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DESIGNING USER EXPERIENCES IN CASUAL GAMES TO ENHANCE PRODUCT KNOWLEDGE
Get PDFThe widespread adoption of mobile platforms has transformed the gaming industry, making casual games highly popular due to their accessibility via smartphones and tablets. Beyond entertainment, casual games now serve as effective educational and marketing tools for delivering product knowledge. This study explores how user experience (UX) design can enhance product education in casual games by focusing on game mechanics, UX principles, narrative engagement, and product placement. Using a Design-Based Research (DBR) approach, this study develops, tests, and refines interactive experiences to ensure the effective implementation of design elements. Testing with 50 participants showed a 30% improvement in product recall after playing, along with high satisfaction levels regarding game usability and engagement. Participants also demonstrated improved time management skills and emotional connection to the game content. The game integrates challenges and activities designed to build cognitive and emotional engagement. Artificial intelligence (AI) technology is utilized through Unreal Engine to create a realistic and immersive environment. By incorporating product information into engaging gameplay, the game serves as both an educational and entertainment tool. This research provides practical insights for game developers, marketers, and educators on integrating educational content into casual games. By leveraging AI, user testing, and advanced UX strategies, casual games can become effective tools for game-based marketing and education. This game significantly enhances product knowledge retention, user engagement, and practical skills
COMPARISON OF ENSEMBLE METHODS FOR DECISION TREE MODELS IN CLASSIFYING E. COLI BACTERIA
Get PDFCertain strains of Escherichia coli (E. coli) can cause serious illness, so identifying dangerous strains with high accuracy is a priority in supporting public health and food safety. However, traditional machine learning methods, such as Decision Trees, are often not robust enough to handle the complexity of biological data. This research presents a solution by systematically evaluating seven ensemble methods, namely Adaboost, Gradient Boosting, XGBoost, LightGBM, Random Forest, Bagging, and Stacking, using a dataset that includes 336 E. coli samples with eight biological features. These models are evaluated based on accuracy, precision, recall, and F1 score, with parameter optimization to obtain the best results. The results show that XGBoost is superior with accuracy, recall, and F1 score of 88% and precision of 87%, outperforming other methods. This research has the advantage of a comprehensive approach in comparing various ensemble methods simultaneously, accompanied by the application of confusion matrix-based evaluation to ensure the accuracy of the results. Additionally, the ensemble approach proved to be more effective in handling complex data patterns and reducing bias in bacterial strain classification. These findings provide a significant contribution, namely a practical framework for improving laboratory diagnostics and public health surveillance, with machine learning-based solutions that are faster, more reliable, and applicable for both industrial and clinical environments. This research expands understanding of the potential of ensemble methods in microbiological data classification and provides new directions for modern diagnostic technology
SMART ATTENDANCE TRACKING SYSTEM EMPLOYING DEEP LEARNING FOR FACE ANTI-SPOOFING PROTECTION
Get PDFConventional attendance systems face challenges in accuracy and efficiency, often vulnerable to spoofing and data manipulation. This study addresses these issues by developing a smart attendance system integrating Deep Learning-based facial recognition with anti-spoofing technology. The system ensures secure and reliable attendance authentication while automating and enhancing management processes. Utilizing a convolutional neural network (CNN) architecture, the system processes raw facial images directly without additional feature extraction, improving accuracy and efficiency. A novel training strategy, termed 50 Random Samples-30 Sub-epochs Count-1 Epoch, is introduced to optimize the training process. This strategy involves random sampling during each forward pass and grouping 30 passes as one epoch, enabling the use of complex CNN architectures and automatic dataset expansion. The system achieves 98.90% accuracy in identifying genuine attendance, maintaining a confidence level above 80%, significantly reducing spoofing risks and errors. This innovative solution has significant implications, particularly for educational institutions. It automates attendance tracking, minimizes manual effort, reduces errors, and supports disciplinary enforcement through accurate data. Moreover, its scalability allows for application across various environments, offering benefits to a wide range of institutions. By enhancing data accuracy and operational efficiency, this system sets a foundation for smarter, more reliable attendance management, strengthening administrative practices in education and beyond
