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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 277 Documents
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Energy Efficiency Behavior in Mechanical Engineering Operations: The Roles of Machine Performance Literacy, Operational Discipline, and Maintenance Culture Aris Tri Ika Rakhmadi; Pompy Pratisna; Ayip Rivai Prabowo
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.v5i2.274

Abstract

Purpose –Improving energy efficiency in industrial operations requires not only technological advancement but also supportive employee behavior. This study investigates how machine performance literacy contributes to energy efficiency behavior, both directly and through operational discipline, while examining whether maintenance culture strengthens this relationship in mechanical engineering operations. Design/methods/approach – A quantitative survey was conducted with 200 respondents, including machine operators, maintenance technicians, engineers, and production supervisors from several industrial sectors. The proposed research model was analyzed using Structural Equation Modeling–Partial Least Squares (SEM-PLS) to evaluate direct, indirect, and moderating effects among the study variables. Findings – The results indicate that machine performance literacy significantly enhances operational discipline and energy efficiency behavior. Operational discipline was also found to positively influence energy efficiency behavior and partially mediate the effect of machine performance literacy. In contrast, maintenance culture did not significantly strengthen the relationship between operational discipline and energy efficiency behavior. The structural model accounted for 55.4% of the variance in energy efficiency behavior, demonstrating satisfactory explanatory power. Research implications/limitations – Because the study employed a cross-sectional design, the findings should be interpreted with caution regarding causal relationships and may not fully represent all industrial settings. Originality/value – This study offers a behavioral perspective on industrial energy efficiency by integrating machine performance literacy and operational discipline within a single analytical framework. The findings broaden existing knowledge by highlighting human-related factors that complement technology-based approaches to energy efficiency improvement.
The Association Between Students’ Digital Literacy and Adaptation to EdLink-Based Learning: A Single-University Correlational Study Abdillah SAS; Supriadi Syam; Sahabuddin Rifai; Muh Fadli Fauzi Sahlan
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.v5i2.275

Abstract

Purpose - This study aims to examine the association between students’ digital literacy and their adaptation to EdLink-based learning in higher education. Design/methods/approach - A quantitative cross-sectional correlational design was employed. Data were collected from 290 undergraduate students at Universitas Bosowa who had used EdLink for learning activities. The instrument consisted of two self-reported scales measuring digital literacy and adaptation to EdLink-based learning. Data were analyzed using descriptive statistics, validity and reliability testing, assumption testing, Pearson correlation, and simple linear regression. Findings - The results showed a positive and statistically significant association between digital literacy and students’ adaptation to EdLink-based learning (r = 0.892; R² = 0.795; p < 0.001). Students with higher digital literacy tended to report higher adaptation to EdLink-based learning. However, this finding should be interpreted as correlational rather than causal evidence because the study did not test mediation, moderation, or institutional factors. The very strong association may also reflect self-reported measurement, conceptual proximity between the constructs, and possible common method variance. Research limitations/implications - This study was limited to one university and relied on self-reported questionnaire data. Other factors, such as learning motivation, lecturer support, platform usability, digital infrastructure, and prior LMS experience, were not examined. Originality/value - This study provides contextual evidence from an Indonesian university on the association between students’ digital literacy and adaptation to EdLink-based learning.
Browser-Side Security Vulnerabilities in Healthcare Institutions Using Dynamic Application Security Testing (DAST): A Case Study of RS Mata Makassar Supriadi Syam; Abdillah SAS; Sahabuddin; Muh. Fadli Fauzi Sahlan
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.v5i1.269

