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Jurnal Pendidikan Teknologi Informasi dan Komunikasi
Published by CV Media Inti Teknologi
ISSN : -     EISSN : 29631963     DOI : https://doi.org/10.58723/jentik.v2i1.137
Core Subject : Science, Education,
Computer Science & IT Education Other
Jurnal Pendidikan Teknologi Informasi dan Komunikasi (JENTIK) with ISSN 2963-1963. is a journal managed by CV Media Inti Teknologi. Publish articles on community service activities in the fields of education, design, development, assessment, model, Technology, e-learning, Blended Learning, MOOCs, Multimedia, Digital Learning Etc.
Arjuna Subject : Ilmu Sosial - Pendidikan
Articles 37 Documents
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Wrapper Feature Selection Method for Predicting Student Dropout in Higher Education Singh, Anuradha; Karthikeyan, S.
JENTIK : Jurnal Pendidikan Teknologi Informasi dan Komunikasi Vol. 4 No. 1 (2025): Jurnal Pendidikan Teknologi Informasi dan Komunikasi
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/jentik.v4i1.441

Abstract

Background of Study: Student dropout in higher education is influenced by a variety of factors including demographic, socioeconomic, macroeconomic, admission-related, and academic performance data. Accurately identifying students at risk of dropping out is a significant challenge within educational data mining (EDM), especially when working with large, complex datasets.Aims and Scope of Paper: This study aims to identify an optimal subset of features that can improve the accuracy of student dropout prediction. The scope includes comparing the effectiveness of different machine learning algorithms combined with a heuristic-based feature selection method to find the best-performing model.Methods: A Wrapper-based feature selection approach was employed using Ant Colony Optimization (ACO) as the search strategy. ACO was integrated with five classifiers—Random Forest (RF), Logistic Regression (LR), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Neural Network (NN)—to select the most relevant feature subsets. The performance of each combination was evaluated and compared.Result: The study found that ACO combined with Random Forest (ACO-RF) outperformed the other combinations in feature selection effectiveness. The selected features were then validated using various machine learning algorithms and a neural network. Among them, the neural network achieved the highest accuracy of 93%.Conclusion: The proposed ACO-RF wrapper method is an effective feature selection strategy for predicting student dropout in higher education. The method enhances model performance, especially when used with neural networks, and offers a promising approach for early identification of at-risk students.
School Feasibility Analysis and Grade Improvement Strategies Using the Random Forest Algorithm Aliyya, Farrel Rahma; Farizi, Syahandhika Naufal; Riza, Lala Septem; Megasari, Rani; Nugraha, Eki; Wahyudin, Asep
JENTIK : Jurnal Pendidikan Teknologi Informasi dan Komunikasi Vol. 4 No. 2 (2025): Jurnal Pendidikan Teknologi Informasi dan Komunikasi
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/jentik.v4i2.475

Abstract

Background of Study: Educational disparities across Indonesian provinces persist, particularly in infrastructure, teacher quality, and dropout rates, necessitating data-driven analysis for equitable improvements.Aims: This study investigates school feasibility and proposes strategies to enhance provincial education performance using the Random Forest algorithm.Methods: Aggregated provincial education data covering student numbers, dropout rates, teacher qualifications, and classroom conditions were transformed into derivative indicators. A binary classification (Feasible/Not Feasible) based on national dropout median was applied. The model was developed using R with six systematic steps, including training and evaluation of a Random Forest model (ntree = 100, mtry = 3) using accuracy, sensitivity, and specificity.Result: The model accurately classified school feasibility. Key predictors included teacher quality, student-teacher ratios, and classroom conditions. Several provinces were identified as “Not Feasible.”Conclusion: Machine learning proves effective for education policy support. The study offers targeted recommendations such as improving infrastructure, enhancing teacher training, and reducing dropouts to promote equitable education in Indonesia.
Integrating Digital Cognitive Mapping into Speaking Instruction: A Quasi-Experimental Study with Indonesian EFL Students Hamdan, Hamdan; Triyogo, Agus; Oktaviani, Ayu
JENTIK : Jurnal Pendidikan Teknologi Informasi dan Komunikasi Vol. 4 No. 2 (2025): Jurnal Pendidikan Teknologi Informasi dan Komunikasi
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/jentik.v4i2.565

