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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 15 Documents
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Search results for , issue "Vol. 10, No. 3, August 2025" : 15 Documents clear
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.
Integrating ISSM and SCT into the TAM Framework: A Conceptual Model and Empirical Study on E-Government Services Prasetyo, Beny; Ayuningtiyas, Rindi; Adnan, Fahrobby
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.v10i3.2224

Abstract

Proposing and developing the right model is necessary to increase the effectiveness and success of e-government service implementation. Combining models that highlight technological aspects and psychological issues can generate satisfaction and improve service quality. This research develops and tests a combination of the Information System Success Model (ISSM), Technology Acceptance Model (TAM), and Social Cognitive Theory (SCT). This research aims to determine the results of the fit model test for the proposed model and empirically test the factors that significantly affect the success of e-government through satisfaction. To validate the conceptual model, PLS-SEM is used. The type of research conducted is quantitative. The sample used to test the model consisted of SiKeren service users in the Jember Regency Government, totaling 260 samples, determined using Hair's theory and probability sampling techniques, particularly simple random sampling. The results of this study indicate that the proposed model is suitable. The Standardized Root Mean Square Residual (SRMR) value of 0.070 or < 0.08 indicates that the model is considered to be supported by the measured data. The Goodness of Fit (GoF) value is 0.686, indicating a strong match between the observed data and the developed model. The model effectively captures the R-Square value for Perceived Ease of Use, Perceived Usefulness, and Satisfaction, which have medium criteria with values of 0.595, 0.724, and 0.606, respectively. Of the 16 hypotheses proposed, 12 were accepted, and 4 were rejected. This study found that Perceived Ease of Use and Perceived Usefulness are influenced by the constructs of the IS success model, except that the system quality variable on Perceived Usefulness is not significant. This study also found that TAM factors significantly influence computer self-efficacy and satisfaction. The anxiety variable is not significant to the TAM factor and the cognitive theory of Computer Self-Efficacy. The overall relationship between the analyzed variables has a small effect size.
Cybersecurity Management Strategies for Smart Cities in Indonesia: Cultural Factors and Implementation Challenges Alam, RG Guntur; Faruq, Amrul; Effendy, Machmud
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.v10i3.2226

Abstract

The implementation of smart cities in Indonesia presents significant cybersecurity challenges, particularly amid bureaucratic complexity, low digital literacy, and limited institutional capacity. This study explores cybersecurity management strategies in the context of Jakarta Smart City (JSC), emphasizing sociotechnical dynamics and embedded cultural-institutional factors. Employing a qualitative approach and the Actor-Network Theory (ANT) framework, this research examines four key moments in the stabilization of cybersecurity networks: problematization, interessement, enrollment, and mobilization. Empirical findings reveal that challenges such as fragmented governance, security awareness gaps, and limitations in technological adaptation are addressed through context-specific strategies. These include regulatory reforms, multi-stakeholder collaboration, hybrid governance models, and the localization of international standards, particularly ISO/IEC 27001. The study also incorporates Indonesia’s Personal Data Protection Law (Law No. 27/2022) as a foundational legal framework that supports the integration of regional cybersecurity policies. Rather than focusing solely on technical solutions, this research emphasizes the importance of aligning cybersecurity strategies with local norms, leadership structures, and user practices. The proposed strategic model contributes to the cybersecurity governance literature by integrating ANT perspectives with empirical insights from a developing country. It offers a locally adapted and scalable framework to guide policymakers and smart city administrators in building resilient and culturally sensitive cybersecurity systems.
Intelligent Traffic Management System Using Mask Regions-Convolutional Neural Network Pasha, Muhammad Kemal; Atmadja, Aldy Rialdy; Firdaus, Muhammad Deden
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.v10i3.2233

Abstract

Urban centers worldwide continue to face challenges in traffic management due to outdated traffic signal infrastructure. This study aims to develop an intelligent traffic management system by implementing the Mask Regions-Convolutional Neural Network (MR-CNN) algorithm for real-time vehicle detection and traffic flow optimization. Utilizing the CRISP-DM framework, this research processes CCTV footage from the Pasteur-Pasopati intersection in Bandung to identify and quantify vehicles dynamically. The proposed system leverages an enhanced Mask R-CNN model with a ResNet-50 FPN backbone to improve detection accuracy. Experimental results demonstrate an 80% vehicle detection accuracy, with a macro-average precision of 0.89, recall of 0.83, and an F1-score of 0.82. These findings highlight the system’s capability to replace conventional fixed-time traffic signals with a more adaptive approach, adjusting green light durations based on real-time traffic density. The proposed solution has significant practical implications for reducing congestion and improving traffic flow efficiency in urban environments.
UI/UX Design for AR Card Game: Enhancing English Vocabulary Learning with Augmented Reality Krisdiawan, Rio; Sugiharto, Tito; Amalia Asikin, Nida; Rohmawati, Lutfi
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.v10i3.2240

