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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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An Adaptive Swarm Clustering Algorithm for Game AI Based on Reinforcement Learning Godot and Particle Swarm Optimization (RLGPSO) Trisna Gelar; Iwan Awaludin; Raditya Pasya; Raihan Fuad; Muhammad Rizqi Solahudin
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.v11i2.2480

Abstract

The management of extensive agent swarms presents significant challenges in dynamic, real-time environments, particularly within the context of game artificial intelligence, such as real-time strategy games. Traditional Particle Swarm Optimization (PSO) techniques demonstrate effectiveness in optimization tasks; however, they frequently exhibit suboptimal convergence and insufficient flexibility in complex and challenging scenarios. This study presents a hybrid methodology that combines Reinforcement Learning (RL) and Particle Swarm Optimization (PSO) to develop an adaptive swarm clustering system. This method utilizes a Deep Deterministic Policy Gradient (DDPG) agent operating externally through an API to dynamically adjust Particle Swarm Optimization (PSO) parameters, thereby maintaining a separation between adaptive intelligence and the simulation engine. This allows the swarm to effectively navigate and group within a procedurally generated 2D simulation environment with physical obstacles, unlike previous studies that rely on static mathematical benchmarks. A quantitative analysis employing Mixed Linear Model Regression (MLMR) indicates that this hybrid method significantly outperforms traditional, manually tuned PSO in terms of convergence time and diversity value. The RLGPSO model showed an 11.46% decrease in convergence time on highly complex maps. This result was statistically significant, with a p-value of 0.002 from the MLMR analysis.  This research presents a framework for the deployment of intelligent, self-organizing agent swarms, enhancing the realism and efficacy of contemporary game artificial intelligence.
Modeling and Simulation of a Heat and Airflow Control System in a Fish Smoking Chamber Using K-NN Muhammad Edy Hidayat; Alang Sunding; Umar Muhammad; Irvawansyah
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.v11i2.2492

Abstract

This study presents the modeling and simulation of a heat and airflow control system in a fish smoking chamber using the K-Nearest Neighbors (K-NN) algorithm. Accurate control of temperature and airflow is crucial for ensuring consistent product quality, flavor, texture, and microbial safety in smoked fish. Traditional methods often face challenges in maintaining stable chamber conditions due to nonlinear interactions between heat sources, airflow distribution, and chamber geometry. The research was conducted using a structured methodology consisting of system modeling, K-NN algorithm development, simulation, and performance evaluation. The results demonstrate that the K-NN model achieved optimal performance at k = 5, with an overall prediction accuracy of 92.8%. The Root Mean Square Error (RMSE) was recorded at 1.85 °C for temperature prediction and 0.18 m/s for airflow, confirming the model’s robustness. Compared with conventional approaches, K-NN outperformed Linear Regression and achieved higher accuracy with less complexity than Artificial Neural Networks (ANN). The implications of these findings indicate that predictive modeling enables better process stability, reduces the risk of uneven smoking, and lowers energy consumption. The novelty of this research lies in the dual prediction of heat and airflow, providing a comprehensive framework for smart control in traditional food processing. While the study is limited to simulations, it offers valuable insights for future experimental implementation and integration into intelligent smoking chamber systems.
Comparative Performance Analysis of Nutrient and pH Control in Hydroponic Systems Using Coupled and Decoupled Methods Ina Rahmawati Putri; Bambang Siswojo; Mochammad Rusli
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.v11i2.2504

