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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. 3, august 2026" : 20 Documents clear
A Data-Driven Framework Integrating Clustering and Classification for Fair Tuition Grouping (UKT) Prediction Windy Chikita Cornia Putri; Wiyli Yustanti; Ervin Yohannes; Yoyok Prastyo
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 3, August 2026
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i3.2578

Abstract

This study aims to identify the most effective combination of feature selection techniques and classification algorithms for predicting student tuition groups (Uang Kuliah Tunggal, UKT) based on pre-admission data. Three feature selection methods—Exploratory Factor Analysis (EFA), Recursive Feature Elimination (RFE), and Random Forest Feature Importance (RFFI)—were employed and combined with five supervised learning models: Decision Tree, Random Forest, Support Vector Machine (SVM) with RBF kernel, Naïve Bayes, and K-Nearest Neighbor (KNN). The results demonstrate that the EFA–SVM (RBF) combination achieved the best performance, with an average accuracy exceeding 98%, outperforming other models across most faculties. EFA also yielded the highest Silhouette Score (0.2933), indicating a more stable and distinct cluster structure than RFE (0.2564) and RFFI (0.2575). These findings highlight the critical role of appropriate feature selection in improving classification accuracy and model generalization, particularly by emphasizing socioeconomic variables such as parental income, land area, housing conditions, and basic family facilities. The integration of factor-based dimensionality reduction with nonlinear classification algorithms proved effective in developing a more transparent and equitable UKT prediction model. This research contributes to the advancement of data-driven decision support systems in higher education and provides a foundation for future automation of tuition group determination processes.
Regularization Techniques to Improving the Stability and Accuracy of the MLC Algorithm Usman Sudibyo; Noor Ageng Setyanto; Ahmad Wahid Kurniawan; Carissa Devina Usman
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 3, August 2026
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i3.2583

Abstract

Maximum Likelihood Classification (MLC) is a classification algorithm that has important applications in the fields of image processing and remote sensing. No use of MLC was found in other fields. MLC assumes that data come from a certain probability distribution (for example, a normal distribution), which may be too simple to describe complex data or data with a non-normal distribution. This can lead to poor performance in situations where distribution assumptions are not met. That is why, in the existing literature, there is no use of MLC for classification problems other than remote sensing. We propose a regularization technique to reduce distribution assumption errors in MLC called Regularized Maximum Likelihood Classification (RMLC). Regularization techniques are integrated into the covariance matrix, where regularization can make the data variance larger or smaller than the actual variance. This technique can also overcome singularities in the covariance matrix, non-Gaussian data, and data containing outliers. Experimental results on 13 public datasets show a significant increase in accuracy performance. The average accuracy increase reaches more than 11%, from 0.802 to 0.919, highlighting its potential for broader applicability and enhanced performance.
AIoT-Enabled Automatic Waste Sorting System with Real-Time WhatsApp Notifications Muchamad Rusdan; Sri Kuswayati
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 3, August 2026
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i3.2593

Abstract

The waste management crisis, particularly in educational institutions, requires innovative solutions that combine artificial intelligence and automation. This research develops and evaluates an automated waste sorting system based on the Artificial Intelligence of Things (AIoT), integrated with WhatsApp notifications. The system utilizes the EfficientNet-B0 deep learning model optimized through transfer learning and runs on a Raspberry Pi 4 edge device to classify waste into five categories: plastic, paper, metal, glass, and organic, in real time. The classification results are translated into physical actions by a servo actuator mechanism, while ultrasonic sensors monitor the trash bin capacity. The real-time notification system, implemented through the WhatsApp API, sends alerts to administrators. A 30-day evaluation conducted on campus showed that the system achieved a classification accuracy of 92.3% with an inference latency of 1.8 seconds. The mechanical system successfully sorted waste with a 94.5% success rate, while WhatsApp notifications achieved a 99.1% delivery rate, with an average administrator response time of 8.2 minutes during operational hours. A comparative analysis demonstrated that the system increased sorting efficiency by 87% and reduced operational costs by 45% compared with manual waste sorting methods. These findings conclude that the proposed integration of edge AI, mechanical automation, and WhatsApp notifications provides a smart waste management solution that is not only effective and real-time but also practical, economical, and sustainable for wider implementation.
Accuracy Comparison of Multivariate Newton-Raphson, Newton-Kantorovich, and Levenberg–Marquardt Methods for Solving Nonlinear Systems Using Numerical Simulation Syaharuddin Syaharuddin; Hendi Hidayah; Vera Mandailina; Saba Mehmood; Wasim Raza
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 3, August 2026
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i3.2603