Abstract

This study aims to develop and evaluate an Augmented Reality (AR)-based learning tool in the form of an AR Card Game to enhance English vocabulary acquisition among third-grade elementary school students, specifically on the topic of “Fruits and Vegetables.” The development process employed the User-Centered Design (UCD) methodology to ensure that the user interface and user experience (UI/UX) were aligned with the cognitive characteristics and needs of the target users. The prototype, designed using Figma, integrates interactive features including 3D object visualization, audio pronunciation guides, gamified elements, and physical card-based AR interaction. Evaluation was conducted through student questionnaires, teacher interviews, and classroom observations. The results indicate that the AR Card Game was positively received. A total of 85.07% of students reported improved understanding through 3D visuals, while 89.55% found the audio helpful for pronunciation. The gamification feature achieved a mean score of 4.18 (SD = 0.73), and a one-sample t-test revealed a statistically significant difference from the neutral score (p < 0.001), confirming its motivational impact. The coefficient of variation (17.48%) indicates consistent student responses. Teacher feedback also supported the tool’s effectiveness, although recommendations were made to improve navigation and enhance the evaluation component. Limitations of this study include its short-term implementation and focus on a single thematic domain. Future research is recommended to investigate long-term engagement, adaptive difficulty mechanisms, and the scalability of AR-based learning in broader curricular contexts. The findings underscore the potential of AR Card Games as effective and engaging tools for early language education in digital learning environments.
Classification of Livin' by Mandiri Customer Satisfaction Using MLP with BM25 and TF-IDF Feature Weighting Mardiah, Aina; Dillah, Salsa; Surianto, Dewi Fatmarani; Fadilah, Nur; Zain, Satria Gunawan
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.v10i3.2248

Abstract

The increasing use of mobile banking applications such as Livin' by Mandiri requires an analysis of customer satisfaction based on user reviews. This study classifies customer satisfaction levels using the Multi-Layer Perceptron (MLP) algorithm with two feature extraction methods, namely BM25 and TF-IDF. A total of 1,143 reviews were collected from the Google Play Store and App Store. Three test scenarios were applied: (1) comparison of feature extraction methods, (2) application of Synthetic Minority Over-Sampling Technique (SMOTE), and (3) application of Synonym Replacement-based Easy Data Augmentation (EDA) technique. The evaluation results show that the combination of BM25 and data augmentation produces the highest performance, with 97% accuracy and 98% precision, recall, and F1-score, respectively. BM25 proved to be more effective in understanding the context of reviews, while data augmentation improved the quality of representation, especially for minority classes such as neutral sentiment. These findings make a significant contribution to the improvement of Livin' by Mandiri digital services and serve as a reference for the development of review-based satisfaction classification systems in the digital banking sector.
Effectiveness of a Competitive Educational Game with a Game Controller in English Game-based Language Learning Mahardhika, Galang Prihadi; Masitha, Astari Husna; Kamada, Masaru
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.v10i3.2253

Abstract

Game-based language learning has emerged as a promising approach to language learning activities. Despite its potential, concepts for implementing game-based language learning that emphasize player-to-player and player-to-game interactions have not been widely adopted. This study presents an educational game as a game-based language learning application that incorporates face-to-face interaction concepts and competitive game approaches to enhance player-to-player interaction. Additionally, the game utilizes a specially designed game controller to improve player-to-game interaction. The impact of the proposed educational game on the students' learning experience, gaming experience, and motivation was evaluated through a process involving 42 high school students (14 females and 28 males). The findings suggest that integrating concepts of face-to-face interaction in competitive game scenarios and the game controller design proposed in this study fosters social interactions among players, positively influencing students' learning experience, gaming experience, and motivation. Furthermore, the findings reveal that students prefer game controllers with microswitch buttons because they provide a physical feel that reduces errors during gameplay. This underscores the importance of ergonomic, easy-to-use game controller designs that minimize errors when playing educational games. By focusing on the interplay between player-to-player and player-to-game interactions, this study provides insight into designing interactive educational games that utilize interaction technology, particularly for language learning.

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