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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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Bamboo Diameter Detection System Based on Image Processing as a Pre-Assessment for an Automated Bamboo Splitting Technology Hakim, Sinta Uri El; Arifianto, Rokhmat; Sugiyanto; Pratiwi, Ilham Ayu Putri; Bahari, Galuh; Krisnaputra, Radhian
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 2, May 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i2.2170

Abstract

Bamboo is recognized for its eco-friendly attributes and rapid growth, serves as a promising sustainable alternative to wood. However, the high production cost of laminated bamboo remains a major challenge due to labor-intensive processes, particularly manual splitting, which affects efficiency and labor costs. To overcome this issue, this study presents an automated bamboo diameter measurement system that leverages Canny Edge Detection and Hough Transform to ensure precise and uniform slat dimensions. A dataset of 100 bamboo images with diameters ranging from 11 - 13 cm was utilized for training and testing. The system achieved a high accuracy, with a coefficient of determination (R²) of 0.973, demonstrating strong predictive reliability. Furthermore, Bayesian Optimization was applied to fine-tune parameters, resulting in an optimized configuration for both Canny Edge Detection and Hough Transform. The proposed system reduces dependence on manual labor, thereby lowering production costs and improving overall manufacturing efficiency. Automation in the bamboo splitting process ensures consistent and precise slat dimensions, supporting scalability and enhancing the economic feasibility of laminated bamboo production. The findings of this study provide a practical and sustainable solution to optimize production, making laminated bamboo a more viable and competitive material in the industry.
Clustering of High School Quality Using Fuzzy C-Means in the Special Region of Yogyakarta Province Ilmi, Lilin Rofiqatul; Haryanto; Sumunaringtyas, Maria
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 2, May 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i2.2187

Abstract

This research aims to reveal the results of clustering high school quality using fuzzy c-means in the Special Region of Yogyakarta Province. This research is quantitative and descriptive. Data collection was conducted through documentation. The research data are secondary data from the 2023 high school education report card. The sample consisted of 51 schools, which were determined using the proportional stratified random sampling. Data analysis was performed using the quantitative descriptive method and fuzzy c-means. The results of the study are clustering on the main indicator data producing three clusters: cluster 1 consists of 11 private schools accredited A and B, cluster 2 consists of 22 public and private schools accredited A, and cluster 3 consists of 18 schools accredited A, B, and C. Cluster 2 excels with the overall best performance, cluster 1 has moderate performance with several areas needing improvement, such as instructional leadership, the use of information technology for budget management, and inclusiveness, and cluster 3 shows the lowest performance, requiring significant attention and improvement in almost all aspects, especially literacy, numeracy, instructional leadership, and the use of information technology for budget management. Cluster 3, which had the lowest performance, showed an urgent need for improvement in almost all aspects.
Development of Lung Cancer Risk Screening Tool with Causal Discovery Model Evaluation Approach Wibowo, Sandi; Mutaqin, Jatniko Nur; Apriansyah, Ari; Komiyatu, Muhamad; Soekidjo, Gusti Ayu Putri Saptawati
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 2, May 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i2.2188

Abstract

Causal graph discovery approaches in healthcare for detecting high-risk diseases have been more widely applied in the last decade. The main challenge in causal graph discovery in healthcare data is the complexity of big data, which requires appropriate algorithms to reveal causal relationships between variables. This study focuses on evaluating the performance of seven causal discovery models—Peter-Clark (PC), Greedy Equivalent Search (GES), Direct LiNGAM, Directed Acyclic Graph-Graph Neural Network (DAG-GNN), Greedy Sparsest Permutation (GraSP), and Recursive Causal Discovery (RCD)—on opensource healthcare datasets. The model performance was evaluated using the Structural Intervention Distance (SID), Structural Hamming Distance (SHD), Matthews Correlation Coefficient (MCC), and Fobernius Norm (FN) metrics. The evaluation results conclusively show that the GES model performs best on low-complexity datasets. Meanwhile, the DAG-GNN model offers consistent performance on high-complexity data with MCC values ranging from 0.77 to 0.88. The application of the GES model for lung cancer risk screening, based on user question responses, demonstrated effectiveness by measuring MCC, SID, and SHD scores between the reference adjacency metrics and the resulting screening metrics.
Integrating Adaptive Sampling with Ensembles Model for Software Defect Prediction Yusuf, Muhammad; Haq, Arinal; Rochimah, Siti
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 2, May 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i2.2191

