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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 575 Documents
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Hate Speech Analysis Using IndoBERT in YouTube Comments on the 2024 Indonesian Presidential Debate Video 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 (Article in Progress)
Publisher : Universitas Muhammadiyah Malang

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

Abstract

A Hate speech in the digital political space during election campaigns has the potential to cause polarization and undermine the quality of public discussion. This study analyzes hate speech in YouTube comments related to the five stages of the 2024 Indonesian presidential debate. We used IndoBERT, a Transformer-based language model specifically trained in Indonesian, to classify comments into hate speech and non-hate speech categories. The dataset consists of 38,742 comments collected from official debate videos. The dataset was labeled using a combination of manual annotation (20%) and semi-supervised learning (80%) using a pseudo-labeling approach. Experimental results show that IndoBERT achieved an average accuracy of 89.7% and a macro F1-score of 0.89 across all stages. IndoBERT outperformed baseline models such as mBERT, SVM, and Random Forest. These findings suggest that IndoBERT is more effective in capturing the linguistic nuances and distinctive Indonesian political rhetoric than multilingual or classical models. This study contributes an Indonesian-language political dataset and a comprehensive evaluation of relevant hate speech detection models for further research. Keywords: hate speech, IndoBERT, 2024 presidential debate, semi-supervised learning.
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 (Article in Progress)
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 (Article in Progress)
Publisher : Universitas Muhammadiyah Malang

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

Abstract

Rainfall forecasting is a fundamental aspect of water resource management, hydrometeorological disaster mitigation, and agricultural planning, all of which are strongly influenced by climate variability. The complexity of rainfall data, characterized by non-linear, non-stationary, and highly fluctuating patterns, necessitates the use of adaptive and accurate predictive approaches. This study aims to conduct a comparative analysis of five forecasting models, namely the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, Holt’s Exponential Smoothing, Backpropagation Neural Network (BPNN), the hybrid GARCH–Holt model, and the advanced hybrid GARCH–Holt–BPNN model, in order to identify the most effective method for monthly rainfall forecasting. Rainfall data for the period 2015–2024 were used for model training and testing. Model performance was evaluated using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). In addition, this study incorporates the development of a MATLAB-based Graphical User Interface (GUI) to facilitate interactive model implementation and visualization of forecasting results. The results indicate that the GARCH model excels in capturing data volatility, Holt’s Exponential Smoothing effectively follows short-term trends with stability, and BPNN is capable of modeling non-linear relationships despite its sensitivity to data variability. The hybrid GARCH–Holt model demonstrates improved accuracy compared to single models. Furthermore, the hybrid GARCH–Holt–BPNN model achieves the most optimal performance, with an accuracy approaching 99% and the lowest MAPE value of 1.13%, reflecting excellent generalization capability. These findings confirm that the integration of linear and non-linear methods within a hybrid framework significantly enhances rainfall forecasting accuracy and contributes to data-driven decision-making in the field of hydrometeorology.
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 (Article in Progress)
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 limitations and observer bias. This research seeks to create an automated system using Deep Learning for cell detection and classification, evaluating two key approaches: Multi-class Segmentation (single-stage) and Segmentation followed by Classification (two-stage). U-Net architecture was employed for segmentation, while MobileNetV2 and VGG16 were used for classification. The models were tested on the public IHC4BC dataset and primary data from Airlangga University Hospital (RSUA). The study also evaluated the impact of Resizing versus Tiling data processing strategies. Experimental results showed that while MobileNetV2 and VGG16 classification models achieved a high testing accuracy of 98.80%, the two-stage integrated system revealed 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 and distinguished cell types simultaneously, achieving the lowest positive cell MAE of 18.46 and a negative cell MAE of 1.66. This study concluded that multi-class segmentation with a Tiling strategy was 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 (Article in Progress)
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 in Real-Time Strategy (RTS) games, directly impacting gameplay quality and player experience. However, implementing these systems presents significant challenges, particularly in balancing rigid formation structure with pathfinding efficiency in dynamic environments containing 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 and 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 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 (Article in Progress)
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 required mechanisms that were capable of verifying identity without exposing confidential information. Zero-Knowledge Authentication addressed this challenge by enabling interactive identity verification without revealing secret credentials however, its performance strongly depended on the reliability of the cryptographic key generation process. This study investigated the use of Received Signal Strength as a source for cryptographic key generation and addressed the instability caused by noise and signal fluctuation in wireless channels. A preprocessing approach based on the Kalman Filter was proposed to improve the quality of Received Signal Strength measurements prior to key generation. The Kalman Filter was applied to reduce noise and enhance signal reciprocity between communicating nodes, ensuring that both parties generated identical cryptographic keys. The filtered signal values were then utilized to support the Zero Knowledge Feige-Fiat-Shamir authentication mechanism by replacing the conventional communication channel with keys derived from the preprocessed signal measurements.The performance of the proposed approach was evaluated through key consistency, entropy level, and bit mismatch rate between legitimate nodes. The experimental results showed that Kalman Filter–based preprocessing improved the stability of Received Signal Strength measurements and significantly increased the consistency of generated keys compared to unfiltered approaches. Consequently, the authentication success rate was enhanced while maintaining the confidentiality properties of Zero-Knowledge Authentication. These findings demonstrated that Kalman Filter assisted preprocessing effectively strengthened the security and reliability of cryptographic key generation for wireless Zero-Knowledge authentication systems.
Poultry Disease Classification Using EfficientNetV2-L and MobileNetV2 Based on Fecal Images Rosida Vivin Nahari; Anisyafaah; Riza Alfita
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 3, August 2026 (Article in Progress)
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i3.2648

