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 15 Documents
 1   2 
Search results for , issue "Vol. 10, No. 4, November 2025" : 15 Documents clear
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.

Page 2 of 2 | Total Record : 15

 1   2 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue 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