Abstract

Background of study: Speaking is a fundamental component of communication skills for English as a Foreign Language (EFL) students. Nevertheless, many learners encounter difficulties in expressing ideas fluently and coherently in oral communication. These challenges highlight the need for instructional strategies that facilitate idea generation, organization, and delivery through technology-enhanced learning environments.Aims: This study aims to examine the effect of integrating digital cognitive mapping into speaking instruction on the speaking performance of Indonesian EFL university students.Methods: A quasi-experimental pretest–posttest control group design was employed involving 44 students enrolled in an English Language Education program. The participants were divided into an experimental group (n = 22) and a control group (n = 22). The experimental group received speaking instruction supported by digital cognitive mapping tools, while the control group engaged in conventional speaking activities without cognitive or digital mapping. Students’ speaking performance was assessed using standardized speaking rubrics, and the data were analyzed using descriptive and inferential statistics.Result: The results indicated that the experimental group showed a statistically significant improvement in speaking scores from pretest to posttest, whereas the control group demonstrated only a slight and non-significant gain. In addition, the experimental group significantly outperformed the control group on the posttest.Conclusion: The study concludes that digital cognitive mapping is an effective instructional tool for enhancing EFL students’ speaking performance, particularly in planning, organizing, and delivering oral texts. These findings support the integration of technology-enhanced, learner-centered speaking activities using visual knowledge-organization tools in higher education contexts.
Implications of Gamification for Student Learning Motivation: Meta-Analysis Study Emilzoli, Mario; Hernawan, Asep Herry; R. Nadia, Hanoum; Rullyana, Gema; Saidah, Zahrani Putri; Susanti, Lia; Gifari, Muhamad Kosim
JENTIK : Jurnal Pendidikan Teknologi Informasi dan Komunikasi Vol. 4 No. 2 (2025): Jurnal Pendidikan Teknologi Informasi dan Komunikasi
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/jentik.v4i2.579

Abstract

Background of the study: Gamification is increasingly applied in education to stimulate engagement and motivation, yet empirical findings on its effectiveness remain mixed.Aims: This study aims to determine the overall effect of gamification on students’ learning motivation and to examine whether education level, sample size, and publication year moderate this effect.Methods: A meta-analysis was conducted using Scopus-indexed journal articles published between 2015–2024. Studies were included if they reported motivation outcomes with sample size, mean, and standard deviation. Effect sizes (Hedges’ g) were calculated and synthesized using a random-effects model with OpenMEE.Results: Twenty-five articles (26 outcomes) met the criteria. Gamification demonstrated a significant and very strong overall effect on learning motivation (ES = 1.061). Moderator analysis showed stronger effects at the senior high school level, while the influences of sample size and publication year were comparatively smaller.Conclusion: Gamification substantially enhances students’ learning motivation compared with non-gamified approaches. These results support the careful and contextual integration of gamified elements in teaching to optimize learner engagement and instructional quality.
Comparative Analysis of the Effectiveness of Lectures and Video-Assisted Learning in Improving Learning Outcomes of Students at TK Perjuangan in Bengkulu City Sari, Intan Permata; Muhammad, Adamu Abubakar; Sapri, Johanes
JENTIK : Jurnal Pendidikan Teknologi Informasi dan Komunikasi Vol. 4 No. 2 (2025): Jurnal Pendidikan Teknologi Informasi dan Komunikasi
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/jentik.v4i2.594

Abstract

Background of study: The main factor behind this research is the changing characteristics and learning style needs of Generation Alpha, today's kindergarten students. To create a fun, meaningful, and conscious learning environment, schools and teachers must incorporate digital technologies such as smart TVs or interactive large screen panels into their learning processes. Video-assisted learning (VAL) is considered attractive because it can make learning easier for kindergarten-aged children to understand and enjoy.Aims: The purpose of this study was to evaluate the effectiveness of conventional lectures compared to video-assisted learning in improving student learning outcomes at Perjuangan Kindergarten in Bengkulu City.Methods: This study used an experimental method with a sample of fifteen students from Class B1 and fifteen students from Class B2, each of whom received different treatments. Pre-tests and post-tests with pictures tailored to aspects of child development were used to assess learning outcomes. To conclude the effectiveness of the two methods, the data were tested with N-Gain scores using SPSS.Result: Students in classes taught using the VAL method obtained an N-Gain score of 68.5%, indicating that this method is quite effective in improving their learning outcomes. Students in classes taught using the lecture method only obtained an N-Gain score of 26.9%, indicating that this method is not effective in improving student learning outcomes. This study shows a significant difference between the two methods.Conclusion: This study concluded that the Video Assisted Learning method was more effective in improving student learning outcomes than the lecture method.
Rasch Model Analysis for IoT-based PJBL Hybrid Learning Design in MBKM Curriculum Risdianto, Eko; Barjesteh, Hamed; Sanmugam, Mageswaran; Jamal, M. Abdul
JENTIK : Jurnal Pendidikan Teknologi Informasi dan Komunikasi Vol. 4 No. 2 (2025): Jurnal Pendidikan Teknologi Informasi dan Komunikasi
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/jentik.v4i2.605

