Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control

Spoon Stabilization for Essential Tremor Patients Using PID Control Optimized by PSO

Putri Ayu Zartika, Muhammad Aziz Muslim, Erni Yudaningtyas — Sat, 01 Nov 2025 00:00:00 +0000

Essential tremor is a neurological disorder that causes uncontrollable hand tremors, interfering with daily activities such as eating. This study aims to develop a spoon stabilization system controlled by a Proportional-Integral-Derivative (PID) controller, which was tuned using Particle Swarm Optimization (PSO) and the Cohen-Coon method for performance comparison. The system utilized an inertial measurement unit to detect tremors, while a Kalman filter was used to reduce noise before a microcontroller controlled a servo motor to stabilize the spoon. The system was evaluated through simulations and hardware implementation, and its performance was assessed based on rise time, overshoot, delay time, and settling time. The results showed that the Kalman filter significantly reduced noise, lowering the average pitch angle error deviation from 1.028° to 0.037° and the roll angle error from 0.822° to 0.031°. The PSO-based tuning outperformed the Cohen-Coon method in response speed and system stability, achieving a faster rise time (0.09 s for roll, 0.34 s for pitch), a shorter settling time (0.74 s for roll, 0.59 s for pitch), and a lower delay time (0.1 s for roll, 0.15 s for pitch). However, the Cohen-Coon method resulted in a lower overshoot for the roll angle (6.08%) compared to the PSO-based tuning (11.98%). The findings suggest that implementing a PID controller optimized via PSO is a viable approach for spoon stabilization in individuals with essential tremor.

Post Attack Mitigation on Open Journal System Services Using Knowledge Understanding Assessment Defense (KUAD) Method

Hero Wintolo, Imam Riadi, Anton Yudhana — Sat, 01 Nov 2025 00:00:00 +0000

This research was conducted to investigate evidence of an attack and to restore data after an attacker compromised an Open Journal System (OJS) service on a computer server. The method used in this research is a new approach developed from the Network Forensic Digital Life Cycle (NFDLC) method. This new method, known as KUAD, has several stages for collecting cyber-attack evidence and restoring it after the Gacor attack has occurred. The stages in the KUAD method include initiation, acquisition, execution, mitigation, and disposition. The novelty of this method, compared to the previous one, lies in the inclusion of the mitigation stage, which aims to restore data or documents after an attack. The tool used to detect the attack and find evidence of the attack is Tripwire, whereas the tools used to restore lost data include crontab, which runs backup commands with rsync in four steps. Tripwire can optimally detect attacks by displaying the number of data entries that were added, deleted, or modified. A total of 15,135 files in .docx, .pdf, and .jpg formats, deleted by the attacker, were successfully restored using this backup technique. The success rate of using this technique for post-cyber attack mitigation reached 100%.

Game Development of Banjar Archive for Interactive Cultural Education Ultilizing Large Language Models

Sat, 01 Nov 2025 00:00:00 +0000

The preservation of Banjar cultural heritage is threatened by globalization and the fading interest of younger generations. This study addressed these challenges by developing an interactive educational game using the Game Development Life Cycle (GDLC) framework and integrating Large Language Models (LLMs) for adaptive and immersive player interactions. The six stages of GDLC namely initiation, pre-production, production, testing, beta, and release were systematically applied, resulting in a game that blends dynamic narratives to engage players while educating them about Banjar culture. Black Box Testing verified 14 test scenarios that all passed successfully, ensuring system stability and reliability. Additionally, user experience evaluation using the Game Experience Questionnaire (GEQ) highlighted high levels of immersion (4.936), competence (4.448), flow (3.124) and positive affect (4.976) among players, with minimal reported tension (1), challenge (1.744) and negative affect (1.07). These results demonstrated that the game successfully balances educational goals with engaging gameplay, fostering meaningful connections to Banjar heritage. By leveraging LLM technology, the game enhances interactivity, offering an innovative approach to Banjar cultural preservation in the digital era. This research extends the existing body of knowledge on AI-driven gamification strategies in heritage conservation with a specific focus on Banjar culture.

The Evolution of Image Captioning Models: Trends, Techniques, and Future Challenges

Ade Bastian, Abrar Wahid, Zacky Hafsari, Ardi Mardiana — Sat, 01 Nov 2025 00:00:00 +0000

This study provides a comprehensive systematic literature review (SLR) of the evolution of image captioning models from 2017 to 2025, with a particular emphasis on the impending problems, methodological enhancements, and significant architectural developments. The evaluation is guided by the increasing demand for precise and contextually aware image descriptions, and it adheres to the PRISMA methodology. It selects 36 relevant papers from reputable scientific databases. The results indicate a significant transition from traditional CNN-RNN models to Transformer-based architectures, which leads to enhanced semantic coherence and contextual comprehension. Current methodologies, such as prompt engineering and GAN-based augmentation, have further facilitated generalization and diversity, while multimodal fusion solutions, which incorporate attention mechanisms and knowledge integration, have improved caption quality. Additionally, significant areas of concern include data bias, equity in model assessment, and support for low-resource languages. The study underscores the fact that modern vision-language models, such as Flamingo, GIT, and LLaVA, offer robust domain generalization through cross-modal learning and joint embedding. Furthermore, the efficacy of computing in restricted environments is improved by the development of pretraining procedures and lightweight models. This study contributes by identifying future prospects, analyzing technical trade-offs, and delineating research trends, particularly in sectors such as healthcare, construction, and inclusive AI. According to the results, in order to optimize their efficacy in real-world applications, future picture captioning models must prioritize resource efficiency, impartiality, and multilingual capabilities.

