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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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Scenario-Based Charging Demand and Load Characterization for Electric Two-Wheeler Systems Meidi W. Lestari; Fitria N. Hulu; Rina Anugrahwaty; Muhammad S. H. Daulay; Mutiara W. Sitopu; Aprima A Matondang
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.v5i2.13017

Abstract

Purpose - This study aims to develop a spatio-temporal load characterization framework for electrified two-wheeler mobility and evaluate the potential implications of charging demand on urban distribution networks under varying operational scenarios. Design/methodology/approach - The framework integrates mobility behavior, energy consumption, and charging demand using key parameters, including travel distance (40–80 km/day), energy consumption rate (0.04–0.06 kWh/km), and fleet size (100–1000 units). Findings - Results show that individual energy demand ranges from 1.6 to 4.8 kWh/day per vehicle, while aggregated demand increases proportionally, reaching approximately 0.16 MWh/day, 0.80 MWh/day, and 1.60 MWh/day for fleets of 100, 500, and 1000 units, respectively. Charging demand is highly concentrated during evening periods, creating synchronized load peaks and localized stress on distribution infrastructure. Research implications/limitations - The study is limited to simulated mobility and charging scenarios; however, it provides a practical framework for assessing the impacts of electric two-wheeler adoption on urban power distribution systems. Originality/value - This research offers an integrated spatio-temporal approach combining mobility patterns, energy consumption, and charging behavior within a unified framework, providing valuable insights for coordinated charging strategies, load balancing, and infrastructure planning.
Analytical Framework for Evaluating AI Detection Performance Across Writing Modalities in Technical English Writing Agustina Ginting; Harris Aminuddin; Ulfa Hasnita
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.v5i2.13018

Abstract

Purpose – This study proposes a conceptual framework for AI-based detection of handwritten and technical English writing in engineering education by investigating how different writing modalities influence AI detection performance. Design/methodology/approach – The proposed framework integrates four representative writing modalities, namely handwritten, typed, hybrid, and transcribed texts. A framework-based analytical evaluation was conducted using representative detection scores together with accuracy, precision, recall, F1-score, confidence intervals, and heatmap visualization to compare AI detection performance across writing modalities. Findings – The analytical evaluation suggests that AI detection performance varies across writing modalities, with representative accuracy ranging from 0.72 to 0.89. Detection scores progressively increase from handwritten (0.41–0.50) to typed (0.52–0.58), hybrid (0.60–0.66), and transcribed texts (0.68–0.76). Transcribed texts demonstrate the highest performance, with precision and recall reaching 0.87–0.89, whereas handwritten texts exhibit lower recall (0.44) and broader confidence intervals, indicating greater classification uncertainty. Hybrid writing shows intermediate performance due to overlapping human and AI-assisted linguistic characteristics. Research implications/limitations – This study is limited to a framework-based analytical evaluation using representative writing scenarios rather than empirical observations. Nevertheless, the proposed framework provides practical guidance for developing context-aware AI detection systems that improve fairness and reliability in evaluating technical and English as a Second Language (ESL) writing. Originality/value – This research presents an integrated analytical framework that combines multiple writing modalities with AI detection performance evaluation to investigate modality-dependent differences in technical English writing. The proposed framework contributes to the development of more robust, context-aware, and equitable AI-assisted writing assessment for engineering education.
Waste-to-Energy Modeling Framework for Sustainable Municipal Solid Waste Energy Recovery Using a Fixed-Bed Incinerator in Medan City M. Syahruddin; A. Abdullah; C. Cholish; Panangian M. Sihombing; Michael S. Sinurat; Theresia Anggriani
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.v5i2.13019

Abstract

Purpose – This study evaluates the potential of municipal solid waste (MSW) as a renewable energy source through the implementation of a small-scale waste-to-energy (WtE) system in Medan City, Indonesia. Design/methodology/approach – Waste composition data from four representative districts were analyzed to determine waste characteristics, calorific value, and energy recovery. The assessment was conducted using a fixed-bed incinerator with air supply configuration and energy calculations. Findings –Results indicate that the average calorific value of municipal waste is approximately 2975 kcal/kg, meeting the minimum requirement for thermochemical conversion processes. Energy balance analysis shows that approximately 292.07 kg of dry waste can generate around 25.8 kWe of electrical power during 12 hours of operation. Research implications/limitations – The analysis is based on average municipal waste composition data and assumed energy conversion efficiencies for the fixed-bed incineration system. Actual performance may vary depending on waste characteristics, moisture content, operational conditions, and technology configuration. Future studies should incorporate pilot-scale validation, economic feasibility, and environmental impact to improve the accuracy and applicability of the proposed framework. Originality/value – This study provides a localized waste-to-energy assessment framework by integrating municipal waste characterization, calorific value estimation, thermal energy recovery, and electricity generation analysis for Medan City. The framework addresses the limited availability of city-specific evaluations for small-scale waste-to-energy implementation in developing urban areas.
A Rainfall-Only Hybrid LSTM-Random Forest Framework for Data-Driven Urban Flood-Risk Decision Support in Jakarta under Severe Class Imbalance Dimas Alfa Rizky; Khothibul Umam; Maya Rini Handayani
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.v5i3.13107

