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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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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.
Collaborative Filtering Modification Technology for Recommendation Systems in Smart Digital Agribusiness Marketplace Arif Prabowo, Setya Budi; Subiyanto; Azis Salim, Nur
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.v10i3.2264

Abstract

The rapid transformation in the agribusiness sector, driven by globalization and digitalization, necessitates the adoption of intelligent systems to enhance performance, market accessibility, and decision-making processes. Despite the growing use of personalized recommender systems in e-commerce, geographical context remains insufficiently integrated into recommendation processes. This lack of geolocation awareness diminishes recommendation relevance and accuracy by overlooking geographical factors that influence user preferences. To address this limitation, this work aims to enhance the performance of recommendation systems in agricultural e-commerce by incorporating geolocation context through the integration of the Geo-Mod Neuro Collaborative Filtering (GMNCF) model into an Android-based application for agricultural products. The GMNCF model improves collaborative filtering by incorporating geographical region data to capture spatial user preferences and reduce data sparsity. Using Graph Neural Networks (GNNs), the model captures complex relationships among users, items, and geographic regions to generate more accurate recommendations. Experimental results reveal that GMNCF consistently delivers substantial performance improvements over baseline models such as NGCF, GC-MC, ASMG, and GCZRec. Compared to the strongest baselines, GMNCF demonstrates relative gains of approximately 4.9% in Precision, 5.9% in Recall, 5.6% in F1-Score, and 5.7% in Hit Rate. These improvements underscore the model’s effectiveness in capturing spatially influenced user preferences and strengthening the relevance of recommendations in the agribusiness e-commerce system. Furthermore, user testing with diverse respondents indicates high levels of satisfaction, particularly regarding location-based recommendation features and accessibility. These findings highlight the effectiveness of incorporating geographical region data into recommendation systems, which is particularly beneficial for geographically fragmented agribusiness markets.
Improved Chaotic Image Encryption on Grayscale Colorspace Using Elliptic Curves and 3D Lorenz System Sinaga, Daurat; Jatmoko, Cahaya; Astuti, Erna Zuni; Rachmawanto, Eko Hari; Abdussalam, Abdussalam; Pramudya, Elkaf Rahmawan; Shidik, Guruh Fajar; Andono, Pulung Nurtantio; Doheir, Mohamed
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.v10i3.2251

Abstract

Digital data, especially visual content, faces significant security challenges due to its susceptibility to eavesdropping, manipulation, and theft in the modern digital landscape. One effective solution to address these issues is the use of encryption techniques, such as image encryption algorithms, that ensure the confidentiality, integrity, and authenticity of digital visual content. This study addresses these concerns by introducing an advanced image encryption method that combines Elliptic Curve Cryptography (ECC) with the 3D Lorenz chaotic system to enhance both security and efficiency. The method employs pixel permutation, ECC-based encryption, and diffusion using pseudo-random numbers generated by the Lorenz 3D system. The results show superior performance, with an MSE of 3032 and a PSNR of 8.87 dB, as well as UACI and NPCR values of 33.34% and 99.64%, respectively, indicating strong resilience to pixel intensity changes. During testing, the approach demonstrated robustness, allowing only the correct key to decrypt images accurately, while incorrect or modified keys led to distorted outputs, ensuring encryption reliability. Future work could explore extending the method to color images, optimizing processing for larger datasets, and incorporating additional chaotic systems to further fortify encryption strength.
Optimized Visualization of Digital Image Steganography using Least Significant Bits and AES for Secret Key Encryption Jatmoko, Cahaya; Sinaga, Daurat; Lestiawan, Heru; Astuti, Erna Zuni; Sari, Christy Atika; Shidik, Guruh Fajar; Andono, Pulung Nurtantio; Yaacob, Noorayisahbe Mohd
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.v10i3.2252

Abstract

Data hiding is a technique used to embed secret information into a cover medium, such as an image, audio, or video, with minimal distortion, ensuring that the hidden data remains imperceptible to an observer. The key challenge lies in embedding secret information securely while maintaining the original quality of the host medium. In image-based data hiding, this often means ensuring the hidden data cannot be easily detected or extracted while still preserving the visual integrity of the host image. To overcome this, we propose a combination of AES (Advanced Encryption Standard) encryption and Least Significant Bit (LSB) steganography. AES encryption is used to protect the secret images, while the LSB technique is applied to embed the encrypted images into the host images, ensuring secure data transfer. The dataset includes grayscale 256x256 images, specifically "aerial.jpg," "airplane.jpg," and "boat.jpg" as host images, and "Secret1," "Secret2," and "Secret3" as the encrypted secret images. Evaluation metrics such as Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Unified Average Changing Intensity (UACI), and Number of Pixels Changed Rate (NPCR) were used to assess both the image quality and security of the stego images. The results showed low MSE (0.0012 to 0.0013), high PSNR (58 dB), and consistent UACI and NPCR values, confirming both the preservation of image quality and the effectiveness of encryption for securing the secret data.
Efficient Thoracic Abnormalities Detection Using Mobile Deep Learning Models Bauravindah, Achmad; Fudholi, Dhomas Hatta; Wahyuningrum, Rima Tri
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.v10i3.2268

Abstract

Indonesia faces a critical shortage of radiologists, with only 1.2 radiologists per 100,000 individuals. This shortage leads to delays in diagnosing thoracic abnormalities such as pneumothorax, cardiomegaly, nodule/mass, consolidation, and infiltration. Chest X-ray (CXR) interpretation remains challenging due to overlapping radiological features, necessitating AI-assisted solutions. This study evaluates three lightweight deep learning models—MobileNetV2, ShuffleNetV2, and EfficientNetB0—for automated thoracic abnormality detection using the ChestX-ray8 dataset. We assessed model performance using accuracy, precision, recall, F1-score, and AUC-ROC, selecting the best model based on the highest per-fold F1-score. EfficientNetB0 emerged as the top-performing model, achieving a macro-average F1-score of 0.556 and AUC-ROC of 0.765, outperforming MobileNetV2 (0.494, 0.719) and ShuffleNetV2 (0.481, 0.713). Grad-CAM analysis revealed strong localization for pneumothorax and consolidation but misclassifications in cardiomegaly and nodule/mass detection due to poor feature differentiation. The findings highlight EfficientNetB0’s potential as an AI-assisted diagnostic tool for low-resource settings while also underscoring the need for segmentation-based pretraining and multi-scale feature extraction to enhance detection accuracy. Future work should focus on optimizing sensitivity to subtle abnormalities and ensuring clinical trust through improved interpretability techniques.

Page 51 of 54 | Total Record : 536

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