Abstract

Multivariable nonlinear equation systems often appear in engineering, physics, economics, and artificial intelligence modeling, but often do not have closed analytical solutions. Therefore, accurate, efficient, and stable numerical methods are needed. This study aims to comparatively evaluate three iterative methods, namely Multivariate Newton-Raphson, Newton-Kantorovich, and Levenberg–Marquardt, in solving identical high-complexity multivariable nonlinear systems. Simulations were performed using MATLAB with an error tolerance of 0.001 and a maximum iteration limit of 100. The test system consisted of a combination of trigonometric, exponential, and polynomial functions, resulting in nonlinear interactions that were challenging for each method. The simulation results show that Levenberg–Marquardt excelled with only 6 iterations and a final error of 3.246 × 10⁻¹⁰, indicating high stability and efficiency, followed by Multivariate Newton-Raphson with 13 iterations and an error of 4.606 × 10⁻⁹, while Newton-Kantorovich requires 27 iterations with an error of 5.770 × 10⁻⁷, reflecting slower semi-local corrections. Three-dimensional visualization shows the intersection point of the surface as a solution, providing an intuitive understanding of the iteration trajectory characteristics of each method. The novelty of this research lies in the integrated numerical simulation framework that allows direct quantitative comparison of the three methods on identical systems with the same initial conditions, tolerance, and iteration limits. These findings provide important empirical references for selecting efficient and stable iterative methods for multivariable nonlinear systems, as well as practical guidance for numerical applications in engineering, physics, and scientific computing.
Hate Speech Analysis of YouTube Comments on the 2024 Indonesian Presidential Debate Using IndoBERT Agus Sasmito Aribowo; Yuli Fauziah; Yusna Bantulu; Shoffan Saifullah; Azfa Mutiara Ahmad Fubalo
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 3, August 2026
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i3.2604

Abstract

The rapid digitization of political campaigns has intensified the spread of hate speech on social media, threatening democratic discourse and social cohesion. In Indonesia, YouTube comments on the 2024 presidential election debates have emerged as a critical yet underexplored source of polarizing content. However, existing detection systems struggle with Indonesian-specific linguistic features, code-mixing, and implicit political sarcasm, while high annotation costs and class imbalance further limit the scalability of supervised approaches. To address these challenges, this study introduces a large-scale dataset of 38,742 YouTube comments collected from the five official debate stages and labeled using a cost-effective semi-supervised framework (20% expert-annotated, 80% pseudo-labeled). We systematically evaluate four classification models —IndoBERT, mBERT, SVM, and Random Forest—under identical experimental conditions using evaluation metrics optimized for imbalanced data. Experimental results demonstrate that IndoBERT consistently outperforms all baseline models, achieving an average accuracy of 89.7% and a macro F1-score of 0.89 across all debate stages. Notably, IndoBERT maintains high recall for the minority hate speech class (0.81–0.90), confirming its superior ability to capture localized political rhetoric and contextual nuances that multilingual and classical models frequently miss. This study contributes a publicly available Indonesian political hate speech dataset, validates a scalable semi-supervised annotation pipeline, and provides empirical evidence that domain-specific Transformer models are essential for reliable content moderation in politically charged, low-resource environments.
Comparison of Word2Vec and GloVe Performance in Bi-LSTM Models for Indonesian News Classification Muhammad Faris Wafda; Husni; Ika Oktavia Suzanti; Firdaus Solihin; Mula'ab; Army Justitia
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 3, August 2026
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i3.2608