Abstract

pH and TDS are critical parameters in hydroponic systems that directly influence plant growth. This research develops an automatic control system for nutrient solution and pH regulation in hydroponic cultivation using a PID controller implemented with both coupled and decoupled methods. The aim of the research is to evaluate the performance differences between these two control approaches and to contribute to the development of more accurate and adaptive strategies for maintaining nutrient solution quality. Lettuce plants were used as test subjects with target conditions of 550 ppm TDS and pH 6.5. The research was conducted through MATLAB simulations and hardware implementation to assess system performance. The simulation results indicated that the decoupled method provides superior performance, achieving a pH rise time of 6.04 s, a settling time of 31.24 s, an overshoot of 9.5%, and zero steady-state error. The TDS response exhibits a rise time of 84.97 s, a settling time of 161.67 s, zero overshoot, and zero steady-state error. Hardware implementation demonstrates similar trends, with a pH rise time of 8.34 s, a settling time of 11 s, zero overshoot, and a steady-state error of 0.90%. The TDS response shows a rise time of 30.7 s, a settling time of 36 s, an overshoot of 4.36%, and a steady-state error of 0.60%. In contrast, the coupled method produces slower responses, longer settling times, and higher steady-state errors. Overall, the decoupled method proves to be more effective and responsive in maintaining pH and nutrient stability, demonstrating strong potential for application in smart agriculture systems.
Performance Analysis of Cluster-based Multi-UAV Routing Protocol under Various Mobility Models using NS-3 Harry Darmawan; Prima Kristalina; Moch. Zen Samsono Hadi
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.v11i2.2507

Abstract

In this paper, the performance of a cluster-based multi-UAV communication system is analyzed as a means to enhance network reliability and coordination in support of Search and Rescue (SAR) operations within disaster-affected areas. The proposed approach addresses the challenges of maintaining connectivity, ensuring efficient data transmission, and facilitating effective collaboration among UAVs in critical environments. The system is designed with a four-layer architecture: Base Station (BS), Cluster Head (CH), Clustered Drone (CD), and User Equipment (UE). These layers are modeled and evaluated using Network Simulator 3 (NS-3). Three routing protocols, namely OLSR, AODV, and DSDV are evaluated under three types of UAV mobility models: Gauss-Markov, Random Waypoint (RWP), and Reference Point Group Mobility (RPGM). Quality of Service (QoS) parameters for wireless networks, such as throughput, packet delivery ratio (PDR), delay, and packet loss, are analyzed under several cluster-based UAV scenarios. The simulation results show that the cluster-based multi-UAV model using OLSR routing protocol achieves the best performance under the RPGM mobility model, with an average throughput of 67.57 kbps, 87.47% PDR, 86 ms delay, and 12.53% packet loss, outperforming the other routing protocols. The OLSR routing protocol demonstrates the highest consistency, with higher throughput and PDR values, as well as lower delay and packet loss compared to AODV and DSDV, particularly in small- to medium-scale node densities. This research contributes to the development of UAV-based cluster communication systems, particularly in terms of efficiency, stability, and adaptability to dynamic disaster environments.
An Adaptive Cross-Tied Interconnection for Shaded PV Arrays: A Mathematical Analysis for Efficiency Enhancement Efendi S Wirateruna; Mohammad Jasa Afroni; Wahyu Mulyo Utomo; Mukhammad Zakky Syahrul Aziz
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.v11i2.2529

Abstract

This study investigates the Adaptive Cross-Tied Interconnection (ACTI) configuration to improve the power output efficiency of photovoltaic (PV) arrays operating under partial shading conditions. The objective of this study is to develop a mathematical formulation that describes the behavior of ACTI compared to the conventional Series-Parallel (SP) configuration. Mathematical modeling is used to analyze the current distribution, voltage relationships, and the effect of shading patterns on the total output power. Simulations are performed using MATLAB/Simulink to verify the theoretical analysis results. This adaptive configuration dynamically adjusts the cross-tied interconnections based on the illumination intensity data, thereby balancing the current between shaded and non-shaded modules. The results show that ACTI successfully reduces current mismatch losses and increases output power without increasing circuit complexity. In a 3×3 PV array, the ACTI configuration yields a power increase of up to 48% compared to the SP configuration. In a 5×5 array, the efficiency increases ranges from 2% to 6%, depending on the shading pattern. The adaptive switching strategy maintains the current flow stability and produces a smoother power-voltage curve, enabling faster and more accurate tracking of the global maximum power point. These results demonstrate that ACTI provides an efficient, economical, and mathematically sound solution for improving the performance of PV systems under non-uniform irradiation conditions.
Enhancing CNN Performance for Alzheimer’s Disease Classification through Genetic Algorithm Optimization Wildan Arif Maulana; Zainul Abidin; Rahmadwati Rahmadwati
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.v11i2.2543

