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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.
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Articles 20 Documents
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Search results for , issue "vol. 11, no. 2, may 2026" : 20 Documents clear
Analysis and Classification of Capital Assistance Recipients at the Kediri Trade and Industry Department Using Random Forest Arika Norma Wahyu Dorroty; Ardiawan Bagus Harisa
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.v11i2.2352

Abstract

Capital assistance provided by the Kediri City Department of Trade and Industry often faces challenges related to the uncertainty of fund distribution, making it difficult to ensure the effectiveness of the assistance in improving business revenue. To address this, a prediction-based model is applied to evaluate the factors influencing the success of capital assistance in increasing recipients’ income. This study aims to classify recipients based on business revenue outcomes using the Random Forest algorithm. Furthermore, the model identifies key factors affecting the success of assistance and offers recommendations for optimizing future distribution through feature importance analysis. The results demonstrate that the Random Forest model achieves an accuracy of 75%, highlighting its potential as a reliable tool for predicting the success of capital assistance. The feature importance analysis further reveals that training contributes 49% and business type 43%, emphasizing their crucial role in enhancing the effectiveness of future assistance programs.
Performance Evaluation of Motion Estimation and Compensation Algorithms in SNR Scalable Video Encoding Agus Purwadi Purwadi; Hendra Yufit Riskiawan; Agus Hariyanto; Nugroho Setyo Wibowo; Rani Purbaningtyas
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.v11i2.2466

Abstract

Motion estimation is the sequential determination of the direction of motion of an object in a video. The movement of an object is denoted by the term motion vector. Between the current and reference frames, motion vectors can signify shift points. The SAD (Sum of Absolute Different) block matching technique is fundamentally dependent on the assessment of an object's motion. This study proposes a hybrid approach that integrates the Three-Step Search (TSS) and Full Search (FS) algorithms. This integration aims to design a block matching algorithm that is applied to video encoding using signal-to-noise ratio (SNR) scalability. From this design, the study aims to obtain the performance results and evaluate the motion estimation process using both the TSS and FS algorithms for performance comparison in SNR scalability video encoding, in terms of video frame quality, bit rate, and PSNR, based on the average comparison of the two algorithms. Based on the experimental results, the FS algorithm achieved a total BD-PSNR of 0.22 dB with an efficiency rate of 12.45%, whereas the TSS algorithm achieved a total BD-PSNR of 0.18 dB and an efficiency rate of 7.6%. Therefore, the FS algorithm demonstrates superior performance compared to the proposed TSS algorithm in video transmission with SNR scalability.
Maleo Emotion Audio Dataset Indonesia for Emotion Classification Ardi Mardiana; Sri Mentari Widya Ningrum Permana; Ii Sopiandi; Ade Bastian; Eka Tresna Irawan
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.v11i2.2474

Abstract

The limited availability of voice emotion corpora in Indonesian poses a challenge for the development of Speech Emotion Recognition (SER) systems, despite growing needs in sectors such as customer service and human-computer interaction. To address this, we developed the Maleo Emotion Audio Corpus, a collection of three-second audio clips with seven emotion labels (angry, neutral, disgusted, sad, happy, afraid, and surprised), sourced from YouTube. The audio data underwent preprocessing, feature extraction (MFCC, ZCR, energy, spectral roll-off, and spectral flux), and augmentation. The classification model was built using a 1D Convolutional Neural Network (CNN) architecture specifically adapted for the 3-second audio features, comprising four convolutional layers. Evaluation showed the model achieved 94.48% accuracy on the test data. The claim of balanced performance is supported by high F1-scores across all classes, ranging from 0.87 for 'sad' to 0.98 for 'neutral', indicating no single class dominated the results. These findings demonstrate that the developed corpus and model architecture have strong capability for recognizing emotions from Indonesian speech in a locally relevant context. Maleo Emotion collection is available at https://doi.org/10.57967/hf/6144.
LITE-BoostTrack: A Hybrid Real-Time Multi-Object Tracking Architecture for Resource-Constrained Environments Ruri Suko Basuki; Adhitya Nugraha; Ardytha Luthfiarta; Ika Novita Dewi; Allifian Ilham Febriyana; Michael Surya Adi Prasaja; Dzawil Uqul
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.v11i2.2478

Abstract

Multi-object tracking (MOT) is a fundamental task in computer vision that underpins applications such as intelligent surveillance, autonomous driving, and crowd analysis. The primary challenge in MOT lies in maintaining identity consistency under frequent occlusions while ensuring real-time performance on resource-constrained devices. This study proposes LITE-BoostTrack, a hybrid tracking framework that combines the confidence-based association mechanism of BoostTrack with the lightweight embedding strategy of the Lightweight Integrated Tracking and Embedding (LITE) architecture. The proposed model extracts appearance descriptors directly from the internal feature maps of the YOLOv8 detector, thereby eliminating the need for an external re-identification network. This design significantly reduces computational complexity while preserving reliable identity association. Experiments were conducted on the MOT20 benchmark using standard MOT evaluation metrics, including HOTA, MOTA, IDF1, IDSW, and FPS, to assess both tracking accuracy and runtime efficiency. The results show that LITE-BoostTrack achieves a HOTA of 27.31 and IDF1 of 37.48, outperforming LITE-BoT-SORT (HOTA 25.73, IDF1 33.88), while reducing identity switches by 37% (2,939 vs. 4,674) and maintaining real-time performance at 13.22 FPS. These outcomes demonstrate that substantial efficiency gains can be achieved through detector-level feature integration without introducing additional deep embedding modules. Although occasional failures still occur under severe occlusion, LITE-BoostTrack provides a balanced and practical solution that effectively combines accuracy and efficiency for real-time multi-object tracking in edge-computing and embedded vision systems.
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.

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