PENDEKATAN HYBRID TSR-NN UNTUK PERAMALAN INFLOW OUTFLOW UANG KARTAL REGIONAL JAWA TIMUR
Get PDFThe availability of currency circulating in society can influence the economic conditions of a country. The need for money increases when religious holidays approach, such as Eid al-Fitr and Christmas, as well as school holidays and the end of the year. Therefore, it is necessary to plan the need for currency, one of which is by forecasting the circulation of currency, both inflow and outflow. Forecasting is done to predict a value in the future based on historical data. This research aim was to predict the inflow and outflow of regional currency in East Java using the hybrid Time Series Regression (TSR) – Neural Network (NN) method. The methods in time series analysis used to predict are increasingly developing, as are hybrid methods, namely methods that combine several models to produce more accurate forecasts. The analysis results obtained show that the prediction of incoming and outgoing cash flows is better using the hybrid TSR-NN method because it produces a smaller RMSE value, namely 1,656.62, with a MAPE of 0.28 compared to the TSR method. The results of this study are expected to contribute to a hybrid approach for forecasting the regional currency inflow and outflow of East Java
APPLICATION OF ARTIFICIAL NEURAL NETWORK METHODS TO DETECT HEART ATTACKS
Get PDFA heart attack is a medical emergency caused by restricted blood flow to the heart, commonly leading to myocardial infarction due to blood clots or fat accumulation. Early detection of heart disease is crucial to support prevention efforts and assist healthcare professionals in timely diagnosis and treatment. This study applies the Backpropagation Neural Network (BPNN) algorithm as an intelligent computing method for heart attack detection. Experimental results demonstrate a prediction accuracy of 96.47%, confirming the effectiveness of artificial neural networks in identifying heart attacks in patients. These findings highlight the potential of BPNN as a reliable and precise early detection system, which can support more accurate clinical decision-making and improve the effectiveness of heart attack prevention and treatment
PADANG FOOD IMAGE CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK (CNN)
Get PDFThe recognition of Padang traditional foods presents a challenge because of their high visual similarity, which makes manual classification difficult. This study aims to develop an automatic image classification model for Padang foods using the Convolutional Neural Network (CNN) algorithm. The dataset consisted of 1350 images across nine classes of Padang dishes including omelet, chili egg, cow tendon curry, stuffed intestine curry, fish curry, dendeng batokok, rendang, ayam pop, and fried chicken. The CNN architecture was trained for twenty epochs and evaluated using accuracy, loss, confusion matrix, and testing with new images. The results show that the model reached a final training accuracy of 70.2 percent and a validation accuracy of 65 percent, while testing with unseen images produced correct predictions with moderate confidence levels. These findings suggest that CNN is effective for classifying Padang traditional foods and can be applied in culinary promotion, digital food catalogs, and technology based ordering platforms
COMPARATIVE PERFORMANCE STUDY OF SEARCH ALGORITHMS ON LARGE-SCALE DATA STRUCTURES
Get PDF— In the era of big data, searching for information in big data sets is a big challenge that requires efficient search algorithms. This study compares the performance of three classic search algorithms, namely linear search, binary search, and hash search. This study uses large-scale datasets, namely Amazon Product Reviews and Amazon Customer Reviews. Evaluations were conducted based on the complexity of time for each search method. The results of the experiment showed that linear search had the slowest performance with O(n) time complexity, making it inefficient for large data sets. Binary search performs better with O(log n) complexity, but requires pre-sorted data. Hash searches provide the most optimal results in best-case and average with O(1) complexity, but can be reduced to O(n) in the worst case when there are too many collisions in the hash function. Hash search consistently outperforms linear and binary searches in terms of execution speed. Binary search remains highly efficient for sorted data, while linear search is clearly the least efficient, especially for large-scale datasets. Linear search has high execution times and is inconsistent, while binary and hash search are more efficient and stable. The algorithm's performance did not differ significantly between datasets, suggesting the data structure did not affect performance as long as the search type was the same
