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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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Federated Learning and Deep Reinforcement Learning Synergy: Opportunities for Multi-Cloud Serverless Deployment I Gusti Ngurah Wikranta Arsa Arsa; Arief Setyanto; Andi Sunyoto; Alva Hendi Muhammad
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.v11i3.2694

Abstract

The advancement of distributed computing has enabled the use of multi-cloud and serverless computing, which are beneficial due to their flexibility, scalability, and cost efficiency. There are, of course, pertinent challenges associated with these computing paradigms, such as resource heterogeneity, cold-start latency, vendor lock-in, and privacy. Recent trends in Federated Learning (FL) and Deep Reinforcement Learning (DRL) offer promise in tackling these challenges. FL systems enable decentralised, privacy-preserving model training across heterogeneous systems, while DRL systems enable adaptive models for real-time decision-making to optimise system resources and improve performance. This Systematic Literature Review (SLR) covers the years 2020 to early 2026 and examines the intersection of FL and DRL in multi-cloud serverless computing, following the PRISMA methodology. A primary analysis of 50 quality studies was undertaken to answer four privacy-related resource management questions. The results demonstrated FL increases privacy and scalability utilizing decentralised training. Consolidating the Federated DRL and Multi-Agent stacks increases the system by obtaining a better trade-off and optimization among latency, energy, and operational efficiency. However, a few gaps still exist, such as the absence of a more holistic framework, elusiveness in cross-system integration and collaboration, and a lack of concrete real-world applications. More work is needed to build a cohesive Federated Learning framework to improve sustainability and security in the multi-cloud, serverless systems of the future. Beyond a systematic literature review, this study provides a comparative synthesis of FL–DRL-based approaches and proposes a conceptual orchestration framework as a foundation for intelligent resource allocation in serverless multicloud environments.
Dota 2 Hero Buff And Nerf Predictions Based On Professional Match Data Using Random Forest Muhammad Raditya Azanata; Muhamad Azrino Gustalika; Dimas Fanny Hebrasianto Permadi
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.v11i3.2698

Abstract

Balancing updates (buffs and nerfs) are essential in Multiplayer Online Battle Arena games because minor parameter changes can shift the competitive metagame and reduce hero diversity. Unlike previous studies on Dota 2 that focus on match outcome prediction, this study introduces and evaluates hero-centric balance recommendations against official patch actions across patch transitions. To address this gap, this work contributes a patch-to-patch (Version 7.39-7.40b) external validation protocol that compares recommendations from patch t with developer actions in patch t+1 using patch notes. Professional match records were gathered from public sources and sorted by hero and patch into combat, economic, and impact categories. This study proposed a data-driven pipeline to classify each Dota 2 hero as overpowered, underpowered, or balanced from professional match telemetry and to translate these classes into balance recommendations (Nerf, Buff, or Balance). Labels derive from win-rate and pick-rate distributions using statistical control limits (μ ± kσ, k = 0.3) to ensure transparent, repeatable labeling. A Random Forest classifier was trained using grid-searched hyperparameters and evaluated using stratified 6-fold cross-validation with macro-averaged F1 to address class imbalance. Internal evaluation achieved 0.94 accuracy and 0.84 macro-F1. For external validation, patch t recommendations were compared to official balance actions in patch t+1 during six successive transitions; accuracy ranged from 0.436 to 0.672 (mean 0.559), with the best result on 7.39b to 7.39c (84/125). These results indicated that professional telemetry could support interpretable balance monitoring and provide early signals for buff/nerf candidate review.
A Gradient Boosting–Based Platform with Fuzzy Linguistic Representation for Cardiovascular Disease Risk Prediction Amir Saleh; Fadhillah Azmi
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.v11i3.2699

