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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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Implementation of AI-Enhanced Formative Assessment with Learning Analytics to Support Conceptual Understanding and Process-Oriented Learning of High School Students in Informatics Subjects Tara Lestari Safitri; Nuur Wachid Abdulmajid
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.v5i3.13491

Abstract

Purpose – Grade-oriented learning in Indonesian secondary education hinders deep conceptual understanding, as reflected in declining PISA scores. This study examines the potential of AI-based formative assessment (ChatGPT-Padlet) to support improvement in students’ conceptual understanding and a shift in their orientation from a focus on grades to a focus on process. Design/methodology/approach – This single-cycle classroom intervention, drawing on classroom-action-research procedures, involved 33 tenth-grade students in Project-Based Learning supported by a layered feedback system (AI, peers, and teachers). Data were collected through pre- and post-tests, Likert-scale attitude surveys, peer assessments, and qualitative reflections. Findings – The intervention produced a statistically significant improvement in conceptual understanding scores (Δ = 9.09 points; Wilcoxon Signed-Rank Test, Z = -1.93, p = 0.045, r = 0.34; paired t-test reported as a robustness check, p = 0.026) and a non-significant descriptive decrease in competitive orientation (Δ = -0.3; Wilcoxon p = 0.32), while process focus remained stable in mean (M = 3.5, Wilcoxon p = 0.77) with increased variability. Qualitative analysis showed that 67% of students could articulate internet concepts, and 58% showed indications of transfer of knowledge to practical decision-making in their self-reported reflections. Research implications/limitations –  The layered feedback ecosystem may offer a scalable approach to formative assessment in high teacher-student-ratio contexts, though this potential was not directly measured, and the single-cycle, short-duration design limits causal and longitudinal conclusions; further research requires longitudinal testing and more comprehensive deep learning metrics. Originality/value – This study offers an early empirical operationalization of process-oriented educational philosophy through an AI-peer-teacher triadic feedback model within the Indonesian Independent Curriculum context.
Weighted Similarity and Robustness Evaluation in a Case-Based Reasoning Expert System for Diagnosing Koi Fish Diseases Agunawan; Aulyah Zakilah Ifani; Muhammad Fadhlullah
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.v5i3.13665

Abstract

Purpose – Early diagnosis of koi fish diseases remains constrained by limited expert availability and the dominance of static rule-based expert systems that are less adaptive to new cases. This study aims to develop a practitioner-informed Case-Based Reasoning (CBR) screening prototype for six koi fish disease categories using positive-evidence weighted symptom similarity. Design/methods/approach – This research used a Research and Development design involving knowledge acquisition from two koi cultivator practitioner-experts, representation of 15 clinical symptoms, and the Retrieve-Reuse-Revise-Retain cycle. The web-based system was implemented using CodeIgniter 4, PHP 8.0, and MySQL 8.0. Similarity used positive-evidence weighted Jaccard with an insufficient-evidence gate (Σsi < 2). Evaluation comprised a preliminary usability test with 15 respondents and robustness testing on 500 synthetic base profiles transformed under six disturbance scenarios. Findings – Under KB-v2 with positive-evidence similarity, the illustrative case ranked Cloudy Eye at 69.6% while Fin/Tail Rot scored 0.0% (no shared-absence inflation). On the pooled legacy set, overall accuracy was 65.0% (97.3% among scored cases), weighted F1-score was 0.76, 33.2% of cases returned insufficient evidence, and 5.4% of scored cases were ambiguous. The usability test yielded 57.7% Good, 37.7% Fair, and 4.4% Poor item responses. Research implications/limitations – The evaluation used synthetic data generated from the same practitioner knowledge matrix and has not been validated by aquatic veterinarians or real clinical field cases. Originality/value – The study contributes positive-evidence weighted similarity with an insufficient-evidence gate, a governed retain pathway for expert-confirmed cases, and robustness testing with decision-safety metrics under incomplete and noisy inputs.
Gradient Boosting Models with Optuna Hyperparameter Optimization for Contemporaneous Wind Turbine Active Power Estimation at Esenkoy Wind Farm Muhammad Naufal Rustiawan; Yahya Nur Ifriza
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.v5i3.13674