Abstract

The explosion in the volume of textual data from digital news presents challenges in classifying content automatically and efficiently. For the task of classifying Indonesian-language news, this study aims to compare the performance of several word embeddings specifically Word2Vec using CBOW and Skip-Gram architectures and GloVe when applied to a Bidirectional Long Short-Term Memory (Bi-LSTM) model. This study uses a dataset consisting of 6,715 news articles from the Indonesian news portal that have undergone pre-processing, divided into five categories. The model was trained using 80% of the training data with K-Fold Cross Validation (K=5), while the remaining 20% of the data was used for testing. The experimental findings indicate that the Bi-LSTM model, when combined with CBOW embedding, yielded the best performance, achieving 95.16% accuracy and a 95.15% F1-Score. The Skip-Gram model followed with solid performance, achieving an accuracy of 93.30% and the fastest computation time. Conversely, the model that used pre-trained GloVe embedding delivered the poorest performance, achieving 88.98% accuracy. This result suggests that training embeddings on a specific domain is more effective at capturing local context. The conclusion of this study confirms that selecting a word embedding method specifically trained on local datasets is also an important step in achieving optimal accuracy in Indonesian news text classification.
A Comparative Study of Hybrid GARCH–HOLT–BPNN Models for Rainfall Forecasting Using a MATLAB-Based Intelligent Computing System Supardi Supardi; Syaharuddin Syaharuddin; Vera Mandailina; Saba Mehmood
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 3, August 2026
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i3.2636

Abstract

Rainfall forecasting is essential for water resource management, hydrometeorological disaster mitigation, and agricultural planning. This study addressed the limitations of previous research that focused on single models or hybrid approaches combining only two methods, which often failed to capture the simultaneous volatility, trend, and nonlinear characteristics of rainfall data. Monthly rainfall data from 2015 to 2024 were analyzed using three individual models: Generalized Autoregressive Conditional Heteroskedasticity (GARCH), Holt’s Exponential Smoothing, and Backpropagation Neural Network (BPNN). Two hybrid models, GARCH–Holt and GARCH–Holt–BPNN, were also developed to integrate the advantages of statistical and artificial intelligence methods. Hyperparameter tuning was performed to optimize model performance, and forecasting accuracy was evaluated using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). Results showed that GARCH effectively captured short-term volatility, Holt followed trends and seasonal patterns, and BPNN modeled nonlinear relationships despite sensitivity to data variations. The GARCH–Holt hybrid improved stability and accuracy compared to individual models, while the GARCH–Holt–BPNN hybrid achieved the highest predictive performance with a MAPE of 1.13%, indicating strong generalization capability. Forecasted rainfall for 2025 revealed seasonal patterns characterized by periods of heavy, moderate, and light rainfall. A MATLAB-based Graphical User Interface (GUI) was developed to facilitate interactive modeling and visualization. Overall, the proposed hybrid GARCH–Holt–BPNN model provides a more robust and reliable forecasting framework by effectively integrating volatility, trend, and nonlinear components, thereby enhancing predictive accuracy and supporting data-driven decision-making in hydrometeorological applications.
Automated Breast Cancer Cell Counting: Comparing Multi-class Segmentation and Two-stage Classification Strategies Dzaky Hanif Arjuna; Edy Kurniawan; Reza Fuad Rachmadi; I Ketut Eddy Purnama
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 3, August 2026
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i3.2639