Abstract

Cardiovascular disease (CVD) remains one of the leading causes of mortality globally, thus early risk prediction critical for preventative healthcare. Although machine learning methods have shown promising results in CVD prediction, many existing models are difficult to interpret in clinical practice. This study proposes a CVD risk prediction platform that combines gradient boosting (GB) with fuzzy linguistic representation to improve both predictive performance and interpretability. approach has numerous preprocessing phases, including data cleaning, normalization, outlier handling, and recursive feature elimination (RFE) for feature selection. Numerical clinical attributes are transformed into fuzzy linguistic variables to provide more intuitive risk interpretation for healthcare professionals. The gradient boosting model is trained using both original and fuzzy-transformed feature representations to improve model generalization. The proposed approach is evaluated against several baseline machine learning models using accuracy, precision, recall, and F1-score. Experimental results show that the proposed framework achieves better performance than conventional models, with a maximum accuracy of 94.30%. In addition, the developed platform provides visual risk interpretation and decision-support insights that may assist healthcare practitioners in evaluating cardiovascular risk more effectively.
A Memory-Efficient and Gradient-Stable Lightweight ANFIS for Real-Time Humidity Prediction in Precision Agriculture Eddy Nurraharjo; Ema Utami; Kusrini; Kumara Ari Yuana
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.v11i3.2700

Abstract

Precision agriculture requires artificial intelligence solutions that are both accurate and deployable on resource-constrained hardware. however, conventional machine learning models require excessive memory while traditional ANFIS architectures suffer from training instability. This study developed a memory-efficient and gradient-stable lightweight Adaptive Neuro-Fuzzy Inference System (ANFIS) for real-time humidity prediction on microcontroller-class devices. The proposed architecture strategically reduced the rule base from 27 to only 4 interpretable fuzzy rules and limited membership functions to two per input, achieving an 85.2% reduction in learnable parameters. A gradient-stable training mechanism was introduced, combining physics-informed parameter initialization with adaptive gradient clipping to prevent gradient explosion. The model was trained and validated using 31,474 real-world greenhouse samples collected over 218 days, with 80% allocated for training and 20% for temporal testing. Experimental results demonstrated that the gradient-stable architecture successfully converged from a catastrophic R² of -64.08 to 0.9148, with a root mean square error of 1.32% and mean absolute error of 1.05%. The model required only 0.211 KB of memory, representing a 99.9% reduction compared to baseline Random Forest models, while achieving inference time of 8.2 milliseconds on Arduino UNO. The system was successfully deployed on three independent hardware modules, maintaining consistent performance with average RMSE of 1.99% over 168 hours of continuous operation. This study concludes that strategic simplification and stability-aware training enable interpretable neuro-fuzzy systems to operate effectively on ultra-low-resource devices, bridging the gap between predictive accuracy and hardware feasibility in embedded agricultural IoT applications.
ECG Signal-based Classification of Physiological States in ASD Children DWT and Machine Learning Muhammad Irhamsyah; Hanum Aulia; Yunidar Yunidar; Melinda Melinda; Muhsin Muhsin; Syarifah Rauzatul Jannah
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.v11i3.2750

Abstract

ASD is a neurodevelopmental disorder that affects a child's ability to regulate emotions, interact socially, and respond to environmental stimuli. Monitoring physiological conditions in children with ASD is challenging because it relies on subjective observations. In this study, physiological conditions are defined based on observable behavioral states, namely active and quiet. The active state reflects increased physiological arousal and a higher heart rate, while the quiet state represents a resting state with a more stable heart rate. This study proposes an ECG-based classification system to distinguish between these two states. The dataset consists of 2000 samples for each class. Due to noise in the ECG signal caused by body movements, preprocessing was performed using the DWT to improve signal quality. The processed signals were then classified using three machine learning algorithms: SVM, Random Forest, and AdaBoost. The performance of each model was evaluated using accuracy, precision, recall, and F1-score. The results showed that without DWT, Random Forest achieved the highest accuracy of 91.00%, followed by SVM at 88.87%, and AdaBoost at 87.25%. While using DWT, Random Forest achieved the highest accuracy of 93.75%, followed by SVM at 91.37%, and AdaBoost at 90.25%. This indicates that DWT can produce better signal quality. Furthermore, Random Forest was selected as the optimal model and implemented in a Streamlit-based web application for real-time monitoring. These findings indicate that the combination of DWT and Random Forest is effective for classifying physiological conditions in children with ASD and has potential as an objective monitoring tool.
Mathematical Modeling of an Adaptive Lighting System Based on Solar Panels and Digital Communication for Microalgae Synthesis I Gede Suputra Widharma; I Gde Nyoman Sangka; I Gde Ketut Sri Budarsa
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.v11i1.2435