Abstract

Purpose – Wind power estimation is critical for grid stability. This study tests whether Bayesian-tuned gradient boosting, using a leakage-safe pipeline, can estimate turbine power without the Theoretical Power Curve (TPC), benchmarked against LSTM. Design/methods/approach – The study uses Esenkoy SCADA and 2018 MERRA-2 weather data (8,760 hourly observations), split before any transformation; outlier bounds are fitted on training data only, and this dataset needed no imputation. TPC is excluded as a redundant, deterministic function of wind speed. Four gradient boosting models are tuned via Optuna-TPE with 5-fold CV; an LSTM uses identical features and evaluation. Findings – LightGBM has the lowest full-test RMSE (364.17 kW), but CatBoost (385.88 kW) has significantly lower median error (p<0.0001); metrics disagree on the best model, and LightGBM shows a markedly larger train-test gap, consistent with overfitting. CatBoost and GBM outperform LSTM on normal-operation data (p<0.05), while AdaBoost and LightGBM do not. Permutation importance shows wind speed drives over 87% of predictive signal despite differing built-in measures. CatBoost beats LSTM by 18.3% NRMSE on normal-operation data, narrowing to 2.3-9.6% on full data. A seven-seed check confirms these full-test and normal-operation advantages, though intervals overlap. Research implications/limitations – Data cover 2018 at one Turkish site, limiting generalizability; a random split misses temporal shifts, and 50 trials may not fully explore hyperparameter space. Originality/value – This leakage-safe SCADA pipeline shows gradient boosting modestly but significantly outperforms LSTM, with gains depending on abnormal-condition inclusion. Future work should apply temporal cross-validation, test more sites, and tune LSTM more rigorously.
A Sixteen-Year Bibliometric Analysis of Digital Surveillance and Academic Social Networking in Data-Driven Information Ecosystems Helmy Prasetyo Yuwinanto; Koko Srimulyo; Irfan Wahyudi; Arya Wijaya Pramodha Wardhana
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.v5i3.13905

Abstract

Purpose – This research compares publication trends, citation patterns, scientific productivity, and thematic structures in digital surveillance and academic social networking research, while examining how structurally similar data-driven infrastructures generate largely involuntary surveillance but strategically pursued academic visibility. Design/method/approach – Bibliographic data covering sixteen publication years (2010–2025) were retrieved from Scopus through searches of titles, abstracts, and keywords, yielding 2,595 digital surveillance documents and 4,975 academic social networking documents. Scopus Analyze Results, Bibliometrix/Biblioshiny, and VOSviewer were used to assess scientific performance, map thematic structures, and identify keyword co-occurrence patterns. Findings – Digital surveillance publications increased from 46 documents in 2010 to 424 documents in 2025, while academic social networking publications increased from 16 to 995 documents. The United States leads in digital surveillance research with around 770 documents, while India leads in academic social networking research with 729 documents. The theme of digital surveillance centers on datafication, privacy, COVID-19, public health, and artificial intelligence. Meanwhile, academic social networking focuses on ResearchGate, Mendeley, social media, altmetrics, publications, and citations. Implications/limitations of the research – Findings are limited by reliance on a single database, inclusion of English-language documents only, the specific search terms selected, and differing citation windows across publication years, which affect citation-based comparisons. Originality/value – This research maps two previously separate fields within a single comparative framework and interprets visibility and surveillance as a continuum within data-driven information ecosystems, rather than as opposing phenomena.
The Effect of AI-Assisted Marketing on MSME Product Promotion Effectiveness: The Role of Customer Engagement among MSMEs in Dalu Sepuluh B Village Sri Wahyuni; Zulham; Buyung Solihin Hasugian
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.v5i3.14548

Abstract

Purpose –Examine the effect of AI-Assisted Marketing on Promotional Effectiveness and analyze Customer Engagement as a mediating variable among MSMEs in Dalu Sepuluh B Village. The study addresses the limited use of AI to improve promotional effectiveness and customer engagement, particularly among rural MSMEs. Design/methods/approach –Employs a quantitative explanatory cross-sectional design to examine the effect of AI-Assisted Marketing on Promotional Effectiveness. The study involves 100 MSME respondents selected through purposive sampling. Data are collected using a questionnaire consisting of 12 indicators measured on a five-point Likert scale. Data analysis is conducted using PLS-SEM through SmartPLS, including assessment of outer loadings, Cronbach's Alpha, Composite Reliability, AVE, HTMT, path coefficients, t-statistics, p-values, R², and f². Findings – H1 supported: AI-Assisted Marketing has a positive and significant effect on β = 0.509; p < 0.001, H2 supported: β = 0.394; p < 0.001, H3 supported: AI-Assisted Marketing has a direct and significant effect on Promotional Effectiveness (β = 0.281; p = 0.004), H4 supported: Customer Engagement significantly mediates indirect effect = 0.201, The R² for Customer Engagement = 0.259, The R² for Promotional Effectiveness = 0.347. Research implications/limitations –that AI can improve promotional effectiveness and customer engagement. However, generalizability is limited to the context of MSMEs in Dalu Sepuluh B Village. The cross-sectional design and self-reported data also limit causal interpretation. Originality/value – the mechanism linking AI-Assisted Marketing, Customer Engagement, and Promotional Effectiveness, rather than focusing solely on AI adoption. It contributes to the literature on AI-enabled marketing and MSME digital transformation.

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