Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control
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Game Development of Banjar Archive for Interactive Cultural Education Ultilizing Large Language Models
No full textThe preservation of Banjar cultural heritage is threatened by globalization and the fading interest of younger generations. This study addressed these challenges by developing an interactive educational game using the Game Development Life Cycle (GDLC) framework and integrating Large Language Models (LLMs) for adaptive and immersive player interactions. The six stages of GDLC namely initiation, pre-production, production, testing, beta, and release were systematically applied, resulting in a game that blends dynamic narratives to engage players while educating them about Banjar culture. Black Box Testing verified 14 test scenarios that all passed successfully, ensuring system stability and reliability. Additionally, user experience evaluation using the Game Experience Questionnaire (GEQ) highlighted high levels of immersion (4.936), competence (4.448), flow (3.124) and positive affect (4.976) among players, with minimal reported tension (1), challenge (1.744) and negative affect (1.07). These results demonstrated that the game successfully balances educational goals with engaging gameplay, fostering meaningful connections to Banjar heritage. By leveraging LLM technology, the game enhances interactivity, offering an innovative approach to Banjar cultural preservation in the digital era. This research extends the existing body of knowledge on AI-driven gamification strategies in heritage conservation with a specific focus on Banjar culture
Performance Evaluation of Outgoing Interface Selection Method on Fortigate SD-WAN for Network Optimization
Get PDFReliable and high-performance network services are essential to facilitate communication between parent companies and subsidiaries as well as among the subsidiaries themselves. Challenges arise in managing and optimizing outgoing interface selection in an effective and reliable Software-Defined Wide Area Network (SD-WAN) environment. This research evaluates four outgoing interface selection methods, namely Manual, Best Quality, Lowest Cost, and Maximize Bandwidth (SLA), using a tree-based network topology simulated in GNS3 with FortiGate devices as part of the simulation. The results show that under simulated disturbances, such as limiting a single connection line to 10 kbps, the Manual, Best Quality, and Lowest Cost methods perform worse than the Maximize Bandwidth method. In contrast, the Maximize Bandwidth method outperformed the others, achieving only 0% packet loss, 22.275 ms one-way delay, and 7.03 ms jitter, while maintaining the ITU-T G.1010 standard at the preferred level. These findings highlight the reliability and effectiveness of the Maximize Bandwidth method in ensuring consistent data transmission even under fault conditions, while providing practical guidance for network engineers in configuring SD-WAN for uninterrupted high-quality network services in complex business environments
Integrating ISSM and SCT into the TAM Framework: A Conceptual Model and Empirical Study on E-Government Services
No full textProposing 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
Intelligent Traffic Management System Using Mask Regions-Convolutional Neural Network
No full textUrban 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
Optimized BiLSTM-Dense Model for Ultra-Short-Term PV Power Forecasting
Get PDFThe growing integration of photovoltaic (PV) systems into power grids poses challenges due to the inherent variability in PV output, particularly during rapid weather changes. While existing forecasting methods often struggle to capture these fluctuations, accurate ultra-short-term PV power prediction is critical for grid stability. The study aims to develop an optimized BiLSTM-Dense model that enhances forecasting accuracy by incorporating an additional dense layer. The model is designed to improve forecasting performance over a 30-second horizon. It utilizes a dataset of solar irradiance, PV output power, surface temperature, ambient temperature, humidity, and wind speed, collected in late 2023. Data preprocessing involved normalization and smoothing techniques to enhance robustness. Hyperparameter optimization was performed using grid search. Evaluation results demonstrate the superiority of the proposed model, achieving an MAE of 0.00271 and an RMSE of 0.00806 when paired with the Adam optimizer and Swish activation function. Compared to standard BiLSTM, the BiLSTM-Dense achieved MAE and RMSE improvements of 0.52% and 2.19%, respectively. It also outperformed the LSTM model with reductions of 4.00% in MAE and 2.65% in RMSE, and significantly surpassed ARIMA, reducing MAE by 98.87% and RMSE by 97.21%. These findings highlight the model’s ability to capture complex, non-linear dependencies in PV output data, outperforming conventional approaches like ARIMA, which rely on linear assumptions, and simpler architectures like LSTM, which lack bidirectional context integration
Land Price Distribution Prediction in Jakarta Using Support Vector Machine with Feature Expansion and Kriging Interpolation
No full textFluctuations 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 Livin' by Mandiri Customer Satisfaction Using MLP with BM25 and TF-IDF Feature Weighting
No full textThe 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
No full textGame-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
Implementation of Convolutional Recurrent Neural Network for Vehicle Number Plate Identification in Raspberry Pi Based Parking System
No full textThe rapid growth of vehicles in Indonesia has created significant challenges in managing parking facilities. To address this issue, this study proposes an intelligent parking system based on automatic license plate character recognition. The system employs YOLOv8 (You Only Look Once) for license plate region detection and CRNN (Convolutional Recurrent Neural Network) for alphanumeric character recognition. Its architecture integrates a Raspberry Pi, camera module, and servo motor to enable automated license plate detection and recognition during vehicle entry and exit. YOLOv8 generates bounding boxes to isolate license plate regions, which are then processed as input for CRNN. The CRNN extracts visual features through convolutional layers and captures sequential relationships among characters using recurrent layers. The entire pipeline is deployed on Raspberry Pi with TensorFlow Lite to ensure efficient computation in resource-constrained environments. Experimental results demonstrate that YOLOv8 achieved a detection accuracy of 94.69%, with a precision of 98.32%, recall of 96.25%, and F1-score of 97.27%, while CRNN reached a character recognition accuracy of 93.8% across 30 license plates. Although some recognition errors occurred, such as misclassifying ‘G’ as ‘C’, 'W' as 'H', and 'Q' as 'O', the proposed system proved effective and feasible for embedded smart parking applications
Collaborative Filtering Modification Technology for Recommendation Systems in Smart Digital Agribusiness Marketplace
No full textThe 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