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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 536 Documents
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Evaluation of Traffic Distribution Performance of ECMP and PCC+CAKE for Multi-ISP Load Balancing on Real Networks Based Using Mikrotik Fathurrohim, Moh; Basuki, Achmad
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 4, November 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i4.2374

Abstract

Imbalance in bandwidth utilization among Internet Service Providers (ISPs) is a major challenge in network management within educational institutions, especially when differences in ISP capacity cause overload on one main path. To address this issue, this study proposes the application of load balancing methods using Equal-Cost Multi-Path (ECMP) and Per-Connection Classifier (PCC) optimized with the CAKE queue type. The implementation is carried out using MikroTik devices, which support the flexible configuration of both methods. Testing is conducted on a real network using a combination of passive monitoring approach—through the analysis of actual traffic and ISP utilization—and active monitoring. The evaluation results show that the ECMP method still produces an uneven traffic distribution, with a tendency to concentrate the load on one path. In contrast, PCC+CAKE is able to distribute traffic more evenly according to the ISP bandwidth ratio. In addition, PCC+CAKE shows more stable performance on throughput, RTT, and jitter, and has very low packet loss. Therefore, PCC+CAKE is recommended as a more effective load balancing method to increase the efficiency of ISP utilization and overall network quality in a multi-ISP environment.
XGBoost-Powered Ransomware Detection: A Gradient-Based Machine Learning Approach for Robust Performance Ghozi, Wildanil; Lestiawan, Heru; Sani, Ramadhan Rakhmat; Hussein, Jassim Nadheer; Rafrastara, Fauzi Adi
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 4, November 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i4.2405

Abstract

Ransomware remains a rapidly evolving cyber threat, causing substantial financial and operational disruptions globally. Traditional signature-based detection systems are ineffective against sophisticated, zero-day attacks due to their static nature. Consequently, machine learning-based approaches offer a more effective and adaptive alternative. This study proposes an approach utilizing XGBoost for highly effective ransomware detection. We conducted a rigorous comparative analysis of prominent ensemble learning algorithms—XGBoost, Random Forest, Gradient Boosting, and AdaBoost—on the RISS Ransomware Dataset, comprising 1,524 instances. Our experimental results unequivocally demonstrate XGBoost as the superior ensemble model, achieving an impressive 97.60% accuracy and F1-Score. This performance surpassed Gradient Boosting (97.20%), Random Forest (96.94%), and AdaBoost (96.50%). Furthermore, this study benchmarked XGBoost against established state-of-the-art (SOTA) methods, including Support Vector Machine (SVM) and the SA-CNN-IS deep learning approach. The comprehensive results underscore the core contribution of this study: by applying XGBoost with a carefully structured machine learning pipeline, our approach consistently outperforms two state-of-the-art methods (SVM and SA-CNN-IS) as well as other ensemble algorithms. This highlights the critical role of methodological precision in maximizing detection performance against evolving ransomware threats.
Development of a Web-Based Information System for Real-Time Fainting Detection Using YOLO in Smart Healthcare Triyanto, Wiwit Agus; Susanti, Nanik
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 4, November 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i4.2407

Abstract

Loss of consciousness (fainting) is a critical condition that requires prompt treatment, especially in the context of elderly health services and independent patient care. This research aims to develop a web-based information system that is able to detect fainting events in real-time using the You Only Look Once (YOLO) algorithm version 11, which is one of the latest approaches in deep learning-based object detection. The system is designed to monitor video from the surveillance camera directly, make visual inferences of the patient's posture, and provide automatic notifications if a loss of consciousness condition is detected. The dataset was obtained from the Roboflow platform and consists of 9,081 annotated images representing the fainting position. The YOLOv11 model was trained and tested using training data sharing, validation, and testing methods. The test results showed that the model achieved mAP, precision, recall and F1-score values of 98.70%, 98.00%, 97.30% and 97.65%, respectively. The developed information system is able to display the detection visually through the bounding box on the dashboard and record the time of the incident. With this performance, this system shows great potential in improving patient safety through intelligent monitoring and automated response in hospital, nursing home, and residential environments. This research also opens up opportunities for the development of more adaptive AI-based health monitoring systems and computer vision in the future.
Implementation of Convolutional Recurrent Neural Network for Vehicle Number Plate Identification in Raspberry Pi Based Parking System Muzammil, Rivaul; Oktiana, Maulisa; Roslidar, Roslidar
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 4, November 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i4.2320