Abstract

Poultry diseases have a significant impact on livestock productivity; therefore, early detection is crucial to prevent infection spread. Deep learning approaches have recently shown promising results in improving disease classification accuracy. Convolutional Neural Network (CNN) models can identify poultry diseases through fecal images using automatic feature extraction. This study proposes poultry disease classification using two CNN architectures, EfficientNetV2-L and MobileNetV2. Each model was trained under three scenarios: baseline, class weights, and Focal Loss, using the Poultry Diseases Detection dataset from Kaggle consisting of four classes of chicken fecal images. The experimental results show that applying Focal Loss improves model performance compared to other scenarios. The EfficientNetV2-L model with Focal Loss achieved the highest accuracy of 99.51%, precision of 99.57%, recall of 99.51%, and F1-score of 99.52%. Meanwhile, MobileNetV2 performed reasonably well with faster training time. These findings indicate that combining Focal Loss with efficient CNN architectures enhances the classification of imbalanced datasets and has the potential to be implemented in real-time poultry disease detection systems
Optimization of Retargeting Motion Capture for Remo Dance Using Fuzzy Logic Didit Prasetyo; Nugrahardi Ramadhani; Kartika Kusuma Wardani; Indriana Dwi Andiany
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 3, August 2026 (Article in Progress)
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i3.2663

Abstract

Retargeting motion capture for traditional dance animation faces challenges in maintaining biomechanical accuracy while preserving cultural expressiveness, especially when human motion data are transferred to character models with different skeletal structures. This research aims to optimize the retargeting of East Java Remo Dance through an adaptive artificial intelligence-based evaluation approach. The Remo dance movement was recorded using a multi-camera optical motion capture system and retargeted to two types of 3D characters: realistic and stylized. The evaluation was conducted using quantitative metrics (Mean Squared Error, Structural Similarity Index, Dynamic Time Warping, and Kalman Filtering) as well as a qualitative approach through Laban Movement Analysis. Subsequently, Mamdani fuzzy logic was integrated to synthesize all these parameters into the Fuzzy Retargeting Quality Score (FRQS). The results showed that the realistic character had higher movement accuracy (MSE = 0.0032; SSI = 0.89; DTW = 0.92) and obtained an FRQS value of 86.4 (very optimal category), whereas the stylized character obtained an FRQS of 71.2 (moderately optimal), reflecting a compromise between movement precision and visual appeal. The integration of fuzzy logic allows for more contextual and human-centric retargeting evaluation, as well as strengthening the dual-model approach to the preservation and education of traditional dance based on digital animation.
Federated Ensemble Learning with SHAP–LIME Interpretability for Smart Home Energy Prediction Rahma Puspitasari; Siti Sendari; Muhammad Arif Hermawan; Joshua Andrian; Ira Kumala Sari
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 3, August 2026 (Article in Progress)
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i3.2665

Abstract

The increased adoption of IoT-based Smart Home systems in Indonesia has resulted in a growing volume of device-level energy data, opening up opportunities for the development of predictive models to support efficient household electricity consumption. However, challenges related to accuracy, interpretability, and data privacy remain a major concern, especially when data is distributed across multiple devices. This study evaluates the performance of four tree-based ensemble models, namely Random Forest, Gradient Boosting, XGBoost, and LightGBM, in centralized learning and federated learning scenarios using the Indonesia Smart Home Dataset. After undergoing feature preprocessing and refinement, including the removal of Sofa Pressure and Bed Pressure due to high noise, each model was trained and evaluated using MAE, MSE, and RMSE metrics. Federated learning was implemented through the Federated Averaging (FedAvg) algorithm to maintain data privacy without the need to transfer raw data between devices. The results show that LightGBM consistently provides the best performance in both scenarios and demonstrates resilience to data fragmentation and heterogeneity. Although there was a slight increase in error in federated learning, the error values remained within an acceptable range. SHAP and LIME analyses revealed that high-power devices such as air conditioners, water pumps, rice cookers, lights, and refrigerators had the greatest contribution.
Predicting Social Media Post Engagement and Virality Using Graph Neural Network Approaches and Content-Based Features Fathimah Az Zahrah; Riska Dhenabayu; Muhammad Fajar Wahyudi Rahman; Renny Sari Dewi; Zamabhungane Hadebe Aminah
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 3, August 2026 (Article in Progress)
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i3.2686

Abstract

Social media teams increasingly rely on early signals to prioritize content, yet forecasting engagement and identifying viral posts remain difficult under temporal drift and heavy-tailed interaction counts. This study evaluated Graph Neural Network (GNN) approaches for predicting post engagement and virality from pre-posting content-based and contextual features. The Social Media Engagement Report dataset, which contained 100,000 posts across Twitter, LinkedIn, Facebook, and Instagram spanning March 2021–March 2024, was used. Post-release variables (impressions, reach, engagement rate) were excluded to prevent leakage. A homogeneous post–post graph was constructed using k-nearest-neighbor similarity in an embedding space and exact-match links on low-cardinality context. Ridge/Logistic Regression, Random Forest, and XGBoost as the baselines were compared against GraphSAGE and GAT under a chronological train, validation, and test split. Regression used MAE, RMSE, and R2, while virality classification used ROC-AUC, PR-AUC, and Precision at the top 1% ranked posts. GraphSAGE yielded the strongest virality screening, achieving ROC-AUC = 0.66, PR-AUC = 0.54–0.56, and Precision@1% up to 0.75, substantially above non-graph baselines. For regression, GAT produced the lowest errors despite a negative R², indicating limited explained variance. Overall, similarity-graph GNNs are most effective for early virality identification, whereas exact count prediction remains challenging in a strictly pre-posting, time-aware setting.

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