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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 6 Documents
 1 
Search results for , issue "Vol. 4 No. 1 (2025): Jurnal Pendidikan Teknologi Informasi dan Komunikasi" : 6 Documents clear
Analyzing the Effects of the Covid-19 Pandemic on University Students' Academic Performance Administration: A Post-Pandemic Assessment Arshad, Mahek; Ajmal, Muhammad; Riady, Yasir
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.v4i1.435

Abstract

Background of study: Covid-19 disease has significantly impacted all characteristics in life worldwide. Higher education is encountering substantial problems throughout the diverse industries impacted. This transition has resulted in significant implications, especially for the students' in education performance. Aims and scope of paper: This learning aimed to examine the effect of the Covid-19 on university students' academic routine and to assess the problems posed by the adoption of online learning practices during this period. The study also seeks to provide remedies by creating conceptual models to mitigate the effects of these difficulties in the future. Methods: This study applied a qualitative methodology to analysed the textual material and employed a fundamental quantitative methodology for doing descriptive analysis that corroborates the findings. A self-administered questionnaire was created. The study employed purposive sampling, yielding a total of 106 respondents. Students encountered various obstacles, including inadequate assistance to adjust to the alterations in their education resulting from COVID-19. Results: They faced insurmountable workloads that were untenable without sufficient assistance from educators. Moreover, challenges included inadequate computer literacy, absence of energy and internet access at home, and sluggish connection speeds exacerbated their learning experiences, ultimately affecting their academic achievement. Universities are advised to prioritize the allocation of training and resources to adeptly handle analogous situations in the future. Conclusion: Educators should be provided assistance in developing interactive online learning resources and materials for institutions of higher education. Establishing and maintaining efficient communication techniques with management, staff, educators, and students is crucial for ensuring that all parties are informed and updated regarding subsequent phases of the transition and ongoing training support.
IoT-Based Cup Sealer Machine Automation Using Nodemcu ESP32 Aminudin, Nur; Usmanto, Budi; Feriyanto, Dwi; Septasari, Dita; Andika, Tahta Herdian; Muhammad, Adamu Abubakar
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.v4i1.442

Abstract

Background of study: The development of the food and beverage industry demands innovation in efficient and reliable packaging processes. Conventional cup sealing machines often face limitations in speed and precision, necessitating technology-based solutions. Aims and scope of paper: This objective of the study is to design and implement an automated cup sealer system based on the Internet of Things (IoT), using the NodeMCU ESP32, capable of performing sealing and real-time monitoring. The system integrates a flowmeter sensor to detect the presence of cups, a stepper motor for the sealing process, and an LCD display along with WiFi connectivity for monitoring production data. Methods: The methodology involves hardware design, control system programming, and performance testing of the device under various temperature and motor speed parameters. Result: The results show that the system can increase production efficiency by up to six times compared to the manual method, with a capacity of 300 cups per hour and a sealing success rate of 95% at an optimal temperature of 100°C and a motor speed of 10 RPM. Synchronization among components was enhanced through sensor calibration and algorithm development. Conclusion: In conclusion, this automated system not only improves efficiency and accuracy but also offers flexibility and IoT-based control, making it highly relevant for small and medium-sized industries.
Predicting Student Depression Using the Naive Bayes Model on the Student Depression Dataset from Kaggle Sonjaya, Rebina Putri; Gintara, Andre Rangga; Riza, Lala Septem; Nursalman, Muhammad; Nugraha, Eki; Wahyudin, Didin
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.v4i1.448