Abstract

Handling class imbalance is a challenge in software defect prediction. Imbalanced datasets can cause bias in machine learning models, hindering their ability to detect defects. This paper proposes an integration of Adaptive Synthetic Sampling (ADASYN) and ensemble learning methods to improve prediction accuracy. ADASYN enhances the handling of imbalanced data by generating synthetic samples for hard-to-classify instances. At the same time, the ensemble stacking technique leverages the strengths of multiple models to reduce bias and variance. The machine learning models used in this study are K-Nearest Neighbors (KNN), Decision Tree (DT), and Random Forest (RF). The results demonstrate that ADASYN, combined with ensemble stacking, outperforms the traditional SMOTE technique in most cases. For instance, in the Ant-1.7 dataset, ADASYN achieved a stacking accuracy of 90.60% compared to 89.32% with SMOTE. Similarly, in the Camel-1.6 dataset, ADASYN achieved 91.56%, slightly exceeding SMOTE’s 91.32%. However, SMOTE performed better in simpler models like Decision Tree for certain datasets, highlighting the importance of choosing the appropriate resampling method. Across all datasets, ensemble stacking consistently provided the highest accuracy, benefiting from ADASYN's adaptive resampling strategy. These results underscore the importance of combining advanced sampling methods with ensemble learning techniques to address class imbalance effectively. This approach improves prediction accuracy and provides a practical framework for reliable software defect prediction in real-world scenarios. Future work will explore hybrid techniques and broader evaluations across diverse datasets and classifiers.
Design and Simulation of Battery Charging System with Constant Temperature–constant Voltage Method Sudiharto, Indhana; Wahjono, Endro; Sasetyo, Muhammad Yudha; Suryono; Budikarso, Anang
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 2, May 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i2.2194

Abstract

Batteries are essential to many contemporary applications, including electric cars and portable electronics. Overheating and charging time efficiency are the two biggest issues with battery charging. Overheating presents safety hazards and hastens battery deterioration. Due to their inability to regulate temperature, conventional charging techniques like Constant Current - Constant Voltage (CC-CV) result in excessive temperature rises during battery charging, which shortens battery life. A novel approach that helps lessen excessive temperature rises is the Constant Temperature - Constant Voltage (CT-CV) method, according to researchers. In order to avoid excessive temperature increases during the initial charging, the CT technique initially regulates the applied temperature. Second, to guarantee full capacity without causing damage to the battery, the CV technique is used to maintain a steady voltage. A fuzzy logic controller (FLC) control system is used to regulate the temperature and current at the DC-DC converter's output. The FLC control system's goal is to control the duty cycle such that the buck converter's output is 65V 11.5A. The simulation results show that the CT-CV method can reduce the increase in temperature in the battery with an average temperature during the battery charging process of 23.57° C with fuzzy control and 23.71° C with PI control. In addition, by comparing two control systems with the CT-CV method, namely PI and fuzzy, it was found that the fuzzy method was able to accelerate battery charging by 4.16% compared to the PI control.
Optimizing Autonomous Navigation: Advances in LiDAR-based Object Recognition with Modified Voxel-RCNN Firman; Satyawan, Arief Suryadi; Susilawati, Helfy; Haqiqi, Mokh. Mirza Etnisa; Artemysia, Khaulyca Arva; Sopian, Sani Moch; Wijaya, Beni; Samie, Muhammad Ikbal
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 2, May 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i2.2199