Abstract

Purpose – Digital transformation has made healthcare websites critical for patient services, yet regional providers in developing economies often face a "security-functionality" paradox. This study conducts an automated vulnerability assessment of the RS Mata Makassar website to profile browser-side security and discusses how observed misconfigurations could hypothetically affect clinical operations if exploited. Design/methodology/approach – The research employs a black-box Dynamic Application Security Testing (DAST) approach using the open-source Wapiti scanner. The methodology involves crawling public endpoints and performing non-intrusive fuzzing to evaluate declarative security controls, specifically Content Security Policy (CSP), HTTP Strict Transport Security (HSTS), and secure cookie attributes. Findings – While no critical injection flaws (SQLi/XSS) were detected, the assessment revealed a complete absence of basic security controls. Compliance scores reached 0/5 for CSP, 0/5 for HSTS, and 0/3 for secure cookie attributes. These results fall significantly below global healthcare benchmarks, exposing high vulnerability to session hijacking and protocol downgrades. Originality/value – This study audits browser-side security misconfigurations, specifically CSP, HSTS, and cookie attributes using a black-box DAST approach with Wapiti on a regional healthcare website. This study provides a low-cost technical audit approach for identifying browser-side security misconfigurations in a regional healthcare website.
Automated Abstractive Summarization and Entity Extraction of Online News Information Using mT5 and BERT Marwan Aldi Pratama; Siti Nur'aini; Maya Rini Handayani; Khothibul Umam
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.v5i2.276

Abstract

Purpose – This research aims to address the phenomenon of information overload on online news portals by developing an automated text summarization system capable of generating abstractive summaries while preserving essential entities. In addition, this research also aims to improve the coherence and quality of summaries compared to conventional extractive methods. Methods/approach – This research employs a quantitative approach with an experimental method conducted on 100 news articles regarding the Israel–Iran conflict collected from CNN via RSS. The proposed system integrates the mT5 model for abstractive summarization and the multilingual BERT model for Named Entity Recognition (NER). The stages encompass data acquisition, preprocessing, the preparation of reference summaries, automated summarization, entity extraction, and evaluation using reduction rates and ROUGE metrics. Findings – The research results show that the system is capable of producing summaries with an average reduction rate of 89.83%, such that the summary length is only approximately 10.17% of the original text. Evaluation indicates a ROUGE-1 value of 0.4095, ROUGE-2 of 0.2356, and ROUGE-L of 0.3442. The mT5 pipeline model yielded marginally superior ROUGE-1 and ROUGE-L scores, whereas the baseline mT5 model demonstrated a slight advantage in the ROUGE-2 metric. Conversely, the extractive TextRank method lagged significantly behind both transformer based models, particularly in generating fluent and contextually coherent summaries. Research limitations – This research has limitations in terms of data coverage, which still focuses on a single conflict domain, as well as entity classification errors due to lexical ambiguity and limitations in the model's contextual understanding, which may affect the generalization and accuracy of the system. Originality – This research offers an integration between abstractive summarization and entity extraction within a structured pipeline, there by producing summaries that are not only concise but also more informative and organized.
Design and Validation of a PLC-Based Smart Solar Dome Dryer as a Contextual Learning Platform for Enhancing Industrial Competencies in Informatics Education Baso Ali; Muhammad ikram; Shoira Abdiyeva
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.v5i2.271

Abstract

Purpose - Industry 4.0 increases competency demands for informatics graduates. Graduates need software skills and industrial automation literacy. This study designed and validated a Programmable Logic Controller (PLC)-based smart solar dome dryer as a contextual learning platform for developing industrial competencies in informatics education. Design/methods/approach – This study designed and validated a PLC-based smart solar dome dryer as a contextual learning platform to enhance students’ industrial competencies. The prototype was assessed through functional testing, sensor accuracy and stability testing, drying performance evaluation, and expert feasibility review as a learning medium. Learning effectiveness was examined using a quasi-experimental pretest–posttest design with 120 informatics students in experimental and control groups. Instrument validity and reliability were confirmed using Aiken’s V and Cronbach’s alpha. Findings – The system operated reliably as a closed drying platform and produced more stable temperature and humidity conditions with more efficient drying time than conventional methods. Students in the experimental group achieved significantly higher competency gains than those in the control group (p < 0.001), with a very large effect size (Cohen’s d = 3.21; Hedges’ g = 3.18).   Research implications/limitations – The findings suggest that authentic PLC-based automation systems can support contextual industrial learning in Education 4.0. However, the quasi-experimental design and single institutional context limit causal and generalization claims.   Originality/value – This study contributes a validated agro-industrial automation prototype and empirical evidence on its potential value as a contextual learning platform in informatics education.
Hybrid Deception–Detection Approach Using Dionaea Honeypot and Snort IDS for Wireless Network Security Alvin Kamil; Muhlis Tahir
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.v5i2.277