Abstract

The rapid growth of vehicles in Indonesia has created significant challenges in managing parking facilities. To address this issue, this study proposes an intelligent parking system based on automatic license plate character recognition. The system employs YOLOv8 (You Only Look Once) for license plate region detection and CRNN (Convolutional Recurrent Neural Network) for alphanumeric character recognition. Its architecture integrates a Raspberry Pi, camera module, and servo motor to enable automated license plate detection and recognition during vehicle entry and exit. YOLOv8 generates bounding boxes to isolate license plate regions, which are then processed as input for CRNN. The CRNN extracts visual features through convolutional layers and captures sequential relationships among characters using recurrent layers. The entire pipeline is deployed on Raspberry Pi with TensorFlow Lite to ensure efficient computation in resource-constrained environments. Experimental results demonstrate that YOLOv8 achieved a detection accuracy of 94.69%, with a precision of 98.32%, recall of 96.25%, and F1-score of 97.27%, while CRNN reached a character recognition accuracy of 93.8% across 30 license plates. Although some recognition errors occurred, such as misclassifying ‘G’ as ‘C’, 'W' as 'H', and 'Q' as 'O', the proposed system proved effective and feasible for embedded smart parking applications.
Classification of Breast Cancer Histopathology Images with Attention-Based Multiple Instance Learning Method Setiyani, Safira Hasna; Noersasongko, Edi; Affandy
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 4, November 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i4.2310

Abstract

Breast cancer is one of the deadliest types of cancer among women worldwide. Early detection plays a crucial role in increasing the chances of successful treatment and reducing the risk of death. Various efforts have been made by both the general public and medical professionals to raise awareness, promote early screening, and ensure timely medical intervention. With advances in technology, the use of computer-based systems, particularly in the field of medical image analysis, has become increasingly important. One such application is histopathological image analysis to support the diagnostic process in breast cancer cases. Histopathological image classification has gained significant attention from researchers in recent years, and various machine learning and deep learning techniques have been applied to improve its accuracy. Convolutional Neural Networks (CNNs), as part of the deep learning framework, have shown promising results in identifying tissue patterns in histopathological images. However, despite their high accuracy, CNNs are often less interpretable, making it difficult to understand the reasoning behind their predictions—especially when dealing with subtle features such as small spots, dots, or fine lines that may be overlooked. This study addresses these limitations by proposing a method that not only classifies histopathological images with high accuracy but also enhances readability through localization techniques. The goal is to make the classification process more transparent and clinically useful. Using widely recognized datasets like BreakHIS, the proposed method achieves a classification accuracy of up to 97.50%, demonstrating its potential as a reliable tool in medical diagnostics and breast cancer research.
Multi-objective MPPT Optimisation for PV System Using QHBM Algorithm in Madura Island Nugraha, Agil Zaidan; Aripriharta; Handayani, Anik Nur
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 4, November 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i4.2337

Abstract

This study presents the application of the Queen Honey Bee Migration (QHBM) algorithm, for Maximum Power Point Tracking (MPPT) in an off-grid photovoltaic (PV) system on Madura Island. Implemented in Python, QHBM optimizes a 3.3 kW PV array (six polycrystalline silicon panels, 550 W each, configured in 2-series and 3-parallel) under tropical conditions (irradiation: 860–970 W/m², temperature: 26–30°C) using data from the East Java BMKG Trunojoyo Meteorological Station. QHBM’s multi-objective optimization balances power conversion efficiency (95.0–99.1%), power quality (THD < 4.5%), and component longevity (current ripple: 3.1–3.2 A), outperforming Perturb and Observe (P&O: 78% efficiency under low irradiation and 34% under partial shading) and Particle Swarm Optimization (PSO: 85% and 88%). Trade-offs are managed by minimizing ripple-induced thermal stress (10–15% lower than P&O) and achieving rapid convergence (0–3 ms vs. 300–500 ms for PSO), ensuring reliability in Madura’s dynamic climate. The system, integrated with a single-phase full-bridge inverter (96% efficiency), delivers a consistent daily energy output of 14,941.87 Wh (SD ±267.45 Wh) and reduces CO2 emissions by 118.49 kgCO2e annually. QHBM was chosen over P&O and PSO for its superior efficiency, faster response, and robustness under partial shading and noisy irradiation (±10% variations), offering a scalable solution for sustainable electrification in Indonesia’s archipelagic regions.
Enhancing Plant Recommendation through IoT-integrated LLM Systems Maulana, Panji; Cutifa Safitri
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.v11i1.2241

Abstract

Over the past decade, artificial intelligence has experienced phenomenally rapid and extensive expansion across a variety of industries. Along with developments over time, the agricultural sector stands to benefit significantly from the integration of technology. A significant challenge encountered by farmers is selecting the appropriate crop to plant. The selection of crops is influenced by various factors. Despite advancements in agricultural technology, a considerable gap remains in the integration of IoT with large language models (LLM) for delivering context-specific and data-driven plant recommendation. This study evaluates the reliability of plant recommendations produced by Internet of Things (IoT) devices utilizing the Llama 3.2 model. The model will utilize real-time environmental data, including soil pH, altitude, and temperature, to recommend appropriate plant. The recommendations will be compared between base model and fine tune model using precision, recall and f1 metrics and be assessed in relation to established agricultural literature concerning plant compatibility and growth requirements with human evaluation. This research achieved an AUC value that exceeded that of the base model by 10%, Precision exhibited a 25% increase relative to the base model, while recall demonstrated a significant rise of 52% from the base model. The F1 score also improved by 39% compared to the base model.
Design of 2x1 Microstrip Antenna Array Single Band with Proximity Coupling for Enhanced CCTV Performance Setiabudi, Dodi; Agustina, Citra; Syaifullah, Muh. Arif; Sarwono, Catur Suko; Herdiyanto, Dedy Wahyu; Chaidir, Ali Rizal; Laagu, Muh Asnoer
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.v11i1.2303