Abstract

This study aimed to enhance the object recognition capabilities of autonomous vehicles in constrained and dynamic environments. By integrating Light Detection and Ranging (LiDAR) technology with a modified Voxel-RCNN framework, the system detected and classified six object classes: human, wall, car, cyclist, tree, and cart. This integration improved the safety and reliability of autonomous navigation. The methodology included the preparation of a point cloud dataset, conversion into the KITTI format for compatibility with the Voxel-RCNN pipeline, and comprehensive model training. The framework was evaluated using metrics such as precision, recall, F1-score, and mean average precision (mAP). Modifications to the Voxel-RCNN framework were introduced to improve classification accuracy, addressing challenges encountered in complex navigation scenarios. Experimental results demonstrated the robustness of the proposed modifications. Modification 2 consistently outperformed the baseline, with 3D detection scores for the car class in hard scenarios increasing from 4.39 to 10.31. Modification 3 achieved the lowest training loss of 1.68 after 600 epochs, indicating significant improvements in model optimization. However, variability in the real-world performance of Modification 3 highlighted the need for balancing optimized training with practical applicability. Overall, the study found that the training loss decreased up to 29.1% and achieved substantial improvements in detection accuracy under challenging conditions. These findings underscored the potential of the proposed system to advance the safety and intelligence of autonomous vehicles, providing a solid foundation for future research in autonomous navigation and object recognition.
Analysis of Mental Health Disorders via Social Media Mining Using LSTM and Bi-LSTM Kholifah, Binti; Syarif, Iwan; Badriyah, Tessy
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 3, August 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i3.2205

Abstract

Mental health disorders are a growing global concern, with many individuals lacking early detection and appropriate treatment. Mental illness can impact a person’s quality of life and often goes undetected until symptoms worsen. One contributing factor to this problem is the limited ways to detect mental disorders in their early stages. Social media, especially platform X, offers the potential to analyze users’ emotional expressions that may indicate a mental disorder, such as depression or anxiety. Psychological symptoms can be explored more broadly using Natural Language Processing. This study optimizes several text preprocessing techniques to extract meaningful information from social media text. To convert words into numerical vectors, several word embedding methods are used, such as Word2Vec, FastText, and GloVe. Meanwhile, the classification process is carried out using LSTM and Bi-LSTM because they are considered capable of studying data sequence patterns, such as sentence structure, effectively. The results show that the addition of expanding contractions, emoticon handling, negation handling, repeated character handling, and spelling correction in the preprocessing text can improve the model performance. In addition, Bi-LSTM with pre-trained FastText shows better results than the other methods in all experiments, achieving 86% accuracy, 87.5% precision, 84% recall, and 85.71% F1-Score.
Land Price Distribution Prediction in Jakarta Using Support Vector Machine with Feature Expansion and Kriging Interpolation Pilar Gautama, Hadid; Prasetiyowati, Sri Suryani; Sibaroni, Yuliant
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 3, August 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i3.2216

Abstract

Fluctuations in land prices over time are significant, especially in big cities, one of which is Jakarta. The increase in land prices is influenced by high demand, location-related needs, ease of access to various public facilities and population density. Uncontrolled prices and lack of information about the distribution of land prices cause buyers to acquire land that does not meet their needs. This study develops a land price distribution prediction system for Jakarta for 2025-2026 using Support Vector Machine (SVM) with time-based feature expansion and spatial interpolation. The SVM model with an RBF kernel demonstrated superior performance, achieving 93.14% accuracy for 2025 predictions using the t-1 model. For 2026 predictions, the t-2 model achieved 83.33% accuracy. This approach involves utilizing one to two years of historical data and systematically selected features, ensuring more accurate and relevant predictions. Ordinary kriging interpolation visualizations revealed a significant shift in land price distribution patterns, indicating a decline in affordable land availability and an increase in high-value properties across Jakarta. The integration of SVM and kriging interpolation, coupled with comprehensive evaluation metrics, provides a robust methodological framework for predicting urban land price distributions. This system offers practical implications for informed decision-making in Jakarta's dynamic land market, enabling stakeholders to make efficient, budget-based property decisions. The research contributes significantly to urban planning by providing a comprehensive tool for understanding and predicting land price trends, which can assist various stakeholders in making informed property investment decisions.
Classification of Arrhythmia Electrocardiogram Signals Using Kernel Principal Component Analysis and Naive Bayes Melinda, Melinda; Farhan; Irhamsyah, Muhammad; Miftahujjannah, Rizka; D Acula, Donata; Yunidar, Yunidar
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 3, August 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i3.2219