Classification of Breast Cancer Histopathology Images with Attention-Based Multiple Instance Learning Method

Safira Hasna Setiyani, Edi Noersasongko, Affandy — Sat, 01 Nov 2025 00:00:00 +0000

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.

Transfer Learning Approaches for Non-Organic Waste Classification: Experiments Using MobileNet and VGG-16

Zamah Sari, Setio Basuki — Sat, 01 Nov 2025 00:00:00 +0000

This paper develops machine learning (ML) models for classifying non-organic waste automatically. The goal is to support more effective waste management by increasing recycling rates, reducing landfill use, and minimizing environmental impact. The ML models proposed in this paper classify 20 types of non-organic waste collected from the internet, which consists of 2,552 instances. Our experiments reveal several key findings. First, MobileNet, which achieved 86% accuracy, outperforms VGG-16, which reaches only 72% accuracy. Second, both models show good classification performances in classifying glass bottles, toothbrushes, and cigarette butts. Third, both models suffer from misclassification in visually similar categories, especially when it comes to paper-based waste like books, cardboard, foam packaging, and carton packaging. Fourth, MobileNet has difficulty detecting plastic packaging, carton packaging, and books, while VGG-16 exhibits higher misclassification rates for foam packaging, cardboard, and newspapers. These results pose a further critical development of the model to classify non-organic waste with similar textures and shapes. Moreover, it presents the urgency of improving the model to distinguish visually similar waste materials. Considering the number of labels used in this paper compared with existing studies, the findings demonstrate the competitiveness of our models for non-organic waste classification.

Implementation of Convolutional Recurrent Neural Network for Vehicle Number Plate Identification in Raspberry Pi Based Parking System

Rivaul Muzammil, Maulisa Oktiana, Roslidar Roslidar — Sat, 01 Nov 2025 00:00:00 +0000

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.

Multi-objective MPPT Optimisation for PV System Using QHBM Algorithm in Madura Island

Agil Zaidan Nugraha, Aripriharta, Anik Nur Handayani — Sat, 01 Nov 2025 00:00:00 +0000

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.

Modified U-Net for Leaf Segmentation of Eucalyptus pellita Seedlings in Open Natural Environments

Sat, 01 Nov 2025 00:00:00 +0000

This study addressed leaf segmentation in open nursery environments for Eucalyptus pellita seedlings, where fluctuating illumination, cluttered backgrounds, and overlapping foliage had hindered reliable monitoring at operational scale. We proposed a Modified U-Net that integrated a ResNet-50 encoder for high-resolution feature extraction, L2 regularization in the decoder to improve generalization, and a composite binary cross-entropy plus Dice loss to balance pixel-level accuracy with shape conformity. We assembled 2,424 RGB images from an operational nursery and evaluated three architectures (Modified U-Net as the primary model, SegNet, and DeepLabv3+) under cloudy, sunny, and scorching illumination. We conducted inference at native resolution and summarized per-image metrics using medians with interquartile ranges, followed by nonparametric significance testing. The Modified U-Net consistently outperformed the baselines across all scenarios, achieving median Dice coefficients of 0.872 (cloudy), 0.841 (sunny), and 0.854 (scorching), with corresponding Intersection over Union values of 0.773, 0.725, and 0.745. A Kruskal-Wallis test on per-image Dice and Intersection over Union yielded no significant differences across lighting conditions (H = 4.012, p = 0.1345), indicating stable performance under natural illumination variability. Qualitative overlays revealed localized errors, including glare-induced false positives in sunny scenes and shadow-related artifacts under scorching light, which did not materially shift global overlap distributions. We concluded that the proposed architecture delivered robust, high-fidelity segmentation in realistic nursery conditions and provided a practical basis for field deployment, with further gains expected from glare- and shadow-aware augmentation and lightweight optimization for near real-time inference on edge devices.

