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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 575 Documents
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Imitation Learning to Accelerate Training Process of Multi-Agent Reinforcement Learning in 2v2 Pong Game Marvin Yonathan Hadiyanto; Budi Harsono; Indra Karnadi; Ivan Tanra
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 2, May 2026
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i2.2564

Abstract

Training multi-agent reinforcement learning (MARL) systems often requires a significant amount of time due to sample inefficiency, particularly when agents must perform extensive exploration in complex environments and coordinate among multiple entities. This study proposes the use of imitation learning to accelerate the MARL training process in a 2v2 pong game by leveraging demonstrations from a 1v1 pong game to shape the initial policy without undergoing inefficient exploration procedures. We employ a deep Q-network (DQN) framework with centralized training and decentralized execution (CTDE) to compare the performance of pretrained and untrained agents in the 2v2 pong environment. Experimental results show that learning from demonstrations in the 1v1 setting improves reward accumulation and game scores of pretrained agents in the 2v2 pong game. The performance improvement peaks at 700 demonstration learning steps and diminishes at larger learning steps due to excessive memorization of the demonstration gameplay. Furthermore, comparative experiments demonstrate that imitation learning with 700 learning steps achieves learning efficiency improvements of approximately 300% and 571% compared to the zonation method and standard reinforcement learning pretraining, respectively. These results indicate that imitation learning from demonstrations can effectively reduce the prolonged training process in MARL, offering a viable solution, particularly when data collection, computational resources, and training time are severely constrained.
Improving Postprandial Glucose Forecasting Using Diagnosis-Aware Stacked Learning Fatma Indriani; Mohammad Reza Faisal; Naufal Said
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 2, May 2026
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i2.2566

Abstract

Predicting glucose levels after a meal (postprandial glucose) can help anticipate abnormal responses and improve diabetes management. Yet such prediction remains difficult because post-meal glucose depends on multiple interacting factors, including prior glucose trends, meal composition, and recent activity. This study develops machine learning models to forecast short-term post-meal glucose levels using the CGMacros dataset, which combines continuous glucose monitoring (CGM) data from Dexcom and Libre sensors with meal macronutrient annotations and activity measurements. Several feature combinations and regression models were evaluated to identify an optimal representation. Results show that combining baseline glucose statistics with meal composition yields the lowest error across all regressors. Building on this feature configuration, a stacked learning framework was implemented in which a global model provides initial predictions refined by diagnosis-specific CatBoost regressors for Healthy, Pre-diabetes, and Type 2 Diabetes groups. Across 18 configurations spanning two sensors and three horizons (30, 60, 120 minutes), stacking reduced normalized RMSE by 3.5 ± 3.7% on average, with the strongest improvements at 120-minute horizons (mean 5.5%) and for linear global models (up to 13.6% reduction). Gains varied by diagnosis group and sensor type, highlighting the importance of device-aware validation. These results demonstrate that diagnosis-aware stacking enhances both accuracy and robustness, offering a practical foundation for personalized glucose forecasting in digital health systems.
Integrating Tabular Data and Textual Representations for Clinical Risk Prediction Using Machine Learning and Large Language Models M.Rafly Rahman; Setio Basuki; Muhammad Ilham Perdana; La Febry Andira Rose Cynthia
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 2, May 2026
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i2.2570

Abstract

Global health is currently facing serious challenges due to the increasing number of chronic disease patients, such as those with heart failure, diabetes, and cancer. This issue arises from the limitations of electronic health record (EHR) systems, which are not yet fully capable of ensuring accurate clinical diagnoses because of potential data input errors and delays in symptom identification by medical personnel. In response to this issue, this paper focuses on the integration of medical tabular data with a classification approach based on classical machine learning (ML) and large language models (LLM) to improve the accuracy of patient diagnosis predictions. This paper aims to develop and compare the performance of various ML models, such as XGBoost, SVM, and logistic regression, as well as LLM models like Gemini, LLaMA, and Qwen in fine-tuning, few-shot, and zero-shot scenarios. The paper results show that the combination of Gemini and the few-shot approach (250 shots) achieved the highest accuracy of up to 99.8% in predicting heart failure risk. The main finding of this study is that the narrative text representation of tabular data processed with LLM significantly enhances contextual understanding and classification accuracy, making this approach highly potent for application in AI-based clinical decision-making.
Evaluating Synonym Augmentation Impact on SBERT Performance for Indonesian Social Media Style Classification Jessicha Putrianingsih Pamput; Aindri Rizky Muthmainnah; Dewi Fatmarani Surianto; Nur Azizah Eka Budiarti; Abdul Wahid
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 2, May 2026
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i2.2580