Abstract

Microalgae are promising photosynthetic microorganisms widely used in biofuel, pharmaceutical, and environmental applications. Their cultivation efficiency is highly influenced by light intensity, temperature, and pH. This study presents a mathematical model of an adaptive lighting system powered by solar energy and controlled through digital communication for sustainable microalgae synthesis. The system dynamically regulates LED illumination using real-time environmental feedback from temperature and pH sensors integrated into an IoT network. The model combines first-order ordinary differential equations (ODEs) to describe solar input, LED power consumption, environmental response, and communication delay. Numerical simulations performed in MATLAB show that the adaptive control algorithm maintains optimal illumination while minimizing unnecessary energy use. Compared to conventional static lighting, the proposed model achieves a 35% reduction in energy consumption and improved environmental stability despite communication latency. The study provides a foundational framework for developing intelligent, energy-efficient photobioreactor systems that align with the Sustainable Development Goals (SDG 7 and SDG 13). Future work may extend the model toward real-time, predictive, and machine-learning-based control for field-scale implementation.
Integrating Meteorological and PV Data for Short-Term Solar Irradiance Forecasting Using BPNN Ahmad Rizal Agustian; Unit Three Kartini; Muhammad Miftahul Rizqi; Sa'adatud Daroini
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.v11i1.2449

Abstract

Solar power plants are highly dependent on solar radiation intensity, which fluctuates due to changes in atmospheric conditions. To maintain system stability and efficiency, an accurate short-term solar radiation prediction model is essential. This study developed a model for forecasting global solar radiation one hour ahead using the Backpropagation Neural Network (BPNN) method. The dataset was obtained from a photovoltaic (PV) system at Building A8 of Surabaya State University, recorded over four days (June 14-17, 2025) at two-minute intervals. Five input variables were used: clearness index, solar radiation, air temperature, air humidity, and PV output power, resulting in a total of 3,020 data samples. The model was trained through a trial-and-error process by varying the number of neurons, hidden layers, and epochs to determine the optimal configuration. The forecast capability of the model was assessed through four statistical indicators: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R²). The best performance was achieved with a network architecture of 15 input neurons representing input variables resulting from data transformation using the sliding window method, one hidden with 25 neurons, and a single unit in the output layer trained for 2000 epochs, resulting in R2 = 0.98, MAPE = 5.89%, and MSE = 0.00027. The novelty of this research lies in the integration of meteorological data with actual PV power output as model input, enabling the network to capture more realistic nonlinear temporal relationships. The proposed short-term forecasting model provides a practical approach to predicting solar radiation based on historical data and can support efficient energy management and photovoltaic system performance analysis.
ANFIS-Controlled High Step Up DC DC Converter for Fuel Cell Systems with Enhanced Efficiency Against Load Variation Harmini Harmini; Mochamad Ashari; Feby Agung Pamuji
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.v11i1.2456

