showed that the most suitable number of clusters was k=3 with a Silhouette Score measurement of 0.503, indicating moderate clustering quality with acceptable cluster cohesion and separation. The resulting segmentation consisted of three main clusters, namely Loyal Customers, Need Attention Customers and At Risk Customers, where each cluster had different contribution characteristics and potential for the company. Research implications – The segmentation results provide practical recommendations that may help companies maintain Loyal Customers, improve engagement among Need Attention customers, and reduce customer churn risk through more targeted marketing strategies. This study used a single public dataset; therefore, the segmentation results may differ when applied to other datasets or industrial sectors. Originality/value – The originality of this study lies in the application of RFM-based customer value transformation prior to K-Means clustering on the Marketing Campaign dataset. This approach provides interpretable customer segmentation and behavioral insights that support data-driven marketing decision-making. The findings suggest that integrating RFM analysis with K-Means clustering can generate meaningful customer segments and provide actionable insights for customer retention, re-engagement, and loyalty management.
Project-Based Learning Based on Collaborative Reflection (PJBL-CR) as a Learning Innovation in Pancasila Education: A Qualitative Study of Fifth-Grade Elementary School Students Feby Arief Nugroho; Ida Wahyu Wijayati; Marsini Marsini; Unik Ambar Wati
Information Technology Education Journal Vol. 5, No. 3, August (2026)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v5i3.12834

Abstract

Background – Pancasila Education in elementary schools is often implemented through teacher-centered approaches, which may limit students’ active participation and opportunities to relate Pancasila values to everyday experiences. Therefore, learning innovations that provide meaningful, collaborative, and reflective learning experiences are needed to support character development and 21st-century competencies. Objective: This study aimed to describe the implementation of Project-Based Learning with Collaborative Reflection (PJBL-CR) as a learning innovation in Pancasila Education for fifth-grade elementary school students. Method – This study employed a qualitative descriptive approach involving 14 fifth-grade students and one Pancasila Education teacher at SDN Kraton 6, Magetan, Indonesia. Data were collected through classroom observations, semi-structured interviews, student reflection journals, and documentation. The data were analyzed using the interactive model of Miles and Huberman, consisting of data reduction, data display, and conclusion drawing. Results – The findings indicate observed changes in students’ learning activities during the implementation of the PJBL-CR model. Descriptive classroom observations showed increases across five indicators: active participation in discussions increased from 42% to 86%, ability to express opinions from 35% to 79%, group collaboration from 50% to 88%, participation in project activities from 47% to 90%, and contextual understanding of Pancasila values from 53% to 84%. These percentages were used as descriptive support for the qualitative findings and were not intended as inferential statistical evidence. Student reflection journals and interview data further suggest that collaborative reflection helped students examine responsibility, tolerance, mutual cooperation, and democratic decision-making during project activities. Conclusion – The PJBL-CR model may serve as an alternative learning strategy for strengthening character education and 21st-century competencies in elementary school contexts. The novelty of the model lies in the systematic integration of collaborative reflection throughout project-based learning phases, enabling students to connect project experiences with the practical implementation of Pancasila values. However, because this study was limited to a single classroom with a small number of participants, further studies involving broader educational contexts are recommended.
Development and Validation of a Web-Based Thesis Title Submission Information System Using the ADDIE Model at Indonesian Maritime Polytechnic Endang Lestari; Masrupah; Wardimansyah Ridwan; Muhammad Hidayat L
Information Technology Education Journal Vol. 5, No. 2, May (2026)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v5i2.279

Abstract

Purpose – This study aimed to develop and validate a web-based thesis title submission information system for Merchant Marine Polytechnic of Makassar and to examine cadets' user acceptance of the system. Design/methods/approach – This research employed a research and development design using the ADDIE through observation, documentation, expert-validation questionnaires, user-acceptance questionnaires, and supporting interviews. Data were analyzed descriptively using mean scores, percentages, and an aggregate Aiken-type validity index because only aspect-level validation data were available. Findings – Expert validation showed very high feasibility: information system introduction (M = 4.63; AVI = 0.91), user control (M = 4.83; AVI = 0.96), system interface (M = 4.50; AVI = 0.88), and final system performance (M = 4.50; AVI = 0.88). The small-group trial obtained M = 4.36 (87.27%), indicating a very practical system. The field trial showed positive user acceptance in ease of use (M = 4.22; 84.40%) and interface quality (M = 4.16; 83.20%). Research implications/limitations – The system can potentially support more efficient and better documented thesis title submission services. However, the evaluation was limited to one maritime polytechnic, did not include objective operational comparison with the manual workflow, and could not report reliability or dispersion statistics because item-level data were unavailable. Originality/value – This study contributes a documented ADDIE-based development and validation process for a multi-role academic administration system in a maritime higher education context, covering cadet submission, supervisor review, program-head validation, status tracking, and controlled institutional piloting readiness.
A Fuzzy Logic Approach for Student Performance and Exam Integrity Assessment in a Web-Based Smart Quiz System in Higher Education I Wayan Sugianta Nirawana; Ramaulvi Muhammad Akhyar; Eliyah Achanta Manapa Sampetoding
Information Technology Education Journal Vol. 5, No. 3, August (2026)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v5i3.12904

Abstract

Purpose – This research aims to develop a Web-Based Smart Quiz System that integrates performance analytics and user behavior monitoring to support more objective and comprehensive learning evaluations. The problem raised is the limitations of the online quiz system in combining fuzzy logic-based performance analysis with exam integrity supervision on the system dashboard. Design/methods/approach – This system is developed using a prototype model.  Student performance and exam integrity were analyzed using a Mamdani fuzzy logic approach based on four input variables, namely score percent, integrity, attempt count, and time ratio. For fuzzy inference processing, warning count, tab switch count and fullscreen exit count were aggregated into a single integrity violations index, which was used as the integrity variable. The system is also equipped with examination integrity features, an analytics dashboard, and question item analytics to support adaptive evaluation and examination integrity assessment. Findings – The results showed that 81 quiz attempts were classified into 33 Superior, 39 Good, 7 Enough, and 2 Needs Coaching. Risk analysis showed that 79 attempts were in the Safe category, while 2 attempts required further attention. These results indicate that the proposed system can support adaptive performance evaluation while facilitating examination integrity monitoring during online assessment. Research implications/limitations – The study was conducted within a single higher education institution and relied on predefined membership functions and rule bases. Future studies should involve larger and more diverse populations and explore longitudinal validation of the model. Originality/value – This study presents a prototype-based integrated assessment platform that combines fuzzy performance evaluation, examination integrity monitoring, user behaviour analytics, and question item analytics within a single web-based Smart Quiz System. The proposed platform is intended as a decision-support tool for learning evaluation and provides a foundation for future validation and enhancement using larger datasets and advanced analytics methods.

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