Abstract

Language on social media reflected the identity and characteristics of its users, including differences in language style between generations. Millennials and Generation Z were two dominant demographic groups in digital communication that exhibited linguistic variations, which often caused gaps in understanding during online interactions. Variations in language structure and expression posed challenges in understanding the context of cross-generational communication. Therefore, this study aimed to classify linguistic styles across generations in social media texts by combining Sentence-BERT (SBERT). FastText-based synonym augmentation in Indonesian, and Support Vector Machine (SVM) as a margin-based classification model that utilizes embedding representations from SBERT. The results showed that synonym augmentation improved model accuracy from 85% to 93%, with a similarity threshold of 0.7 providing the best balance between data diversity and semantic consistency. These findings confirmed that synonym-based augmentation and SBERT semantic adaptation were effective in capturing generational linguistic differences in informal Indonesian. This approach had the potential to be applied in other NLP tasks that required contextual understanding of social language variation, such as sentiment analysis and cross-generational dialect detection.
Distributed Secondary Control with Consensus-Based Adaptive Droop and Voltage Observer for DC Microgrids Khusnul Hidayat; Arif Nur Afandi
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 2, May 2026
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i2.2631

Abstract

This paper proposes a fully distributed secondary control scheme for a low-voltage DC microgrid with ring topology. The main objectives are to restore the common bus voltage to its nominal reference and to achieve accurate proportional current sharing among distributed generator units in the presence of non-uniform line resistances and mixed load conditions. The proposed secondary layer integrates a consensus-based adaptive droop controller and a consensus-based voltage observer. The adaptive droop mechanism dynamically adjusts the virtual impedance of each converter using neighbor-to-neighbor current information to reduce current-sharing errors, while the voltage observer provides a distributed estimate of the average bus voltage to compensate for droop-induced voltage deviations. The effectiveness of the proposed method is validated through simulation on a ring-configured DC microgrid consisting of four converters and five buses. A comparative study demonstrates that conventional droop control improves current sharing but introduces significant steady-state voltage deviation. By contrast, the proposed integrated approach achieves nearly zero current-sharing error while maintaining the DC bus voltage close to its reference value. The dynamic performance is further evaluated under both resistive-load and constant-power-load variations. The results show that the controller ensures fast voltage restoration, accurate proportional current sharing, and stable operation without sustained oscillations, even under nonlinear constant-power-load conditions. These findings indicate that the proposed distributed secondary control strategy provides robust voltage regulation and precise current sharing for ring-type DC microgrids.
A Data-Driven Framework Integrating Clustering and Classification for Fair Tuition Grouping (UKT) Prediction Windy Chikita Cornia Putri; Wiyli Yustanti; Ervin Yohannes; Yoyok Prastyo
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 3, August 2026 (Article in Progress)
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i3.2578

Abstract

This study aims to identify the most effective combination of feature selection techniques and classification algorithms for predicting student tuition groups (Uang Kuliah Tunggal, UKT) based on pre-admission data. Three feature selection methods Exploratory Factor Analysis (EFA), Recursive Feature Elimination (RFE), and Random Forest Feature Importance (RFFI) were employed and combined with five supervised learning models: Decision Tree, Random Forest, Support Vector Machine (SVM) with RBF kernel, Naïve Bayes, and K-Nearest Neighbor (KNN). The results demonstrate that the EFA–SVM (RBF) combination achieved the best performance, with an average accuracy exceeding 98%, outperforming other models across most faculties. EFA also yielded the highest Silhouette Score (0.2933), indicating a more stable and distinct cluster structure compared to RFE (0.2564) and RFFI (0.2575). These findings highlight the critical role of appropriate feature selection in improving classification accuracy and model generalization, particularly when emphasizing socioeconomic variables such as parental income, land area, housing conditions, and basic family facilities. The integration of factor-based dimensionality reduction with non-linear classification algorithms proved effective in developing a more transparent and equitable UKT prediction model. This research contributes to the advancement of data-driven decision support systems in higher education and provides a foundation for future automation in tuition group determination processes.
Regularization Techniques to Improve the Stability and Accuracy of MLC Algorithm Usman Sudibyo; Noor Ageng Setyanto; Ahmad Wahid Kurniawan; Carissa Devina Usman
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 3, August 2026 (Article in Progress)
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i3.2583

Abstract

Maximum Likelihood Classification (MLC) is a classification algorithm that has important applications in the fields of image processing and remote sensing. No use of MLC was found in other fields. MLC assumes that data comes from a certain probability distribution (for example, a normal distribution), which may be too simple to describe complex data or have a non-normal distribution. This can lead to poor performance in situations where distribution assumptions are not met. That is why in various literatures there is no use of MLC for classification problems other than remote sensing. We propose a regularization technique to reduce distribution assumption errors in MLC called Regularization on maximum likelihood classification (RMLC). Regularization techniques are integrated into the covariance matrix, where regularization can make the data variance larger or smaller than the actual variance. This technique can also overcome singularities in the covariance matrix, non-Gaussian data, and data containing outliers. Experimental results on 13 public datasets show a significant increase in accuracy performance. The average accuracy increase reaches more than 11%, from 0.802 to 0.919, highlighting its potential for broader applicability and enhanced performance
Optimization of Solar Panel Installation Potential Mapping Based on Convolutional Neural Network Maulisa Oktiana; Rika Sri Utami; Ilham Maulana; Annisa Gusti Ananda
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 2, May 2026
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i2.2584