Abstract

The manual interpretation of Hematoxylin and Eosin (H&E) histopathology images for breast cancer diagnosis is hindered by time constraints and observer bias. This research aims to develop an automated system using deep learning for cell detection and classification by evaluating two key approaches: Multi-class Segmentation (single-stage) and Segmentation followed by Classification (two-stage). The U-Net architecture was employed for segmentation, while MobileNetV2 and VGG16 were used for classification. The models were evaluated on the public IHC4BC dataset and primary data from Airlangga University Hospital (RSUA). The study also evaluated the impact of Resizing and Tiling data processing strategies. Experimental results showed that although the MobileNetV2 and VGG16 classification models achieved a high testing accuracy of 98.80%, the integrated two-stage system exhibited a high counting error, with a Mean Absolute Error (MAE) of 119.87 for positive cells, primarily due to under-segmentation of overlapping cells. In contrast, the Multi-class Segmentation approach utilizing the Tiling strategy demonstrated superior performance. This model effectively preserved spatial resolution while distinguishing cell types simultaneously, achieving the lowest MAE of 18.46 for positive cells and 1.66 for negative cells. This study concludes that Multi-class Segmentation with the Tiling strategy is the most effective and accurate approach for automated cell counting in histopathology images.
Scalable Multi-Agent Formation Control in RTS Games: A Virtual Anchor and Fluid-Based Allocation Ibnu Athaillah; Moch. Kholil
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 3, August 2026
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i3.2643

Abstract

The control system for troop formation movement is a critical component of Real-Time Strategy (RTS) games, directly affecting gameplay quality and player experience. However, traditional CPU-bound pathfinding algorithms fail to maintain 60 FPS when handling more than 1,000 agents in dynamic environments, particularly when balancing rigid formation structure with pathfinding efficiency amid complex obstacles. This study proposes an integrated framework for troop formation movement that synthesizes a virtual Anchor navigation paradigm with a Fluid-Based Formation Position Allocation algorithm.  Unlike traditional leader-follower methods, the proposed system utilizes a virtual anchor to calculate global pathfinding via NavMesh, while constituent agents dynamically adjust their positions relative to this reference point. To mitigate trajectory conflicts during formation changes, the system employs a fluid-dynamics-inspired sorting strategy that deterministically maps agents to target slots using parallel processing. The architecture is optimized for real-time performance using the Unity Job System, allowing for the coordination of large-scale agent aggregates. Experimental validation was conducted through behavioral scenarios—including Tunnel, Split, and Crowd tests—as well as stress tests involving up to 4,096 agents. The results demonstrate that the system successfully maintains formation integrity, executes autonomous regrouping after obstacle traversal, and ensures collision-free movement. Performance analysis further indicates that the control logic remains computationally stable at scale, with the primary limitations shifting to graphical rendering overhead rather than algorithmic complexity.
Kalman Filter-Based RSS Preprocessing for Cryptographic Key Generation in Zero-Knowledge Feige Fiat Shamir Authentication M. Cahyo Kriswantoro; Eko Handoyo; Ahmad Lathif Aditya
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 3, August 2026
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i3.2644

Abstract

Secure authentication in wireless communication environments requires mechanisms capable of verifying identity without exposing confidential information. Zero-Knowledge Authentication (ZKA) addresses this challenge by enabling interactive identity verification without revealing secret credentials. However, in practical wireless implementations, the reliability of ZKA strongly depends on the quality and consistency of the cryptographic keys used during the authentication process. One promising approach is to generate keys from physical-layer characteristics, such as Received Signal Strength (RSS). Nevertheless, raw RSS measurements are highly unstable due to noise, interference, mobility, and signal fluctuations, which often result in low reciprocity and key mismatch between legitimate nodes. This study proposes a Kalman Filter-based preprocessing method to improve RSS quality prior to cryptographic key generation for Zero-Knowledge Feige–Fiat–Shamir authentication. The Kalman Filter is employed to suppress measurement noise and enhance reciprocity between communicating nodes, allowing both parties to derive more consistent symmetric keys. The generated keys are then integrated into the authentication process to replace conventional static, channel-dependent key components. The proposed approach was evaluated using key consistency, entropy level, and bit mismatch rate as performance metrics. Experimental results show that Kalman Filter-based preprocessing significantly improves RSS stability and increases the correlation between legitimate nodes compared with unfiltered RSS measurements. In addition, the generated keys exhibit lower bit mismatch rates and better entropy characteristics, leading to higher authentication success rates while preserving the confidentiality properties of Zero-Knowledge Authentication. These findings demonstrate that Kalman Filter-assisted RSS preprocessing provides an effective and lightweight solution for strengthening cryptographic key generation and improving the reliability of authentication systems in wireless communication environments.

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