cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kab. sleman,
Daerah istimewa yogyakarta
INDONESIA
ELINVO (Electronics, Informatics, and Vocational Education)
Published by Universitas Negeri Yogyakarta
ISSN : 25806424     EISSN : 24772399     DOI : 10.21831
Core Subject : Science, Education, Engineering,
Computer Science & IT Education Electrical & Electronics Engineering
ELINVO (Electronics, Informatics and Vocational Education) is a peer-reviewed journal that publishes high-quality scientific articles in Indonesian language or English in the form of research results (the main priority) and or review studies in the field of electronics and informatics both in terms of their technological and educational development.
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 255 Documents
 22   23   24   25   26 
Data-Driven Assessment of Rice Yield Gaps in Rainfed Agriculture Using Predictive Modeling and Cluster Analysis Heru Ismanto; Lilik Sumaryanti; Daud Andang Pasalli
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 11 No. 1 (2026): May 2026
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/elinvo.v11i1.95776

Abstract

Optimizing agricultural productivity in marginal areas of Merauke Regency, South Papua Province, Indonesia, faces significant challenges due to a high yield gap and low input efficiency. This study proposes an innovative machine learning-based approach to evaluate and map the performance of upland rice farmer groups by using PFPL (Prospective Farmer Prospective Location) data, which only has previously been used administratively. By integrating a predictive model (Random Forest Regressor), success classification, and K-Means Clustering, this study builds an adaptive and replicable analytical framework to support Data-Driven agricultural decision-making. The analyzed dataset includes 30 farmer groups which containing technical information such as land area, seed use, pesticide use, and herbicide use, as well as actual and targeted yields. The feature engineering process yielded the input efficiency ratio as the primary variable. The Random Forest regression model achieved a near-perfect fit on the available dataset (R² = 0.95; RMSE = 0.41). However, given the limited sample size (30 farmer groups), the result should be interpreted cautiously and regarded as exploratory rather than conclusive. Cluster analysis revealed two segments: a high-input but inefficient group and an efficient group with very high yields. These results highlight that input quantity does not guarantee productivity without efficient use. This study not only expands the literature on agricultural intelligence but also offers a practical approach for policymakers to design efficiency-based interventions, incentives, and training. This approach is also relevant for accelerating digital transformation and food security in underdeveloped regions.
Optimizing Procedural Knowledge Transfer in AI Chatbot-Enhanced Flipped Learning Models on Computer Programming Course Admaja Dwi Herlambang; Aditya Rachmadi
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 11 No. 1 (2026): May 2026
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/elinvo.v11i1.96278

Abstract

This research investigates whether an AI chatbot can further enhance the effectiveness of the flipped classroom model by facilitating personalized learning in computer programming. A quasi-experimental pretest-posttest with a matched samples control group design was used with a population of 60 in a vocational high school in Indonesia. The experimental treatment group of 30 students received flipped instruction supported by AI chatbot; the 30-student strong control group had traditional flipped instruction. Cognitive test data, psychomotor tests, and self-reported learning questionnaires measured data. Results show that the AI-enhanced flipped classroom outperformed the traditional flipped classroom to a great degree in both personalized learning (adjusted mean difference = 12.30, p < 0.001, partial η² = .335) and procedural knowledge acquisition (mean difference = 9.70, p < 0.001). The effect sizes were large for individualized instruction (Cohen's d = 0.89, 95% CI: 0.75 to 1.03) and medium for procedural knowledge (Cohen's d = 0.62, 95% CI: 0.49 to 0.75). Of particular note was that the experimental group not only did better on coding tasks (d = 1.18), but even more so on debugging efficiency (d = 1.48).
Comparing User Perceptions of User Interface and User Experience in Learning Management Systems: A UEQ-Based Study Across Two Universities Tri Setya Darmawan; Agung Fatwanto
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 11 No. 1 (2026): May 2026
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/elinvo.v11i1.86000

Abstract

User perceptions toward software application interface and experience are important factors for the successful implementation of software systems. Therefore, this study was aiming to investigate these success factors by comparing user perceptions towards software application interface and experience when using software applications. This research used a inferential quantitative approach. The instrument used in this study was the UEQ (User Experience Questionnaire) formulated by the UEQ Team. The object of this study was two e-learning systems deployed at two state universities in Yogyakarta. Data on user perceptions toward software application interface and experience were collected twice. The collected data were then statistically analysed to obtain results for equivalence test, paired t-tests, Wilcoxon tests, statistical power, and Cohen's D values. Based on equivalence testing on the data gathered during the first and second collection stages, it was found that user perceptions toward software application interface and experience were equivalent and not different for most criteria and sub-criteria, except for the enjoyable, good, pleasing, and pleasant sub-criteria at University A and enjoyable, good, and motivating at University B. Meanwhile, based on the results of paired t-tests and Wilcoxon tests, it was found that user perceptions toward software application interface and experience were also equivalent and not different for most criteria and sub-criteria, except for the pleasing and good sub-criteria at University A, and the enjoyable and pleasing sub-criteria at University B. Therefore, it is concluded that there was generally no difference in user perceptions toward software application interface and experience when using those e-learning systems.
Evaluating LLM-Based Institutional Information Chatbot Responses Using a Preliminary Human-Scored Analytic Rubric and Automatic Metrics Rosni Lumbantoruan; Arnaldo Marulitua Sinaga; Markus Pardianto Hutagalung; Priskila Christine Natalia Parapat; Mutiara Teccalonica Simanjuntak
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 11 No. 1 (2026): May 2026
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/elinvo.v11i1.91571