OPTIMIZING IT GOVERNANCE FOR ENHANCED SECURITY IN SMART CITIES
Get PDFThe rapid digitization of urban environments through technologies such as the Internet of Things (IoT), cloud computing, and big data analytics has significantly transformed modern cities into smart cities. However, this transformation has raised critical concerns regarding the security and privacy of citizen data. Prior studies have explored various IT governance models, yet there remains a gap in their contextual application to the dynamic and complex nature of smart cities. This research addresses that gap by examining the strategic role of Information Technology (IT) governance in enhancing data security and privacy in smart city initiatives. Through a literature review and analysis of case studies, this study identifies key IT governance frameworks and best practices, and adapts them to the unique operational, regulatory, and infrastructural demands of smart cities. The findings reveal that aligning IT governance with institutional policies, risk management, and legal compliance significantly strengthens urban digital resilience. Moreover, the incorporation of real-time monitoring systems, encryption protocols, and structured incident response plans is found to be effective in mitigating cyber threats. The novelty of this study lies in its integrated model that combines governance principles with smart city-specific risk contexts, offering a strategic roadmap for policymakers. This research contributes to the development of adaptive governance strategies that not only ensure compliance and security but also build public trust in digital urban services. Limitations of the study include the reliance on secondary data and the need for empirical validation, which will be addressed in future research through pilot implementations and stakeholder engagement
UNVEILING SPATIAL PATTERNS OF LAND CONVERSION THROUGH MACHINE LEARNING AND SPATIAL DISTRIBUTION ANALYSIS
Get PDFKayu Agung District in Ogan Komering Ilir (OKI) Regency, South Sumatra, has undergone rapid population growth, resulting in notable land-use transformations. This study examines land-use change dynamics from 2019 to 2023 and identifies their spatial distribution using satellite imagery. Satellite imagery classification was performed using three machine learning algorithms—K-Nearest Neighbors (KNN), Naïve Bayes, and Logistic Regression—with KNN achieving the highest accuracy. Spatial analysis employing the Variance-to-Mean Ratio (VMR) revealed that land-use changes are spatially clustered, indicating concentrated land conversion in specific areas. These findings emphasize potential environmental risks, including declining green open spaces and increasing urban pressure. The study contributes by integrating machine learning and spatial statistical analysis (VMR) as a comprehensive framework for understanding land-use conversion, providing scientific insights to support adaptive spatial planning and the achievement of Sustainable Development Goal (SDG) 11: Sustainable Cities and Communities
COMPARATIVE STUDY OF TRANSFORMER-BASED MODELS FOR AUTOMATED RESUME CLASSIFICATION
Get PDFThis study presents a comparative evaluation of transformer-based models and traditional machine learning approaches for automated resume classification—a key task in optimizing recruitment workflows. While traditional approaches like Support Vector Machines (SVM) with TF-IDF demonstrated the highest performance (93.26% accuracy and 95% F1-score), transformer models such as DistilBERT and RoBERTa showed competitive results with 93.27% and 91.34% accuracy, respectively, and fine-tuned BERT achieved 84.35% accuracy and an F1-score of 81.54%, indicating strong semantic understanding. In contrast, Word2Vec + LSTM performed poorly across all metrics, highlighting limitations in sequential modelling for resume data. The models were evaluated on a curated resume dataset available in both text and PDF formats using accuracy, precision, recall, and F1-score, with preprocessing steps including tokenization, stop-word removal, and lemmatization. To address class imbalance, we applied stratified sampling, macro-averaged evaluation metrics, early stopping, and simple data augmentation for underrepresented categories. Model training was conducted in a PyTorch environment using Hugging Face’s Transformers library. These findings highlight the continued relevance of traditional models in specific NLP tasks and underscore the importance of model selection based on task complexity and data characteristic