Abstract

Purpose – Urban flood-risk screening in Jakarta requires prediction approaches that remain useful when complete hydrological and infrastructural flood data are not consistently available. This study evaluates a rainfall-based decision-support framework for next-day flood-risk detection using Long Short-Term Memory (LSTM), Random Forest (RF), and a hybrid LSTM-RF model. Design/methods/approach – Rainfall predictors were reconstructed from ERA5-Land reanalysis and transformed into lagged, rolling-window, and seasonal features. Official flood-occurrence labels were obtained from BNPB’s DIBI records. The supervised dataset comprised 4,134 daily observations (132 flood and 4,002 non-flood events) and was chronologically split into training, validation, and test sets. Findings – At the fixed 0.50 threshold, the LSTM model achieved the highest ROC-AUC of 0.735516 and recall of 0.740741, although its precision remained low at 0.054945 because of many false positives. RF and Hybrid LSTM-RF achieved high accuracy of 0.967391 but failed to detect positive flood cases in the hold-out test set. Research implications/limitations – These findings show that accuracy is insufficient for evaluating rare-event flood prediction and that rainfall-only models require careful threshold calibration, official-label expansion, and additional hydrological predictors before being interpreted as operational flood-warning tools Originality/value – Flood-occurrence labels were constructed from the official tabular Data Informasi Bencana Indonesia (DIBI) records managed by BNPB, so the supervised target represented officially recorded flood events rather than rainfall-threshold proxy labels. The study also evaluates and compares LSTM, RF, and Hybrid LSTM-RF models using rainfall-only predictors for next-day flood-risk detection under severe class imbalance.
The Development of Learning Media in Geography Education: A Bibliometric Analysis of Global Research Trends 2015–2025 Nadya Putri Fitriani; Sugiyanto; Seno Budhi Ajar
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.v5i3.13175

Abstract

Purpose This study aims to map global research trends, intellectual structures, and thematic developments in learning media development within geography education and closely related geospatial, geoscience, and environmental education contexts from 2015 to 2025. Design/methods/approach – A bibliometric approach was employed using data retrieved from the Scopus database. Following a PRISMA-based screening process, 705 eligible articles were selected from 2,752 initial records. Data were analyzed using VOSviewer 1.6.20 and Microsoft Excel through publication trend analysis, bibliographic coupling, and keyword co-occurrence mapping. Findings – The results indicate a substantial increase in publications after 2020, reflecting the accelerated digital transformation of education. Indonesia emerged as the leading contributor under the full-counting approach. Bibliographic coupling analysis revealed a transition from digital and contextual learning media toward AR/VR-based environments and AI-supported educational innovations. Keyword co-occurrence analysis identified dominant themes such as mobile learning, augmented reality, virtual reality, and sustainability, while artificial intelligence, digital literacy, and critical thinking emerged as weak-signal research themes identified through complementary descriptive keyword analysis. Research implications/limitations – The study is limited to English-language open-access journal articles indexed in the Scopus database and therefore may not fully represent the broader literature. Originality/value – This study provides a comprehensive bibliometric mapping of learning media development across geography education and related geospatial, geoscience, and environmental education domains, revealing its intellectual evolution, thematic structure, and emerging research frontiers.
Case-Based Root Cause Investigation of Integrated Charging Control Unit Failure Using Diagnostic and Physical Evidence Yusep Sukrawan; Rastia; Ridwan Adam Muhamad Noor; Akhmad Saufan; Gilang Ciptadi Mahatkarsa; Apri wiyono; Ibnu Mubarak
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.v5i3.13179