Abstract

Purpose – This study implements a hybrid deception–detection approach by integrating Snort IDS and the Dionaea honeypot, supported by the ELK Stack for centralized monitoring and visualization within a wireless school network environment. The proposed approach provides a practical and low-cost security monitoring solution for educational institutions with limited cybersecurity resources.Design/methods/approach – The research method involved literature review, system design, implementation, and testing using simulated port scanning, brute force, and Denial of Service (DoS) attack scenarios. Snort IDS was configured to detect suspicious network traffic, while Dionaea operated as a decoy service to record attacker interactions. Generated alerts and interaction logs were centralized and visualized through the ELK Stack.Findings – The implementation results show that the proposed system generated alerts and interaction logs for all simulated attack scenarios within the controlled experimental environment. Snort IDS generated 2,928 port scanning alerts, 426 brute force alerts, and 3,428 DoS alerts, while Dionaea recorded 493 FTP interaction logs. The ELK Stack centralized and visualized 7,275 generated log records in near real-time. Baseline monitoring under normal traffic conditions did not produce false positive alerts. The reported values represent generated monitoring events rather than formal detection-performance metrics.Research implications/limitations – This study was conducted in a controlled school-scale wireless network environment using limited attack scenarios and short-term monitoring observations. Therefore, the findings may not directly represent large-scale production network conditions.Originality/value – This study demonstrates the feasibility of integrating traffic-based intrusion detection, deception-based interaction logging, and centralized monitoring within a unified wireless school network security architecture using open-source technologies.
Enhanced Wind Turbine Power Forecasting via Hyperparameter-Optimized XGBoost Dimas Ramadhani; Yahya Nur Ifriza
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.v5i2.278

Abstract

Purpose – This study aims to evaluate the accuracy and computational efficiency of XGBoost in forecasting wind turbine output using a dataset aligned at hourly timestamps. This topic is important because wind turbine output exhibits fluctuating and non-linear patterns, requiring a model capable of capturing the relationship between meteorological conditions, historical turbine patterns, and the generated Energy values. Design – This study uses the 2016 Sotavento Galicia data, which consist of hourly Numerical Weather Prediction (NWP) data and historical turbine operational data originally recorded every 10 minutes. Temporal alignment was performed by retaining only turbine operational records located exactly at hourly timestamps and then merging them with the NWP data at the same timestamps. The final dataset was modeled as an hourly aligned time series dataset. The Energy variable was used as the prediction target. Since the dataset does not explicitly state the unit of Energy, RMSE and MAE were reported in the original scale of the Energy variable and cautiously interpreted as kWh per retained 10-minute record based on the variable label, recording resolution, and value range. Three model scenarios were compared, namely the XGBoost baseline, XGBoost with GridSearchCV, and XGBoost with RandomizedSearchCV. Internal validation was performed using TimeSeriesSplit, while final testing was conducted using monthly holdout on months 10, 11, and 12. Findings – The results show that XGBoost with RandomizedSearchCV produced the lowest average prediction error, with an RMSE of 135.591, MAE of 87.710, and R² of 0.907. This model reduced RMSE by 5.86% compared to the XGBoost baseline and reduced computation time by 69.51% compared to GridSearchCV. Research implications – These findings are limited to a single wind farm dataset, one observation period, and a constrained hyperparameter search space. Originality – This study demonstrates that RandomizedSearchCV can serve as an efficient tuning strategy for XGBoost-based wind power forecasting.

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