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Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control
Published by Universitas Muhammadiyah Malang
ISSN : 25032259     EISSN : 25032267     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control was published by Universitas Muhammadiyah Malang. journal is open access journal in the field of Informatics and Electrical Engineering. This journal is available for researchers who want to improve their knowledge in those particular areas and intended to spread the knowledge as the result of studies. KINETIK journal is a scientific research journal for Informatics and Electrical Engineering. It is open for anyone who desire to develop knowledge based on qualified research in any field. Submitted papers are evaluated by anonymous referees by double-blind peer review for contribution, originality, relevance, and presentation. The Editor shall inform you of the results of the review as soon as possible, hopefully within 4 - 8 weeks. The research article submitted to this online journal will be peer-reviewed at least 2 (two) reviewers. The accepted research articles will be available online following the journal peer-reviewing process.
Arjuna Subject : -
Articles 584 Documents
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Comparative Evaluation of BM25–FAISS and Small-LLM–GPT in Retrieval-Augmented Generation Concept Map Assessment Maskur Maskur; Didik Dwi Prasetya; Triyanna Widiyaningtyas; Azlan Mohd Zain
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 1, February 2026
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i1.2594

Abstract

Concept map-based assessment is a practical approach to measure students’ conceptual understanding, but manual assessment still faces challenges such as subjectivity, inconsistency, and limited scalability. This study proposes the application of Retrieval-Augmented Generation (RAG) as an artificial intelligence-based automated assessment solution in an educational context. The objectives of this study are to compare the effectiveness of two retrieval methods, BM25 and FAISS, and to analyse the trade-off between large-scale generative models (GPT) and Small-LLM in assessing concept map propositions. This study uses a quantitative experimental approach by combining a retriever and a generator in the RAG system. Performance evaluation is carried out using the Macro-F1 and QWK metrics to measure agreement with expert judgment, and the Explanation Relevance Score (ERS) to assess explanation quality. The experimental results show that the FAISS–GPT combination achieves the best performance, with a Macro-F1 of 0.338 and a QWK of 0.146, slightly superior to the BM25–GPT combination. In contrast, the use of Small-LLM, both with BM25 and FAISS, showed lower performance with Macro-F1 values in the range of 0.167–0.221 and QWK close to zero. This finding confirms that semantic-based retrieval plays a vital role in improving the accuracy of automated assessment, while large-scale generative models are more effective in representing conceptual relationships in depth. This study contributes through a comparative analysis of retrievers and generators, and by introducing ERS as an additional metric for RAG-based automated assessment in the field of education.
Indonesian Pre-trained Language Models with OCEAN-aware Query Expansion Ranking for Personality Prediction from Social Media Text Gede Aditra Pradnyana; I Gede Mahendra Darmawiguna
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 3, August 2026
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i3.2890

Abstract

Personality prediction from social media text has become a critical topic in computational social science, as online posts frequently reflect individual behavioral and psychological characteristics. However, predicting personality from Indonesian social media material remains challenging due to informal language, slang, abbreviations, noisy expressions, and implicit personality-related cues. This study proposed a new OCEAN personality prediction framework that integrates pre-trained language models with an OCEAN-aware Query Expansion Ranking feature learning mechanism. Unlike conventional approaches that rely mainly on contextual embeddings, the proposed framework introduced trait-specific class-discriminative lexical features to strengthen personality-related representation. The prediction task was structured as five separate binary classification problems, with each personality attribute divided into High and Low groups. The experiments were conducted in two stages. First, numerous pre-trained language models, including IndoBERT, IndoBERTweet, Indonesian RoBERTa, and multilingual BERT, were fine-tuned and compared to identify the most suitable contextual representation model. The best baseline model, multilingual BERT, achieved an average accuracy of 75.48% and an average F1-score of 73.58%. Second, multilingual BERT was integrated with class-discriminative lexical features generated by the proposed OCEAN-aware Query Expansion Ranking mechanism. The trait-specific configuration improved the average accuracy to 78.76% and the average F1-score to 76.48%. These results demonstrated that integrating contextual semantic representations with OCEAN-aware lexical feature learning enhanced personality prediction from Indonesian social media material while also offering a clearer representation of trait-relevant language signals.
Enhancing Model Trust in Load Forecasting using Counterfactual Explanations Muhammad Syarif Hidayatullah; Wahyul Amien Syafei; Tarno
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 4, November 2026 (Article in Progress)
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study investigates the effectiveness of Counterfactual Explanations (CE) in improving the interpretability of short-term electricity load forecasting models. The forecasting model was developed using Random Forest Regression (RFR) due to its ability to capture nonlinear multivariate patterns in electricity load data and to provide global, descriptive interpretability through feature-importance analysis. However, feature importance provides static and potentially biased explanations, making it insufficient for instance-level interpretability. To address this limitation, the Diverse Counterfactual Explanations (DiCE) framework was integrated to generate multiple “what-if” scenarios that illustrate model-consistent input adjustments capable of shifting forecast outcomes. The study uses hourly electricity load data from Panama collected between 2015 and 2020, with data from 2020 excluded during preprocessing due to atypical demand patterns caused by COVID-19. The model was trained using time-series cross-validation and optimized through grid search. Forecasting performance was evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). Counterfactual Explanations were assessed using validity, proximity, and compactness metrics, indicating that most generated scenarios are goal-aligned and remain moderately close to the original instances, while the compactness results reflect a feasibility trade-off when large prediction shifts are required. Overall, combining RFR with counterfactual analysis enhances model transparency by bridging global and local interpretability perspectives and provides decision-relevant, scenario-based insights to support transparent and data-driven planning in the energy sector.
Deep Learning–Based ASD Detection from EEG Signals: A Comparison of InceptionTime and XceptionTime Architectures Rachmawati Rachmawati; Arsy Febrina Dewi; Melinda Melinda; Razita Nadhira; Aufa Rafiki; Nurlida Basir
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 4, November 2026 (Article in Progress)
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition characterized by persistent social-communication impairments and restricted or repetitive behaviors. While clinical assessment remains the diagnostic gold standard, the demand for objective and scalable screening tools has motivated EEG-based automated classification. However, under controlled experimental settings, the relative contributions of preprocessing choices and deep learning architecture selection to ASD detection performance remain insufficiently quantified. This study systematically benchmarks artifact-aware preprocessing and model architecture by comparing InceptionTime and XceptionTime across four end-to-end processing schemes that isolate the effects of independent component analysis (ICA) and classifier design under identical segmentation and evaluation protocols. A public King Abdulaziz University EEG dataset comprising 16 subjects (8 ASD, 8 controls) was used. Signals were bandpass-filtered using a fourth-order Butterworth filter (0.5–45 Hz), optionally denoised via ICA, and segmented into 4-s windows with 50% overlap. Models were evaluated using 8-fold subject-wise cross-validation. Performance was assessed using accuracy, precision, sensitivity, specificity, and F1-score, and statistical significance was tested with the Wilcoxon signed-rank test. The Butterworth+ICA+InceptionTime pipeline achieved the best results, with a mean accuracy of 0.9886 ± 0.0046 and an F1-score of 0.9879 ± 0.0049. ICA inclusion and architecture choice yielded significant improvements in accuracy (  for both, ). These findings indicate that structured artifact suppression and multi-scale temporal modeling jointly enhance EEG-based ASD classification, supporting their use in robust clinically oriented screening systems.

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