The Application of the Adaptive Neuro Fuzzy Inference System (ANFIS) Method in Estimating State of Charge (SOC) and State of Health (SOH) of Lithium-Ion Batteries

Selamat Muslimin, Ekawati Prihatini, Nyayu Latifah Husni, Wahyu Caesandra — Sat, 01 Nov 2025 00:00:00 +0000

The increasing reliance on lithium-ion batteries (LIBs) for electric vehicles and portable electronics demands accurate monitoring of battery performance, particularly the State of Charge (SOC) and State of Health (SOH). Conventional estimation methods—such as Coulomb counting, Kalman filtering, and equivalent circuit modeling—face challenges under dynamic conditions due to drift and limited adaptability. Recent studies have explored machine learning and neuro-fuzzy approaches to enhance prediction accuracy, yet many lack integration of real-time hybrid learning or struggle with high estimation error in noisy data environments. This research aims to apply the Adaptive Neuro-Fuzzy Inference System (ANFIS) to estimate SOC and SOH using experimental data from a 48V lithium-ion battery. The novelty lies in combining voltage, current, and capacity data within a MATLAB-based ANFIS framework that employs a hybrid learning algorithm integrating backpropagation and Recursive Least Squares Estimation (RLSE). Training data for SOC estimation used charging voltage and current, while SOH estimation incorporated discharging data and capacity. Results show that ANFIS achieved high accuracy with RMSE of 0.1466 and MAE of 0.021 for SOC, and RMSE of 0.012 and MAE of 0.0017 for SOH. The estimated SOH was 33.61%, closely aligned with actual values. These findings confirm ANFIS as a robust and adaptive method for real-time battery diagnostics. Future work will explore multi-input hybrid models, the integration of IoT-based BMS telemetry, and testing across diverse battery chemistries to generalize the model's performance and extend its application in smart energy systems.

Ambidextrous Blockchain Governance to Strengthen BankCo’s Digital Transformation through COBIT 2019 Traditional and DevOps

Salsabill Nur Aisyah, Rahmat Mulyana, Tien Fabrianti Kusumasari — Sat, 01 Nov 2025 00:00:00 +0000

The adoption of blockchain in banking accelerates digital transformation by enhancing transparency and operational efficiency. However, it also brings with it governance issues pertaining to accountability, compliance, and system integrity within a highly regulated environment. This study addresses these challenges by developing a blockchain governance solution based on ambidextrous approach within COBIT 2019’s Traditional and DevOps Focus Areas. A governance model was built and evaluated through iterative steps. Until saturation was reached, information was gathered through key stakeholder interviews and checked with internal documentation such as yearly reports, risk frameworks, and policy records. The ambidextrous COBIT 2019 framework was used in the analysis for all seven governance components. Governance and Management Objectives (GMOs) were prioritized based on design factors, national regulations (POJK No.11/2022 and SOE Minister Regulation No.PER-2/MBU/03/2023), and insights from prior studies. APO12: Managed Risk was identified as the most prioritized GMO. A capability gap analysis revealed missing leadership roles, overlapping security responsibilities, and underdeveloped risk management practices. Recommendations include formalizing key governance roles and strengthening risk management process for blockchain and DevOps environments. These enhancements are expected to increase the maturity level of APO12 from 3.5 to 4.1, thereby improving BankCo’s risk management, compliance, and innovation capabilities. Ultimately, the findings contribute to continuous digital innovation by aligning risk management practices with strategic performance goals and adaptive control mechanisms rooted in emerging technology principles.

A Metaheuristic Wrapper Approach to Feature Selection with Genetic Algorithm for Enhancing XGBoost Classification in Diabetes Prediction

Sat, 01 Nov 2025 00:00:00 +0000

This study addressed the problem of selecting the most relevant features for improving the accuracy of diabetes classification using health indicator data. The research focused on a binary classification task based on the Behavioral Risk Factor Surveillance System dataset, which comprised over seventy thousand records and twenty-one predictive features related to individual health behaviors and conditions. A metaheuristic wrapper approach was developed by integrating a Genetic Algorithm for feature selection with an XGBoost classifier to evaluate the predictive quality of each feature subset. The fitness function was defined as the average classification accuracy obtained through cross-validation. In addition to feature selection, hyperparameter optimization of the XGBoost model was carried out using a Bayesian-based search strategy to further enhance performance. The proposed method successfully identified a subset of fourteen optimal features that contributed most significantly to the prediction of diabetes. The final model, combining the selected features and optimized parameters, achieved an accuracy of 0.753, outperforming both the baseline models trained on all features and models using features selected through deterministic methods. These results confirmed the effectiveness of combining evolutionary feature selection with model tuning to build efficient and interpretable predictive models for medical data classification. This approach demonstrated a practical solution for managing high-dimensional data in the context of chronic disease prediction.

Evaluation of Traffic Distribution Performance of ECMP and PCC+CAKE for Multi-ISP Load Balancing on Real Networks Based Using Mikrotik

Moh Fathurrohim, Achmad Basuki — Sat, 01 Nov 2025 00:00:00 +0000

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

Sat, 01 Nov 2025 00:00:00 +0000

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

Wiwit Agus Triyanto, Nanik Susanti — Sat, 01 Nov 2025 00:00:00 +0000

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.