Abstract

The increasing global energy demand and the depletion of fossil fuel resources have accelerated the transition toward renewable energy. Solar energy is considered one of the most promising sustainable energy sources. However, identifying suitable locations for solar panel installation remains challenging due to geographic and environmental variability across different regions. This study proposes a Convolutional Neural Network (CNN)-based approach to map potential solar panel installation areas using high-resolution satellite imagery. The model is designed to extract spatial features from land surfaces, including land cover characteristics, building density, and reflectance patterns derived from Sentinel-2 imagery obtained through Google Earth Engine. The proposed framework utilizes a VGG19-based architecture with transfer learning to improve feature extraction and classification performance. Experimental results demonstrate that the proposed model achieves an accuracy of 94.2% in classifying areas suitable for solar panel installation. These findings indicate that deep learning–based spatial analysis can provide an effective approach to support large-scale solar energy planning and decision-making.
AIoT-Enabled Automatic Waste Sorting System with Real-Time WhatsApp Notifications Muchamad Rusdan; Sri Kuswayati
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 3, August 2026 (Article in Progress)
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i3.2593

Abstract

The waste management crisis, particularly in educational institutions, requires innovative solutions that combine artificial intelligence and automation. This research develops and evaluates an automated waste sorting system based on Artificial Intelligence of Things (AIoT) integrated with WhatsApp notifications. The system utilizes the EfficientNet-B0 deep learning model optimized with transfer learning and runs on a Raspberry Pi 4 edge device to classify waste into five categories: plastic, paper, metal, glass, and organic in real time. Classification results are translated into physical actions by a servo actuator mechanism, while ultrasonic sensors monitor trash bin capacity. The real-time notification system via WhatsApp API sends alerts to administrators. A 30-day evaluation on campus showed that the system achieved 92.3% classification accuracy with an inference latency of 1.8 seconds. The mechanical system successfully sorted waste with a 94.5% success rate, and WhatsApp notifications had a 99.1% delivery rate, with an average administrator response time of 8.2 minutes during operational hours. A comparative analysis demonstrated that this system increased sorting efficiency by 87% and reduced operational costs by 45% compared to manual waste sorting methods. These findings conclude that the proposed integration of edge AI, mechanics, and WhatsApp notifications creates a smart waste management solution that is not only effective and real-time but also practical, economical, and sustainable for wider implementation.
Accuracy Comparison of Multivariate Newton-Raphson, Newton-Kantorovich, and Levenberg–Marquardt Methods for Solving Nonlinear Systems Using Numerical Simulation Syaharuddin Syaharuddin; Hendi Hidayah; Vera Mandailina; Saba Mehmood; Wasim Raza
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 3, August 2026 (Article in Progress)
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i3.2603

Abstract

Multivariable nonlinear equation systems often appear in engineering, physics, economics, and artificial intelligence modeling, but often do not have closed analytical solutions. Therefore, accurate, efficient, and stable numerical methods are needed. This study aims to comparatively evaluate three iterative methods, namely Multivariate Newton-Raphson, Newton-Kantorovich, and Levenberg–Marquardt, in solving identical high-complexity multivariable nonlinear systems. Simulations were performed using MATLAB with an error tolerance of 0.001 and a maximum iteration limit of 100. The test system consisted of a combination of trigonometric, exponential, and polynomial functions, resulting in nonlinear interactions that were challenging for each method. The simulation results show that Levenberg–Marquardt excelled with only 6 iterations and a final error of 3.246 × 10⁻¹⁰, indicating high stability and efficiency, followed by Multivariate Newton-Raphson with 13 iterations and an error of 4.606 × 10⁻⁹, while Newton-Kantorovich requires 27 iterations with an error of 5.770 × 10⁻⁷, reflecting slower semi-local corrections.Three-dimensional visualization shows the intersection point of the surface as a solution, providing an intuitive understanding of the iteration trajectory characteristics of each method. The novelty of this research lies in the integrated numerical simulation framework that allows direct quantitative comparison of the three methods on identical systems with the same initial conditions, tolerance, and iteration limits. These findings provide important empirical references for selecting efficient and stable iterative methods for multivariable nonlinear systems, as well as practical guidance for numerical applications in engineering, physics, and scientific computing.

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