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All Journal Energy: Jurnal Ilmiah Ilmu-ilmu Teknik
Syaad Patmanthara
Department of Electrical Engineering and Informatics, Universitas Negeri Malang, 65145, Indonesia
Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering

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An Epistemological Approach to Explainable Automated Assessment of Open Concept Map Propositions Using SHAP Mega Satya Ciptaningrum; Syaad Patmanthara; Didik Dwi Prasetya
ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK (Special Issue on Engineering Paradigm 2025 Edition)
Publisher : Universitas Panca Marga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51747/energy.si2025.255

Abstract

Concept mapping is widely recognized as an effective method for supporting meaningful learning and critical thinking because it allows teachers to assess students’ underlying knowledge structures. However, evaluating concept maps and providing feedback remain challenging, as these processes are time-consuming, increase teachers’ workload, and can reduce instructional efficiency. To address this issue, this study applies Transformer-based architectures, which rely on large-scale pre-training and task-specific fine-tuning, to develop an automated assessment system for concept maps. In addition, Explainable Artificial Intelligence (XAI) is integrated through the SHAP (SHapley Additive exPlanations) framework to generate interpretable explanations of the model’s scoring decisions. Using Transformer models such as BERT and DeBERTa, SHAP values are computed at the token level to show how individual words within each proposition contribute to the final score. The results indicate that tokens with positive SHAP values increase scores in line with correct rubric indicators, whereas negative values reduce them. Tokens that consistently show positive contributions in high-scoring outputs reflect stable and faithful model reasoning. Overall, the findings demonstrate that combining Transformer-based assessment with SHAP explanations improves epistemic transparency by aligning the model’s internal reasoning with expert evaluation criteria, thereby supporting more reliable, interpretable, and trustworthy automated feedback in concept mapping-based learning.
Sentiment Analysis of YouTube Comments Using the K-Nearest Neighbors (KNN) Method from an Axiological Perspective Merinda Lestandy; Syaad Patmanthara
ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK (Special Issue on Engineering Paradigm 2025 Edition)
Publisher : Universitas Panca Marga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51747/energy.si2025.257

Abstract

The rapid development of social media as a space for digital interaction has increased the need for sentiment analysis to understand public opinion in a systematic and measurable way. This study analyzes YouTube comment sentiment using the K-Nearest Neighbor (K-NN) method while also examining the axiological value of applying this technology in support of a more ethical digital ecosystem. The dataset consists of 8,200 YouTube comments obtained from Kaggle without predefined sentiment labels. The data were preprocessed through case folding, tokenization, stopword removal, stemming, and normalization. Initial sentiment labels were generated automatically using K-Means clustering to form two classes—positive and negative—and were partially verified manually. The labeled data were split into training and testing sets with ratios of 50:50, 60:40, 70:30, and 80:20, and evaluated using K-NN with k values of 3, 5, 7, and 9. Model performance was assessed using a confusion matrix with accuracy, precision, recall, and F1-score metrics. The results show that accuracy ranges from 0.95 to 0.96, with the best performance achieved at a 70:30 split and an optimal k value yielding 0.96 accuracy. Beyond technical contributions, this study highlights the ethical and practical value of sentiment analysis for interpreting public opinion, supporting fairer content moderation, and improving communication quality in social media environments.
Co-Authors Didik Dwi Prasetya Mega Satya Ciptaningrum Merinda Lestandy