Abstract

Purpose - This study aims to investigate the probable root cause of Integrated Charging Control Unit (ICCU) failure in an electric vehicle through the integration of service-level diagnostic information, electrical measurements, and physical failure evidence. Research Design - A descriptive-analytical single-case study was conducted on one failed ICCU unit obtained from a Hyundai Ioniq electric vehicle. The investigation included the evaluation of Diagnostic Trouble Codes (DTCs), freeze-frame parameters, insulation resistance measurements, fuse continuity testing, and physical inspection of damaged components. Findings - Diagnostic analysis identified an active DTC, P1A9096 (DC/DC Converter Input Voltage Sensor Fault), together with several historical low-voltage and communication-related DTCs. Freeze-frame data showed that the OBC internal DC-link voltage, charging voltages, and charging currents were recorded at 0.0 V and 0.0 A, indicating that the charging conversion process was unable to establish normal energy transfer. Physical inspection revealed localized burn marks, degraded thermal interface materials, printed circuit board contamination, and residue accumulation around critical electrical components. Electrical testing further identified an insulation resistance of 1.47 MΩ and an open-circuit condition of the high-voltage fuse. The integrated evidence indicates that the failure most likely originated within the ICCU power conversion stage, whereas the observed thermal degradation, insulation deterioration, and environmental contamination are interpreted as contributory factors or evidence-supported hypotheses rather than conclusively verified initiating mechanisms. The open-circuit high-voltage fuse is interpreted as the final protective response to the abnormal electrical event. Implications - The findings are derived from a single failed ICCU unit and therefore should be interpreted as an evidence-based case study rather than confirmation of recurring failure patterns or generalized ICCU reliability characteristics. Several proposed failure mechanisms remain evidence-supported hypotheses because advanced measurements, including thermal imaging, semiconductor parameter testing, capacitance measurements, coolant characterization, and microscopic material analysis, were not performed. Originality - This study demonstrates the value of integrating diagnostic information, electrical measurements, and physical evidence to support evidence-based investigation of ICCU failures. The proposed case-based diagnostic approach provides practical insights for understanding failure mechanisms in electric vehicle charging systems and establishes a foundation for future multi-case investigations.
XGBoost Hyperparameter Optimization Using Optuna TPE for Multiclass Classification of Intrusion Detection in Internet of Things Networks Maulana Akmal Ibrahim; Subhan
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.v5i3.13281

Abstract

Purpose – The majority of research on intrusion detection systems (IDS) still uses outdated datasets that don't accurately reflect contemporary IoT threats, ignoring issues with class imbalance and effective hyperparameter tuning. Using the CICIoT2023 dataset, this work builds a machine learning-based intrusion detection pipeline that can classify 27 IoT attack classes in a multiclass environment. Design/Methods/Approach – The pipeline includes data preprocessing, Random Forest Feature Importance to select the top 20 features from 40 numerical features, MinMaxScaler normalization, SMOTE to handle class imbalance, and Optuna Tree-structured Parzen Estimator (TPE) to optimize XGBoost hyperparameters over 50 trials with three-fold stratified cross-validation. Findings – On 9,992 test samples, XGBoost optimized with Optuna outperformed default XGBoost (F1: 98.97%), Random Forest (F1: 98.98%), and KNN (F1: 94.81%), achieving 99.07% accuracy and F1-Score. With a Best Cross-Validation F1 of 99.88%, Optuna determined the ideal configuration. SMOTE was shown to have the most F1 contribution (0.27%) in the ablation study. Research Implications/Limitations – This study is constrained to a 50,000-sample subset from the full 46 million CICIoT2023 records and has not been validated on other IoT datasets. The pipeline was evaluated solely in a modeling environment and has not been deployed on physical IoT devices, meaning performance under actual hardware constraints remains to be verified. Originality/Value – This study proposes an integrated pipeline with a leakage-free final test set combining XGBoost, Optuna TPE, Random Forest Importance-based feature selection, and SMOTE on CICIoT2023, contributing a potentially more computationally efficient IDS alternative compared to deep learning architectures with multiclass classification across 27 contemporary IoT attack types.
Enhancing Teaching and Learning through Digital Storytelling: Exploring Elementary Teachers' Perspectives and Challenges in Hiroshima Prefecture Setyaningsih Rachmania; Bachrudin Musthafa; Sri Setyarini; Iyen Nurlaelawati; Takamichi Asakura; Nissa Aulia Belistiana Utami
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.v5i3.13303