Abstract

The increasing demand for reliable wireless communication in modern surveillance systems, particularly Closed-Circuit Television (CCTV), requires the development of antennas with high efficiency, wide bandwidth, and stable signal performance. To meet these requirements, this study presents the design and analysis of a 2×1 microstrip array antenna with rectangular patches that use proximity coupling, optimized for operation in the 2.4 GHz ISM band. The antenna was designed and simulated using CST Studio Suite to evaluate its electromagnetic characteristics, while measurements using a Vector Network Analyzer (VNA) were performed to validate the performance of the manufactured prototype. Simulation results show that the antenna achieves a reflection loss of −24.62 dB, a standing wave ratio (VSWR) of 1.12, and a frequency bandwidth of 159 MHz, indicating good impedance matching and wide operational capability. Meanwhile, measurement results showed a reflection loss of −12.59 dB, a VSWR of 1.15, and a frequency bandwidth of 86 MHz. Both simulation and measurement results showed directional radiation patterns, ensuring efficient energy radiation and better signal focus for monitoring coverage. The designed antenna also shows a measured gain of 9.25 dBi, exceeding the simulated gain of 6.99 dBi, confirming improved performance. The difference between simulation and measurement is mainly due to variations in substrate thickness, material tolerance, and environmental factors during testing. Overall, the proximal coupling approach has proven effective in improving coupling efficiency without adding design complexity. This antenna is well-suited for reliable and efficient data transmission in CCTV applications. Furthermore, the findings contribute significantly to advancements in antenna technology, particularly in the domains of wireless communication, IoT, and smart city-based surveillance systems.
Design of a Real-Time User Feedback for Mitigating Spurious SpO₂ Readings in Pulse Oximetry for Outpatient Monitoring Mukhtar, Husneni; Rahmawati, Dien; Setiyadi, Suto; Istiqomah; Madani, Reza Ahmad
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.v11i1.2371

Abstract

Spurious SpO₂ readings—arising from motion artifacts, environmental interference, or device variability—remain a major limitation in wearable pulse oximetry, potentially triggering false alarms or missing hypoxemia during outpatient monitoring. Conventional devices often lack real-time mechanisms to detect and mitigate such errors, with previous reports indicating measurement biases of 11.2 - 24.5% across different models, underscoring the need for improved accuracy and user guidance. To address this gap, we present the design of an IoT-enabled wearable pulse oximeter with real-time user feedback, delivered through a mobile application. The system integrates a pulse oximetry and heart rate sensor (MAX30100) with a carbon monoxide gas sensor (MQ-7) and provides targeted notifications to guide corrective actions such as repositioning the probe, removing nail polish, or moving to fresh air. Validation involved controlled scenario testing (undetected SpO₂, CO >40 ppm, nail polish, loose contact) and user trials with 15 healthy volunteers from varied academic backgrounds. The prototype demonstrated high accuracy, with low relative errors—0.92% (HR), 0.93% (SpO₂), and 0.015% (CO)—and strong usability, achieving 93.3% compliance with corrective prompts, an average response time of 4.0±0.7 seconds, and a satisfaction score of 4.3/5. Compared with commercial oximeters, the proposed system improved reliability by reducing measurement errors by at least 87% through real-time corrective feedback. Future work will focus on energy-efficient power management and large-scale community-based trials to further validate performance across diverse patient populations.
Adaptive EKF-Based Ship Trajectory Estimation with Earth Curvature Modeling and Dynamic Noise Tuning Elfada, Berliana; Gardara, Suci Awalia; Soewono, Eddy Bambang; Widhiyasana, Yudi
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.v11i1.2397

Abstract

Accurate position estimation is critical for the effectiveness of automatic weapon and navigation systems. Standard Extended Kalman Filter (EKF) models typically adopt flat-Earth assumptions and static noise covariances, which limit their accuracy in operational environments. This study proposes an optimized EKF framework that integrates two complementary approaches. First, ship trajectories are represented in Earth-Centered Earth-Fixed (ECEF) coordinates with a WGS-84 reference to account for Earth’s curvature. Second, process (Q) and measurement (R) covariances are adaptively determined using Joint Likelihood Maximization (JLM) with logarithmic scale exploration, allowing the filter to automatically identify the most accurate configuration. Each Q/R setting is evaluated within the EKF framework using root mean square error (RMSE) derived from radar data logs. The method was tested under short-history scenarios (5 and 10 data points) within an operational range of ±15 km, reflecting conditions commonly encountered in Combat Management Systems (CMS). Results show that while coordinate transformation alone provides only marginal improvements at short ranges, the combination of curvature modelling and adaptive Q/R tuning significantly reduces RMSE, achieving average errors approaching zero with high repeatability as measured by standard deviation. This research demonstrates a novel integration of geometric and statistical optimization in EKF design and highlights its applicability to ship trajectory estimation and defence systems.

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