Abstract

The primary challenge in utilizing Fuel Cell (FC) systems lies in their inherently low and fluctuating output voltage, which contrasts with the requirements of a Direct Current (DC) bus network that demands a stable and relatively high voltage level. Ensuring consistent voltage regulation in the DC bus network is essential for reliable system performance. An interface converter is required to elevate and stabilize the voltage output under dynamic operating conditions. This paper introduces a high step-up DC–DC converter integrated with an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based control scheme for enhancing the performance of FC power systems. The proposed work encompasses the modeling, analytical design, and structural development of the converter and its intelligent control mechanism. The proposed high step-up converter exhibits a novel structural configuration that integrates a clamp unit, a Multiplier Cell (MC), and cascaded Quadratic Boost Converter (QBC) stages. The contribution of this converter topology lies in its ability to enhance the reliability of fuel cell–based renewable energy systems, achieve high voltage amplification, ensure optimal efficiency, and maintain dynamic stability. This topology is specifically developed to attain an ultra-high voltage conversion ratio, achieving a significant voltage gain of up to 9.65 times, thereby effectively increasing the input voltage from 45 V to 400 V. The ANFIS controller effectively maintains a stable output voltage of 400 V with a maximum deviation of only ±3.5%. The proposed converter achieves a peak efficiency of 87% under varying load conditions, demonstrating its suitability for fuel cell-based energy systems.
Speed Synchronization of Multi-Conveyor System Using Bidirectional Interaction Topologies Bima Sakti Putra Ardi; Agung Prayitno; Veronica Indrawati
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.v11i1.2459

Abstract

Long production lines composed of multiple stand-alone mode controllers often face challenges when speed synchronization is required, as each setpoint must be manually adjusted one by one. The issue can be addressed by designating one conveyor as the leader, while the others operate as followers that continuously adjust their speeds to match the leader. The main objective of this study is to develop a multi-conveyor leader–follower system based on distributed cooperative control, allowing all follower conveyors to maintain synchronized speeds with the designated leader unit. In this setup, the leader is equipped with the ability to command all followers to align their speeds to its own, which is governed by a fuzzy logic controller (FLC). Each follower operates in one of two modes: a stand-alone FLC mode or a synchronization mode using cooperative control. The cooperative control mechanism relies on speed information shared among neighboring conveyors, as defined by the system topology. Two types of bidirectional interaction topologies are explored in this work: The Bidirectional Coordinated Conveyor Topology (BCCT) and the Bidirectional Leader Coordinated Conveyor Topology (BLCCT). The proposed control strategy was tested on a mini multi-conveyor setup with one leader and four followers. Synchronization tests on two topologies produced RMSE values of 30.88 RPM for BCCT and 43.87 RPM for BLCCT. A brief disturbance was also applied to one follower to assess the controller’s resilience and its effect on overall system coordination. The study confirms that combining fuzzy logic with cooperative control enhances synchronization and coordination across conveyors.
Leveraging Green IoT to Enhance Energy-Saving Efficiency in Fairness-Oriented Residential Photovoltaic Charging Stations Syarifah Muthia Putri; Moranain Mungkin; Harmini; Syechu Dwitya Nugraha
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.v11i1.2470

Abstract

As electric vehicles (EVs) continue to gain global popularity, residential photovoltaic (PV) charging stations are becoming more common, providing a sustainable way to charge EVs. However, the intermittent nature of solar energy creates challenges in ensuring consistent and fair charging, making fairness-based charging scheduling essential. To automate this process, residential PV charging stations require a customized Internet of Things (IoT) system. A significant concern is the substantial energy consumption due to the high volume of data transmission within the IoT system. This research aims to enhance energy efficiency by leveraging green IoT strategies suitable for such applications. The study proposes the use of edge computing, optimized data transmission scheduling, and delta compression techniques at the edge to minimize energy use. The results demonstrate that these strategies are effective in achieving energy savings. Energy-saving efficiency on the source side ranges from 1.96% to 7.84%, while on the load side, it ranges from 57.5% to 61.3%. These findings highlight the effectiveness of the proposed strategies in reducing energy consumption, providing an efficient solution for optimizing data transmission in residential PV charging stations. Overall, the strategies contribute to the sustainable operation of electric vehicle charging infrastructure by improving energy efficiency and ensuring fair distribution of charging resources.

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