Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control
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Optimized Visualization of Digital Image Steganography using Least Significant Bits and AES for Secret Key Encryption
No full textData 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
How HEXAD Types Influence Systemic and Finer-Grained Experiences in Gameful Educational Media: An Exploratory Study
Get PDFEducation in the 21st century demands technology support, in which gameful media, such as educational games, can provide. Providing this support also requires the media to accommodate the different needs of the players, which can be identified by classifying the players’ type using HEXAD typology. However, the effect of HEXAD type classification on players’ experience in gameful media is still vague. This study aims to adress this vagueness by exploring the implementation of HEXAD in a more systemic and fine-grained manner using a playtest of an educational role-playing game. We measured the playtesters’ gameplay and learning experiences (n = 60) through a questionnaire developed based on HEXAD scale, GUESS, and EGameFlow. We also measured the correlation between the playtesters’ HEXAD types and their gameplay and learning experiences. Our analysis of the correlations uncovers exciting findings, including that the “achiever” type strongly appreciates playability features and that playability is among the essential gameplay factors for HEXAD types. We also propose design principles that can guide future research and development of the media
Design and Simulation of Battery Charging System with Constant Temperature–constant Voltage Method
Get PDFBatteries 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
UI/UX Design for AR Card Game: Enhancing English Vocabulary Learning with Augmented Reality
No full textThis 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
Implementation of Deep Learning Based on Convolution Neural Network for Batik Pattern Recognition
Get PDFBatik as a cultural heritage is one of the heritages that needs to be preserved so that it continues to be recognized from generation to generation. Efforts to preserve batik can be made by using technology that can recognize batik motifs. Pattern recognition is a branch of science related to the identification, classification, and interpretation of patterns. Deep learning is one of the technologies that can be used very well for pattern recognition, especially for syllable and image recognition. Convolutional neural network (CNN) is one of the most popular deep learning methods and the most established algorithm for deep learning models. The main advantage of CNN over the preceding methods is its ability to automatically detect features, making the feature extraction and classification process highly organized. This study aims to apply CNN for batik pattern recognition. The batik patterns used were geometric patterns, divided into 7 batik classes. Experiments were conducted on 3100 data, consisting of 3000 for training set and 100 for testing set. At the preprocessing stage, the batik image was resized to 28x28, and the color was changed to grayscale. Training was carried out on 100, 200, and 300 epochs. The classification results prove that CNN can recognize batik patterns well with an accuracy rate of 95%
Optimizing Connected Vehicle Routing Protocol for Smart Transportation Systems
Get PDFThe significant growth in integrating connected vehicles into intelligent transportation networks has underscored the importance of Vehicle-to-Vehicle (V2V) communication in optimizing route efficiency, reducing traffic congestion, and enhancing road safety. However, routing protocols such as AODV face substantial challenges in dynamic automotive environments characterized by high mobility and rapid topology changes, leading to issues like packet loss, delays, and network congestion. Reactive protocols like AODV often suffer from route discovery delays, while proactive protocols like DSDV, although reducing latency, increase bandwidth consumption, making them less effective in highly dynamic contexts. This study introduces the Learning Automata Ad Hoc On-Demand (LA-AODV) routing protocol, designed to improve relay node selection and V2V communication efficiency. The proposed method leverages real-time vehicle data to predict and select optimal relay nodes under dynamic traffic conditions, thereby enhancing packet delivery ratio, throughput, and reducing latency and routing overhead. The results demonstrate that LA-AODV significantly outperforms AODV and DSDV across various traffic scenarios, with an increase in packet delivery ratio up to 4% in high traffic conditions, throughput reaching 125 units, and a reduction in end-to-end delay within the range of 2E+10 to 6E+14. These improvements highlight LA-AODV's superior efficiency in handling packet loss and latency, making it a suitable protocol for data-intensive and safety-critical applications that demand reliable and efficient data transmission. This study contributes by developing the LA-AODV protocol, which significantly enhances V2V communication performance in dynamic traffic scenarios and provides a robust simulation model replicating real-world conditions, potentially reducing traffic accidents
Sentiment Analysis on Social Media Using CNN-RNN Hybrid: A Case Study of Indonesian Presidential Candidate
Get PDFResearch on sentiment analysis for Presidential Candidate 01 on social media cannot be ignored because there is no in-depth understanding of public perceptions and opinions circulating online. The CNN model is quite commonly used for sentiment analysis; however, this model still has quite low accuracy so modifications need to be made. This research aims to increase the accuracy of sentiment analysis through the application of a modified Convolutional Neural Network (CNN) method. The research process includes collecting tweet data related to Presidential Candidate 01 using crawling techniques, data preprocessing, sentiment labeling, data balancing, as well as dividing the dataset into training, validation and test data. The CNN model is modified with additional layers to improve the performance. The model is evaluated by measuring its accuracy, precision, recall, and F1 Score. The research results show that the modified CNN-RNN Hybrid model with the Upsampling method achieves an accuracy of 94% and F1 Score of 0.95, while the CNN-RNN Hybrid model has an accuracy of 86% and F1 Score of 0.82, the CNN Model has an accuracy of 90% and F1 Score of 0.88, and the RNN model has an accuracy of 88% and F1 Score of 0.84, which are higher compared to the Naïve Bayes and LSTM methods used in the previous research. Modifying the CNN method can significantly increase the accuracy of sentiment analysis for Presidential Candidate 01, so that it can become a more effective tool for understanding public perceptions and improving political campaign strategies
Clustering of High School Quality Using Fuzzy C-Means in the Special Region of Yogyakarta Province
Get PDFThis 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
Get PDFCausal 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
Analysis of Mental Health Disorders via Social Media Mining Using LSTM and Bi-LSTM
No full textMental 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