Abstract

The rise in global life expectancy has contributed to a rapidly expanding elderly population and a corresponding increase in Alzheimer’s disease cases, highlighting the need for more accurate and objective diagnostic methods. Although MRI is widely used for brain assessment, early-stage Alzheimer’s detection remains challenging because structural differences between disease stages are often subtle and prone to subjective interpretation by clinicians. To address this limitation, this study proposes a custom Convolutional Neural Network (CNN) developed from scratch for classifying Alzheimer’s disease using brain MRI images. Data diversity was enhanced through augmentation comparison strategies, including Albumentations, which achieved 84.8% accuracy; CutMix, which achieved 88.3% accuracy, and a combined Albumentations-CutMix approach, which enabled the base model to achieve 92.1% classification accuracy. Subsequently, a Genetic Algorithm (GA) was applied to optimize key hyperparameters, enabling efficient exploration of the solution space compared to manual tuning and improving model performance to 96.4% accuracy. The optimized model demonstrated improved stability and generalization across all classes, highlighting the capability of the proposed computational framework to function as a reliable tool for supporting the early detection of Alzheimer-related cognitive decline.
Identification of BSR Disease in Oil Palm Using UAV Imagery through CNN and SCNN Approaches Zakia Azzahro; Rahmadwati; Angger Abdul Razak; Amrul Faruq
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.v11i2.2546

Abstract

Basal Stem Rot (BSR) disease caused by Ganoderma boninense is a major threat to oil palm productivity due to its destructive nature and the challenges associated with early-stage detection. To support sustainable production and mitigate significant yield losses, a system capable of classifying oil palm trees into healthy and infected categories is required. In this study, two deep learning approaches, namely CNN and SCNN, are applied to identify oil palm conditions based on UAV-derived imagery. While CNN is widely used for image-based detection tasks due to its capability to extract relevant visual representations, it is prone to overfitting during training. Therefore, SCNN is employed to address this issue by leveraging image similarity comparison mechanisms. Experimental results show that both methods achieve high classification accuracy, with SCNN outperforming CNN by achieving an accuracy of 96.48% compared to 95.644% for CNN. The superior performance of SCNN indicates its sensitivity to subtle visual differences between healthy and early-stage infected oil palm trees, enabling more reliable classification performance. Thus, SCNN is considered more effective for oil palm condition detection and contributes to reducing overfitting, resulting in improved model stability.
Revealing Stunting Risk Patterns through Comparative Analysis of Hierarchical and Deep Embedded Clustering Fifin Ayu Mufarroha; Abdullah Basuki Rahmat; Husni; Aeri Rachmad; Vivin Ayu Lestari; Tasya Dwiyanti; Malik Maulana
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.v11i2.2555

Abstract

Stunting remains a significant public health issue in Indonesia due to its long-term impact on human resource quality and economic productivity. Despite various intervention programs, disparities in stunting prevalence across regions remain high, particularly in areas characterized by diverse socioeconomic conditions. This study aims to identify regional patterns and group areas based on stunting risk levels using two machine learning approaches: Hierarchical Clustering (HC) and Deep Embedded Clustering (DEC). The data used in this study consist of aggregated toddler measurement data, including the number of toddlers measured, the number of stunting cases, and the percentage of stunting during the 2020–2024 period. The analysis was conducted by comparing the clustering results generated by both methods. The HC method was implemented using the Agglomerative Clustering approach with the Ward linkage criterion, while DEC employed a layered autoencoder architecture optimized using Kullback–Leibler divergence. Cluster quality was evaluated using the Silhouette Score metric. The results show that HC achieved the highest Silhouette Score of 0.5430, while DEC achieved 0.4874, with both methods exhibiting year-to-year performance variation. These findings indicate that HC provides better clustering stability, whereas DEC demonstrates greater adaptability to data complexity and nonlinear patterns. The integration of both methods offers a comprehensive big data–driven health analytics framework, representing an innovative approach for evidence-based decision-making in identifying and addressing stunting-prone regions.
An LSTM-SARIMA Based Forecasting Method for Environmental Quality in Enclosed Poultry House Genta Garuda Bimasakti; Anisja Noni Kartikasari; Hapsari Peni Agustin Tjahyaningtijas
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.v11i2.2557