Abstract

Arrhythmia is a cardiovascular disorder commonly detected through electrocardiogram (ECG) signal analysis. However, classifying arrhythmias based on ECG signals remains challenging due to signal complexity and individual variability. This study aims to develop a more accurate and efficient method for arrhythmia classification. The proposed method utilizes Kernel Principal Component Analysis (KPCA) and the naïve Bayes algorithm to classify arrhythmic ECG signals. KPCA is chosen for its ability to reduce data dimensionality, facilitating the processing of complex ECG signal and improving classification accuracy by minimizing noise. The naïve Bayes algorithm is chosen for its simplicity and computational speed, as well as its effective performance, even with limited data. ECG signals are processed using KPCA to reduce data dimensionality and extract relevant features. Subsequently, the naïve Bayes algorithm is then applied to classify the ECG signals into four categories: Premature Atrial Contraction (PAC), Premature Ventricular Contraction (PVC), Left Bundle Branch Block (LBBB), and Right Bundle Branch Block (RBBB).  The model's performance is evaluated using metrics such as accuracy, sensitivity, specificity, precision, and F1-score. The naïve Bayes model achieves an overall accuracy of 97.67%, with the highest performance observed in the RBBB class at 99.33%. Additionally, the F1-scores across all classes range from 96.62% to 98.57%, demonstrating the model's capability in detecting arrhythmias effectively. These results indicate that the combination of KPCA and naïve Bayes is effective for arrhythmic ECG signals classification.
Analysis of Public Opinion on The Governor Candidate Debate Using LDA and IndoBERT Chamid, Ahmad Abdul; Nindyasari, Ratih; Azizah, Noor; Hariyadi, Ahmad
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 3, August 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i3.2221

Abstract

The gubernatorial candidate debate was broadcast live streaming through various YouTube channels, which attracted public attention. Many discussions and conversations appeared in the comments section of each YouTube channel that broadcasted the debate. Given the numerous public discussions, it is undoubtedly interesting to analyze the contents of the conversations, as well as the expectations and feedback from the public. However, analyzing conversations in the form of text data will be challenging using conventional methods. Therefore, in this study, public opinion will be analyzed using the topic identification and sentiment classification approaches. Topic identification is conducted to obtain accurate information about what the public is discussing, while sentiment classification is used to determine whether each comment contains positive or negative sentiments. This research is novel because it utilizes data collected from various major media YouTube channels and includes a qualitative analysis of the findings. This study uses public comment data taken from the KPU, NarasiTV, and KompasTV YouTube channels; the results obtained included 4,147 data points. Data preprocessing involves identifying topics using the LDA method, evaluating the LDA model, performing sentiment classification using IndoBERT, and visualizing the results of the public opinion analysis. The results revealed five topics with a perplexity value of -7.7909 and a coherence score of 0.5109. In addition, topic 4 is the most dominant compared to other topics, with 1,146 comments classified as positive sentiment and 504 classified as negative sentiment. Topic 4 reflects how religion, culture, and frequently mentioned figures are perceived and discussed by the public, especially in relation to the gubernatorial election (pilgub) or gubernatorial candidate debates.

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