cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota malang,
Jawa timur
INDONESIA
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 555 Documents
 52   53   54   55   56 
An Adaptive Swarm Clustering Algorithm for Game AI Based on Reinforcement Learning Godot and Particle Swarm Optimization (RLGPSO) Gelar, Trisna; Awaludin, Iwan; Pasya, Raditya; Fuad, Raihan; Solahudin, Muhammad Rizqi
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 2, May 2026 (Article in Progress)
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i2.2480

Abstract

Managing extensive agent swarms presents a significant difficulty in dynamic, real-time situations, especially in gaming artificial intelligence, such as real-time strategy. Traditional Particle Swarm Optimization (PSO) techniques, while effective for optimization tasks, often exhibit suboptimal convergence and inadequate flexibility in complex, demanding situations. This study introduces an innovative hybrid approach that integrates Reinforcement Learning (RL) with PSO to create an adaptive swarm clustering system. This approach employs a Deep Deterministic Policy Gradient (DDPG) agent to dynamically modify PSO parameters, enabling the swarm to adeptly maneuver and cluster within a procedurally generated 2D simulation environment featuring physical obstacles, in contrast to earlier studies that depend on static mathematical benchmarks. A rigorous quantitative analysis using Mixed Linear Model Regression (MLMR) demonstrates that this hybrid method significantly and statistically outperforms conventional, manually tuned PSO in terms of convergence time and diversity value. For example, the RLGPSO model achieved an 11.46% reduction in convergence time on high-complexity maps, a result confirmed as statistically significant with a p-value of 0.002 from the MLMR analysis. This study offers a pragmatic approach for the implementation of intelligent, self-organizing agent swarms, directly applicable to improving the realism and efficacy of present-day gaming AI.
Modeling and Simulation of Heat and Airflow Control System in Fish Smoking Chamber using K-NN Muhammad Edy Hidayat; Sunding, Alang; Muhammad, Umar; Irvawansyah
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 2, May 2026 (Article in Progress)
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 through a structured methodology consisting of system modeling, K-NN algorithm development, simulation, and performance evaluation. The results show and 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 show 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.
Comparison of Nutrient and pH Control in NFT Hydroponic Plants for Coupled and Decoupled Methods Putri, Ina Rahmawati; Siswojo, Bambang; Rusli, Mochammad
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 2, May 2026 (Article in Progress)
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i2.2504

Abstract

PH and TDS were critical parameters in hydroponic systems that directly influenced nutrient absorption and plant growth. This study developed an automatic nutrient solution control system for NFT hydroponics using a Proportional-Integral-Derivative (PID) controller with coupled and decoupled approaches. The system employed a DFRobot Gravity: Analog TDS sensor to measure TDS, an Electrode Probe pH-4502C to monitor pH, and an Arduino Uno microcontroller to regulate peristaltic pumps in real time. Lettuce was used as the test crop, requiring 550 ppm TDS and pH 6.5. System performance was evaluated through MATLAB Simulink simulations and hardware implementation based on rise time, settling time, overshoot, and steady-state error. The simulation results showed that the coupled method had slightly faster rise time and settling time compared to the decoupled method, whereas the decoupled method had less overshoot than the coupled. The hardware test showed that the decoupled method performed better, with a pH rise time of 8.34 s, a settling time of 11 s, an overshoot of 10%, and a steady-state error of 0.90%, as well as a TDS 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 exhibited slower responses, longer settling times, and higher steady-state errors. Overall, the decoupled method proved more effective and reliable in maintaining pH and TDS stability, showing strong potential to enhance the efficiency and robustness of NFT hydroponic control systems.
Performance Analysis of Cluster-based Multi-UAV Routing Protocol in Various Mobility Models using NS-3 Darmawan, Harry; Kristalina, Prima; Samsono Hadi, Moch. Zen
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 2, May 2026 (Article in Progress)
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 area. The proposed approach is intended to address the challenges of maintaining connectivity, ensuring efficient data transmission, and facilitating effective collaboration among UAVs in critical environments. The system is designed with four layers 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). The three routing protocols, i.e OLSR, AODV, and DSDV have been evaluated through the three types of UAV mobility: Gauss-Markov, Random Waypoint (RWP). and Reference Point Group Mobility (RPGM). Quality of service parameter for wireless network, such as throughput, packed delivery ratio (PDR), delay, and packet loss has been analysed within several cluster-based UAV schemes. The simulation result shows that cluster-based multi-UAV model with OLSR routing protocol outperforms the best performance in RPGM mobility model with 67.57% average throughput, 87.47% PDR, 86 ms delay, and 12.53% packet loss, better than other routing protocols. OLSR routing protocol indicates the highest consistency with higher throughput and PDR value, smaller delay and packet loss comparing to AODV and DSDV protocols in the small to middle scale of node density. This research contributes in the development of UAV based cluster communication system, especially in efficiency, stabilization and adaptation towards the dynamic environment in the disaster area.
An Adaptive Cross-Tied Interconnection for Shaded PV Arrays: A Mathematical Analysis for Efficiency Enhancement Wirateruna, Efendi S; Afroni, Mohammad Jasa; Utomo, Wahyu Mulyo; Aziz, Mukhammad Zakky Syahrul
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 2, May 2026 (Article in Progress)
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 shade 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 relationship, 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 based on the illumination intensity data, thus balancing the current between the shaded and normal modules. The results show that ACTI successfully reduces current mismatch losses and increases the output power without increasing circuit complexity. In a 3x3 PV array, the ACTI configuration yields a power increase of up to 48% compared to the SP configuration. In a 5x5 array, the efficiency increase 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, allowing 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 Using Genetic Algorithm Optimization Maulana, Wildan Arif; Abidin, Zainul; Rahmadwati, Rahmadwati
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 2, May 2026 (Article in Progress)
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i2.2543