Abstract

Background of study: Even though the use of Project-Based Learning (PjBL) and the Internet of Things (IoT) in education is still growing, the integration of these two technologies in high-level education is still being explored, particularly in the use of the Merdeka Belajar Kampus Merdeka (MBKM) curriculum and the Indikator Kinerja Utama (IKU). This kesenjangan highlights the need for innovative teaching methods that integrate pedagogy and technology to meet the challenges of the digital age.Aims: The purpose of this study is to analyze the need for developing collaborative hibrida learning designs that integrate PjBL and IoT technology in order to execute MBKM curricula and reach IKU pendidikan tinggi.Methods: This study employs a survey methodology in which data is collected by a survey consisting of eighteen questions posed to university professors. Model Rasch with Winstep lunak perangkat is used for data analysis to assess the validity and reliability of research instruments.Result: Results indicate a strong demand for a hybrid learning system that effectively combines PjBL with IoT to support collaborative and interactive learning. The instrument reliability achieved a Cronbach Alpha of 0.95, indicating excellent reliability, while the Person Reliability and Item Reliability were measured at 0.90 and 0.46, respectively. The findings suggest that all questionnaire items were fit for assessing the critical thinking capabilities of students across different institutions.Conclusion: These results highlight the necessity of developing hybrid learning models to address educational challenges in the digital era, ensuring curriculum alignment and facilitating the achievement of MBKM goals. This study contributes to advancing higher education by providing actionable insights into learning design development that integrates cutting-edge technologies.
A Lightweight Hybrid GLCM–MobileNetV2 Model for Batik Motif Recognition in Digital Cultural Learning Environments Haryanto Haryanto; Husna Sarirah Husin; Hartini Hartini; F.R Desiana Kardha; Widyo Ari Utomo
JENTIK : Jurnal Pendidikan Teknologi Informasi dan Komunikasi Vol. 5 No. 1 (2026): Forthcoming Issue
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/jentik.v5i1.637

Abstract

Background: Automatic identification of Surakarta Parang batik motifs presents significant challenges due to the high visual similarity among sub-motifs, where conventional Convolutional Neural Network (CNN) architectures often fail to capture fine-grained texture characteristics.Purpose of Study: A lightweight hybrid model is proposed to integrate 24 GLCM-derived texture features with MobileNetV2 spatial descriptors through a feature fusion strategy to improve motif classification accuracy.Methodology: The proposed methodology employs a hybrid feature extraction strategy, where 24 texture descriptors consisting of six statistical parameters (Contrast, Correlation, Homogeneity, Dissimilarity, ASM, and Energy) calculated across four orientations (0°, 45°, 90°, and 135°) with 1,280 deep spatial features obtained from the MobileNetV2 backbone.Main Findings: Experimental results demonstrate that the proposed hybrid model achieves an accuracy of 99%, representing a substantial performance gain over the baseline MobileNetV2 model (66.67%) and the GLCM-SVM approach (85%). These results indicate that the integration of statistical texture descriptors and deep spatial features notably enhances the recognition of complex batik patterns. Furthermore, the findings suggest that this feature fusion approach is highly effective in resolving the intricate geometric similarities of Parang sub-motifs, providing a more reliable and efficient alternative to standard deep learning models for fine-grained classification tasks.Novelty/Originality of This Study: The novelty of this study lies in the implementation of a feature fusion strategy that compensates for the limitations of lightweight CNNs in texture recognition by incorporating classical statistical descriptors, specifically tailored for the intricate patterns of Parang batik.

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