Abstract

Closed-type poultry houses support stable production performance by maintaining a controlled microenvironment that promotes optimal poultry growth. However, many farms still rely on manual monitoring of environmental parameters such as temperature, humidity, and ammonia concentration, resulting in delayed responses, reduced productivity, and increased environmental stress on poultry. These limitations highlight the need for predictive and automated systems that can monitor and forecast environmental conditions in real time. Previous studies have shown that LSTM networks are effective for nonlinear time-series forecasting. However, when applied independently, LSTM models often face difficulties in capturing linear seasonal patterns and long-term trends inherent in poultry house environmental data. Therefore, this study proposes a hybrid forecasting framework that integrates LSTM and SARIMA models to simultaneously capture nonlinear temporal dependencies and linear seasonal components. Environmental parameters, including temperature, litter moisture, and ammonia concentration, were collected using SHT31, Soil Moisture, and MQ137 sensors. The collected data were processed using a Python-Flask backend system, stored in MongoDB, and visualized through a cross-platform web interface developed using Flutter. Experimental results demonstrate that the proposed LSTM–SARIMA model achieves strong predictive performance, with MAE = 0.62, MSE = 0.55, RMSE = 0.58, MAPE = 7.89%, and R² = 0.86. These findings indicate that the proposed method effectively supports early warning systems and real-time microclimate monitoring, enabling faster environmental control responses and reducing production losses caused by unstable poultry house conditions.
Parameter-Efficient Models for Malaria Detection and Classification Using Small-Scale Imbalanced Blood Smear Images Akhiyar Waladi; Hasanatul Iftitah; Nindy Raisa Hanum; Yogi Perdana; Fitra Wahyuni; Rahmad Ashar
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.v11i2.2558

Abstract

Malaria diagnostic automation faces critical challenges, including severe class imbalance with ratios of up to 54:1, limited datasets containing 200 to 500 images, and computational inefficiency resulting from the need to train separate models for each detection-classification combination. This study developed a multi-model framework with a shared classification architecture that trains classification models once on ground-truth crops and reuses them across all detectors. The framework systematically evaluated three YOLO Medium architectures for parasite detection and six CNN architectures for lifecycle and species classification across four complementary malaria datasets totaling 1,544 microscopy images. Detection achieved mAP@50 scores ranging from 70.84% to 96.27%, with high recall values of 71.05% to 93.12% minimizing missed parasite detections. Classification results demonstrated the importance of dataset-dependent model selection, with parameter-efficient EfficientNet models containing 5.3M to 9.2M parameters consistently outperforming ResNet variants with up to 44.5M parameters. EfficientNet-B1 achieved accuracies of 91.51% on the IML Lifecycle dataset and 98.28% on the MP-IDB Species dataset, while EfficientNet-B0 achieved 86.45% on the multi-patient MD-2019 dataset. ResNet50 achieved 96.13% accuracy on severely imbalanced MP-IDB Stages dataset. Focal Loss optimization with alpha = 1.0 and gamma = 1.5 enabled robust minority-class performance, achieving F1-scores between 0.44 and 1.00 on ultra-minority classes and demonstrating effective handling of class imbalance. The compact models, with sizes ranging from 46 MB to 89 MB, enable practical deployment on resource-constrained hardware.

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