Abstract

Purpose – This study addresses what we term the "Pedagogical Infrastructure Gap" is a condition in which educational institutions have successfully deployed digital hardware and devices through government-led initiatives while neglecting to equip teachers with the pedagogical vocabulary required to theorize their own instructional practices. Method – Employing a descriptive qualitative approach, data were collected from four in-service teachers currently pursuing postgraduate studies at Hiroshima University through semi-structured interviews and document analysis. Findings – Some of the participants actively employ multimodal and narrative-based pedagogical practices that implicit DST-related to identify or articulate their work within that conceptual framework. Implications – This study emphases the untapped pedagogical potential embedded within these implicit practices and recommends professionally-oriented interventions focused on "naming the practice" that is, on bridging the gap between what teachers already do and what they can consciously leverage through theoretical awareness. Originality – Unlike the majority of existing studies, which examine outcomes in contexts where teachers are deliberately trained in Digital Storytelling (DST), this research is uniquely positioned to document how educators accidentally arrive at Digital Storytelling (DST) through independent experimentation with multimodal tools such as iMovie and PowerPoint, thereby contributing an underexplored perspective to the global literature on ICT integration in elementary education.
Clustering Elementary School Students’ Academic Achievement Profiles Using K-Means, the Elbow Method, and Silhouette Evaluation Irmayanti; Hamni Fadlilah Nasution; Rohani
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.v5i3.13453

Abstract

Purpose – This study examined relative academic-score profiles among sixth-grade students at SD Negeri 05 Bilah Barat using Educational Data Mining and K-Means clustering. It compared candidate cluster solutions using the Elbow Method, Silhouette evaluation, repeated-run stability, and educational interpretability. Design/methodology/approach – First-semester report-card scores from 50 students across Civic Education, Indonesian Language, Mathematics, Natural Sciences, and Social Sciences were Min–Max normalized and processed through one Python-based pipeline. K-Means used k-means++ initialization, 100 initializations, a fixed random seed of 42, a maximum of 300 iterations, Euclidean distance, and the Lloyd algorithm. Candidate solutions were evaluated using WCSS, overall and cluster-specific Silhouette Scores, the Adjusted Rand Index, and PCA visualization. Findings – K = 4 produced the highest overall Silhouette Score (0.4606) and the highest mean repeated-run ARI (0.9619), compared with K = 3 and K = 5. The selected solution comprised higher-score (n = 16), intermediate balanced (n = 24), mixed subject (n = 7), and lower-score profiles (n = 3). The first two principal components explained 87.87% of the variance. Research implications/limitations – The profiles are preliminary decision-support information and should not be used as formal classifications or as the sole basis for consequential educational decisions. Originality/value – The study provides a transparent and reproducible comparison of candidate solutions, including stability testing, inverse-transformed centroids, and cautious educational interpretation.
Representation over Kernel: A Cross-Category Analysis of Semantic Review Embedding versus Rating-Overlap Collaborative Filtering for User Cold-Start Moh Rahmat Irjii Matdoan; Triyanna Widiyaningtyas; Firmansyah Ibrahim; Rasna
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.v5i3.13484

Abstract

Purpose – The user cold-start problem cripples recommenders when a new user has almost no interaction history. This study asks two questions prior work conflates: does semantic review embedding similarity outperform conventional rating-overlap collaborative filtering (CF) in cold-start, and does a magnitude-sensitive kernel over the embedding add anything beyond ordinary cosine similarity? Design/methods/approach – Under one identical protocol on two Amazon review domains of differing sparsity, RBF-SBERT (an RBF kernel over SBERT embeddings) is compared with cosine, Pearson, an XGBoost baseline, rating-overlap CF, and two popularity baselines. Cold-start users (one to three interactions) use a per-user sampled protocol; significance uses Wilcoxon and Cohen's d. Findings – In the sparse domain the embedding beats even the strongest simple baseline, item-count popularity, on precision (P@5 0.567 vs 0.252; d=0.65) and ranking (AUC 0.869 vs 0.821); in the denser domain that edge vanishes as popularity matches AUC and exceeds precision, so the advantage is governed by sparsity. Rating-overlap CF is weakest, collapsing to its mean-rating fallback. The RBF kernel is inert (≈ cosine, d=0.007–0.107) because the pooled magnitude it uses is nearly constant (CV 0.07–0.09) and tracks review length, not preference. Research implications/limitations – Cold-start gains arise from the representation, not the kernel; methods must be benchmarked against popularity under sparsity-aware protocols. Evaluation covers two Amazon categories under one sampled protocol. Originality/value – The study separates representation from kernel and precision from ranking quality, giving a reproducible account of when review-based embedding helps cold-start and why.

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