Abstract

The increasing global life expectancy has led to a rapidly growing elderly population, resulting in a higher prevalence of Alzheimer’s disease and a pressing need for effective diagnostic solutions. Despite advances in medical imaging, the early and accurate detection of Alzheimer’s disease remains a major challenge due to subtle differences in brain structures across disease stages. However, the interpretation of MRI images still depends heavily on the abilities of individual medical personnel, which risks introducing subjectivity and potential errors in the diagnostic process. In this context, particularly deep learning, emerges as an effective strategy to overcome these limitations by automating the analysis of medical images and reducing human bias. To address this issue, a custom Convolutional Neural Network (CNN) model was developed from scratch for Alzheimer’s disease classification using brain MRI images. To enhance data diversity and mitigate overfitting, a combination of Albumentations and CutMix data augmentation techniques was applied, yielding an initial classification accuracy of 90%. Model performance was further optimized using a Genetic Algorithm (GA), which efficiently explored the hyperparameter space and identified optimal configurations, boosting classification accuracy to 96%. The optimized model demonstrated robust generalization across all disease categories, confirming the effectiveness of the proposed approach. This research contributes to the development of a more reliable and adaptive deep learning framework for early-stage Alzheimer’s disease detection, offering potential support for clinical diagnostic systems
Identification of BSR Disease in Oil Palm from UAV Imagery Using CNN and SCNN Approaches Azzahro, Zakia; Rahmadwati; Angger Abdul Razak; Amrul Faruq
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 2, May 2026 (Article in Progress)
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 identifying tree conditions into healthy and infected categories is required. In this study, two deep learning approaches, 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 ability 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. Experimental results show that both methods achieve high accuracy, with SCNN outperforming CNN by achieving an accuracy of 96.48%, compared to 95.644%. The superior performance of SCNN demonstrates its sensitivity to subtle visual differences between healthy and early-stage infected trees, enabling more reliable models. Thus, SCNN is considered more optimal for detection oil palm conditions and contributes to reducing overfitting, resulting in improved model stability.
Revealing Stunting Risk Patterns through Comparative Analysis of Hierarchical and Deep Embedded Clustering Mufarroha, Fifin Ayu; Rahmat, Abdullah Basuki; Husni; Rachmad, Aeri; Lestari, Vivin Ayu; Dwiyanti, Tasya; Maulana, Malik
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 2, May 2026 (Article in Progress)
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 health issue in Indonesia due to its long-term impact on human resource quality and economic productivity. Despite various intervention programs, disparities in stunting rates between regions remain high, particularly in areas with diverse socioeconomic conditions. This study aims to identify patterns and group regions based on stunting risk levels using two machine learning approaches: Hierarchical Clustering (HC) and Deep Embedded Clustering (DEC). The data used are aggregated data from toddler measurements, including the number of toddlers measured, the number of stunting cases, and the percentage of stunting in the 2020–2024 period. The analysis was conducted by comparing the cluster results from the two methods. The HC method is implemented using an Agglomerative Clustering approach with the Ward linkage criterion, while DEC uses a layered autoencoder architecture optimized through Kullback–Leibler divergence. To assess cluster quality, the study uses the Silhouette Score metric. The results showed that HC produced the highest Silhouette score of 0.5430, while DEC reached 0.4874, with a year-on-year performance trend. These findings indicate that HC excels in clustering stability, while DEC is more adaptive to data complexity and nonlinear patterns. The combination of the two has the potential to support the formulation of more comprehensive, data-driven policies to identify and address stunting-prone areas.
LSTM-SARIMA Based Prediction Method for Environmental Quality in Enclosed Poultry House Bimasakti, Genta Garuda; Kartikasari, Anisja Noni; Tjahyaningtijas, Hapsari Peni Agustin
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 2, May 2026 (Article in Progress)
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 facilitate consistent output by ensuring a steady microenvironment conducive to optimal avian growth. Nevertheless, numerous farms continue to depend on manual oversight of temperature, humidity, and ammonia levels, resulting in delayed reactions, diminished productivity, and heightened environmental stress on poultry. These constraints underscore the necessity for predictive and automated systems that can monitor and forecast environmental variables in real time. Prior research indicates that LSTM networks are proficient in nonlinear time-series forecasting nonetheless, when used in isolation, LSTM models encounter difficulties in capturing linear seasonal patterns and long-term trends present in chicken house environmental data. This research presents a hybrid forecasting framework that combines LSTM and SARIMA models to concurrently represent nonlinear temporal dependencies and linear seasonal components. Environmental metrics such as temperature, soil moisture, and ammonia concentration were acquired using SHT31, Soil Moisture, and MQ137 sensors, processed using a Python-Flask backend, saved in MongoDB, and visualized through a cross-platform Flutter-based web interface. Experimental findings indicate that the proposed LSTM–SARIMA model exhibits robust predictive efficacy, with MAE = 0.62, MSE = 0.55, RMSE = 0.58, MAPE = 7.89%, and R² = 0.86. The findings demonstrate that the suggested method efficiently facilitates early warning systems and real-time microclimate evaluation, allowing for expedited environmental management measures and minimizing production losses due to unstable poultry house conditions.
Parameter Efficient Models for Malaria Detection and Classification Using Small-Scale Imbalanced Blood Smear Images Waladi, Akhiyar; Iftitah, Hasanatul; Hanum, Nindy Raisa; Perdana, Yogi; Wahyuni, Fitra; Ashar, Rahmad
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 2, May 2026 (Article in Progress)
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i2.2558