Abstract

Large language models (LLMs) are increasingly used for answering institutional information enquiries in higher education, yet the quality of responses is not straightforward to evaluate, as factual accuracy alone does not account for interactive qualities such as clarity, conversational flow, error handling and personalisation. This pilot study developed, preliminary examined a human-scored analytic rubric for assessing ChatGPT responses in a higher-education institutional-information setting and explored the alignment of selected rubric scores with reference-based automatic metrics. We designed a literature-informed rubric comprising 15 criteria across five conceptual domains. Of these, 14 criteria were operationalised through 42 rubric questions, and system usability was rated separately using the System Usability Scale. Seventy-five qualified students from one higher-education institution rated the chatbot responses using a four-point scale. Preliminary evidence at the item level was provided by item-total correlations and Cronbach’s alpha, while Pearson and Spearman correlations were used to investigate the alignment between human scores and reference-based metrics namely ROUGE-1, ROUGE-2, ROUGE-L and SacreBLEU for four content-oriented criteria. The results showed positive but partial agreement between human ratings and referenced-based metrics, with stronger agreement for clarity and up-to-date response than for accuracy and relevance. These findings suggest that reference-based metrics can complement, but not replace, human evaluation for insitutional information chatbot assessment. The study was confined to one institution and did not incorporate inter-rater reliability, expert validation or factor analysis. Thus, the rubric should be seen as a preliminary evaluation instrument rather than a fully validated scale.
Data-Centric Preprocessing Outperforms Loss Modifications for Hard-Class Plant Disease Classification Using MobileNetV3 Bambang Priambodo; Abdul Fadlil; Sunardi
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 11 No. 1 (2026): May 2026
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/elinvo.v11i1.95319

Abstract

Data-centric approaches have gained attention in plant disease classification; however, a systematic evaluation of underperforming ‘hard classes’ remains limited. This study proposes a four phase pipeline comprising granular error diagnosis, no-reference image quality assessment, class-targeted augmentation, and an ablation study to improve hard-class robustness without increasing model complexity. Using the New Plant Disease dataset and a lightweight MobileNetV3-Small backbone, we first established a baseline. Based on this baseline performance, we identified 12 hard classes (defined as those with either F1 < 0.96 or recall < 0.96 on the baseline model) with a mean F1 of 0.9260. The optimal configuration (categorical cross-entropy, no class weighting, baseline head) raised the mean hard-class F1 to 0.9668, corresponding to an absolute improvement of +4.08 percentage points, while maintaining global test accuracy at 98.08%. A two-tier design with three random seeds confirmed robustness (mean hard-class F1 = 0.9652 ± 0.0020). Unexpectedly, after data enrichment, inverse frequency class weighting degraded hard-class F1 (to 0.9621), and the default focal loss parameters offered no additional benefit over plain cross-entropy (0.9647). The MobileNetV3-Small showed comparable performance to the heavier EfficientNetB0 under our evaluation protocol, with no statistically significant difference detected (p = 0.089; limited power due to n = 3). Tomato Target Spot (C35) remained the most persistent bottleneck (F1 ≈ 0.90). Future work includes explainable AI for error analysis and real‑field validation.

Page 26 of 26 | Total Record : 255

 22   23   24   25   26 

Filter by Year

2015 2026


Reset
Filter By Issues
All Issue Vol. 11 No. 1 (2026): May 2026 Vol. 10 No. 2 (2025): November 2025 Vol. 10 No. 1 (2025): May 2025 Vol. 9 No. 2 (2024): November 2024 Vol. 9 No. 1 (2024): Mei 2024 Vol. 8 No. 2 (2023): November 2023 Vol. 8 No. 1 (2023): Mei 2023 Vol 7, No 2 (2022): November 2022 Vol 7, No 1 (2022): Mei 2022 Vol. 7 No. 1 (2022): Mei 2022 Vol. 6 No. 2 (2021): November 2021 Vol 6, No 2 (2021): November 2021 Vol. 6 No. 1 (2021): Mei 2021 Vol 6, No 1 (2021): Mei 2021 Vol. 5 No. 2 (2020): November 2020 Vol. 5 No. 1 (2020): Mei 2020 Vol 4, No 2 (2019): November 2019 Vol. 4 No. 2 (2019): November 2019 Vol. 4 No. 1 (2019): May 2019 Vol 4, No 1 (2019): May 2019 Vol. 3 No. 2 (2018): November 2018 Vol 3, No 1 (2018): May 2018 Vol 3, No 1 (2018): May 2018 Vol. 3 No. 1 (2018): May 2018 Vol. 2 No. 2 (2017): November 2017 Vol 2, No 1 (2017): May 2017 Vol 2, No 1 (2017): May 2017 Vol. 2 No. 1 (2017): May 2017 Vol. 1 No. 3 (2016): November 2016 (Consist of 7 Articles) Vol 1, No 3 (2016): November 2016 (Consist of 7 Articles) Vol. 1 No. 2 (2016): May 2016 (Consist of 7 Articles) Vol 1, No 2 (2016): May 2016 (Consist of 7 Articles) Vol. 1 No. 1 (2015): November 2015 (Consist of 9 Articles) Vol 1, No 1 (2015): Oktober More Issue