Abstract

Background of Study: The increasing prevalence of depression among college students highlights the urgent need for effective early detection strategies to promote mental well-being within higher education environments. Aims and Scope of Paper: This study aims to develop a predictive model for student depression using the Naive Bayes classification algorithm, with a focus on identifying key contributing factors from student-related data. Methods: The research utilizes the Student Depression dataset from Kaggle, containing structured survey data on academic stress, sleep duration, financial stress, GPA, and family mental health history. Data preprocessing included feature selection, handling of missing values, and normalization. The dataset was split into training and testing sets at a 75:25 ratio. Model training was conducted using the R programming language with the application of Laplace smoothing. Result: The Naive Bayes model achieved an accuracy of 77.66%, a specificity of 84.21%, and a sensitivity of 68.42%, indicating strong predictive performance, particularly in identifying depressive cases. Financial and academic stress were identified as the most influential factors. Conclusion: Despite its simplicity, the Naive Bayes algorithm proves to be an effective tool for initial screening of students at risk of depression, offering valuable support for educational institutions in delivering timely mental health interventions.
Development of Interactive Learning Media on Light Vehicle Engine System Based on Classpoint to Improve Student Learning Achievement (A Study on Light Vehicle Engine System Components for Grade XI Students at SMK Negeri 3 Seluma) Hadi, Sofian; Sapri, Johanes; Risdianto, Eko
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.v4i1.449

Abstract

Background of study: The low academic achievement of students in understanding the elements of light vehicle engine systems, particularly in the subject of engine management systems (EMS) at SMK Negeri 3 Seluma, is caused by the lack of use of interactive learning media that are interesting and suited to the characteristics of the students. Therefore, there is a need to develop innovative and interactive learning media to improve student academic achievement. Aims and scope of paper: This research aims to develop ClassPoint-based interactive learning media  on engine management system (EMS) materials and test the feasibility and effectiveness of these media in improving student learning achievement. The scope of the research is focused on grade XI TKR students at SMK Negeri 3 Seluma on the competence of light vehicle engineering regarding the elements of the light vehicle engine system. Methods: The research method uses a Research and Development (R&D) approach with the ADDIE (Analysis, Design, Development, Implementation, Evaluation) development model. Results: The results of the study showed that the ClassPoint-based learning media  developed was declared very feasible based on the results of validation of material experts, media experts, and positive responses from teachers and students with  an overall media feasibility assessment of 92.87% with an average percentage of 92.87%. In addition, the results of the effectiveness test showed a significant increase in student learning achievement, which was evidenced by the comparison of pretest and posttest scores between the experimental class and the control class using the t test with the result t calculated = 2.730 > t table = 1.995 (N=69, α=0.05). Conclusion: The conclusion of this study is that ClassPoint-based interactive learning media is effective and feasible to be used as a learning tool in engine management system (EMS) materials to improve student learning achievement.
The Effect of Conducting Practical Hand-On Activities in SMK Negeri 4 Muko-Muko Siska, Jumiati; Franzhardi, Didi
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.v4i1.434

Abstract

Background of study: The Fourth Industrial Revolution demands graduates equipped with practical, adaptive skills beyond theoretical knowledge. However, conventional teacher-centered instruction often hinders the development of essential competencies, especially in vocational subjects such as graphic design using CorelDRAW, where direct practice is crucial. Aims and scope of paper: This study aims to examine the effectiveness of the Hands-On Activity learning model in improving learning outcomes, motivation, and technical skills of grade X Multimedia students at SMK Negeri 4 Mukomuko. Methods: A quantitative quasi-experimental method was employed using a Nonequivalent Control Group Design involving two naturally formed classes of 30 students each. Class X Multimedia A was assigned as the experimental group (Hands-On Activity), and Class X Multimedia B as the control group (conventional learning). Data were collected through validated pretest and posttest instruments. Normality was tested using Shapiro-Wilk, and homogeneity using Levene's test. An independent samples t-test was conducted to determine the effect of the treatment. Result: The results showed a significant improvement in the experimental group’s learning outcomes. The normality test confirmed the data were normally distributed (Sig. > 0.05), and the homogeneity test indicated equal variances (p = 0.125). Hypothesis testing revealed a t-value of 10.57, exceeding the critical value of 2.0024, leading to the acceptance of the alternative hypothesis. Conclusion: The study concludes that the Hands-On Activity model effectively enhances student engagement, motivation, and mastery of both theoretical and practical knowledge in Informatics, making it a suitable strategy for vocational education.
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.

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