Abstract

Malaria diagnostic automation faced critical challenges including severe class imbalance with ratios up to 54:1, limited datasets with 200 to 500 images, and computational inefficiency requiring separate model training for each detection-classification combination. This study developed a multi-model framework with shared classification architecture that trained classification models once on ground truth crops and reused 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 70.84% to 96.27% mAP@50 with high recall of 71.05% to 93.12% minimizing missed parasites. Classification demonstrated 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 91.51% accuracy on IML Lifecycle and 98.28% on MP-IDB Species, while EfficientNet-B0 achieved 86.45% on multi-patient MD-2019 dataset. ResNet50 achieved 96.13% on severely imbalanced MP-IDB Stages. Focal Loss optimization with alpha of 1.0 and gamma of 1.5 enabled robust minority class performance with F1-scores between 0.44 and 1.00 on ultra-minority classes demonstrating effective imbalance handling. The compact 46-89 MB models enabled practical deployment on resource-constrained hardware.

Page 55 of 56 | Total Record : 555

 52   53   54   55   56 

Filter by Year

2016 2026


Reset
Filter By Issues
All Issue Vol. 11, No. 2, May 2026 (Article in Progress) Vol. 11, No. 1, February 2026 Vol. 10, No. 4, November 2025 Vol. 10, No. 3, August 2025 Vol. 10, No. 2, May 2025 Vol. 10, No. 1, February 2025 Vol. 9, No. 4, November 2024 Vol. 9, No. 3, August 2024 Vol. 9, No. 2, May 2024 Vol. 9, No. 1, February 2024 Vol. 8, No. 4, November 2023 Vol. 8, No. 3, August 2023 Vol. 8, No. 2, May 2023 Vol. 8, No. 1, February 2023 Vol. 7, No. 4, November 2022 Vol. 7, No. 3, August 2022 Vol. 7, No. 2, May 2022 Vol. 7, No. 1, February 2022 Vol. 6, No. 4, November 2021 Vo. 6, No. 3, August 2021 Vol. 6, No. 2, May 2021 Vol. 6, No. 1, February 2021 Vol. 5, No. 4, November 2020 Vol. 5, No. 3, August 2020 Vol. 5, No. 2, May 2020 Vol. 5, No. 1, February 2020 Vol 4, No 4, November 2019 Vol 4, No 3, August 2019 Vol 4, No 2, May 2019 Vol 4, No 2, May 2019 Vol 4, No 1, February 2019 Vol 4, No 1, February 2019 Vol 3, No 4, November 2018 Vol 3, No 4, November 2018 Vol 3, No 3, August 2018 Vol 3, No 3, August 2018 Vol 3, No 2, May-2018 Vol 3, No 2, May-2018 Vol 3, No 1, February-2018 Vol 3, No 1, February-2018 Vol 2, No 4, November-2017 Vol 2, No 4, November-2017 Vol 2, No 3, August-2017 Vol 2, No 3, August-2017 Vol 2, No 2, May-2017 Vol 2, No 2, May-2017 Vol 2, No 1, February-2017 Vol 2, No 1, February-2017 Vol 1, No 3, November-2016 Vol 1, No 3, November-2016 Vol 1, No 2, August-2016 Vol 1, No 2, August-2016 Vol 1, No 1, May-2016 Vol 1, No 1, May-2016 More Issue