Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Not a member yet
1071 research outputs found
Sort by
Combining the Cellular Automata and Marching Square to Generate Maps
No full textAs computer technology advances, one of the entertainment media that has emerged is video games. The development of a video game is becoming more expensive and labor-intensive as technology itself continues to grow. One of the characteristics of a game as an entertainment medium is its replay value, which refers to the fact that the subject matter can be played more than once. Automating content through the use of procedural content generation is done with the goal of lowering expenses and reducing the amount of labour that is required. This research has two goals: designing and developing a Maze Game using the Procedural Content Generation method with the Cellular Automata and Marching Square algorithms, and determining the level of player satisfaction with the games developed using the Game User Experience Satisfaction Scale (GUESS) method. This research will utilize Cellular Automata and the Marching Square algorithm as a method for generating 3D game shapes through Procedural Content Generation. After the game has been developed, it will be performed by players, and the Game User Experience Satisfaction Scale will be used to measure the user experience. The result for overall satisfaction, based on the responses of 25 respondents, is 83.14%. Cellular Automata was effectively implemented to generate the map, while Marching Square was used to generate the 3D mesh, albeit with isolated rooms and graphical errors
Enhancing Problem-Solving Reliability with Expert Systems and Krulik-Rudnick Indicators
No full textProblem-solving is one of the skills needed in the 21st century, but there is a significant gap between the ideal conditions and the reality of students' problem-solving skills. One method that can improve students' problem-solving skills is Krulik and Rudnick, but implementing this method with an expert system to improve problem-solving skills is still limited. This research aims to build an expert system to determine the level of problem-solving using Krulik and Rudnick's problem-solving indicators processed using the forward chaining and certainty factor algorithms. The study had five stages: data analysis, rule generation, certainty measurement, prediction, and testing. The data was processed by developing 5 Krulik and Rudnick problem-solving indicators into 35 statements. Each statement was categorized using Forward Chaining by producing three rules: low, medium, and high. The problem-solving level obtained is calculated using the Certainty Factor for a confidence value. The system's prediction results were evaluated using a confusion matrix, resulting in an accuracy of 80%, a precision of 92%, and a recall of 85%, indicating the system's reliable performance in measuring the level of problem-solving. This research can be used as a reference to support problem-solving in various more advanced educational and professional environments
Empowering Low-Resource Languages: Javanese Machine Translation
No full textThis study addresses the critical need to preserve and revitalize the Javanese language, which despite its widespread popularity, faces challenges as a low-resource language in Indonesia. The decline in Javanese proficiency among younger generations poses a significant threat to the language's cultural significance and heritage. To address this issue, this study introduces an innovative approach to machine translation, focusing on the development of a robust Indonesian-Javanese translation system. Utilizing advanced neural machine translation (NMT) techniques, including Long Short-Term Memory (LSTM) networks, the proposed system aims to bridge the linguistic gap between Indonesian and Javanese. Special attention was given to the unique linguistic characteristics and challenges of Javanese, with the goal of achieving exceptional translation accuracy and fluency. Through extensive experimentation and evaluation, this study aims to demonstrate the effectiveness of the translation system in facilitating cross-cultural communication and language preservation efforts within the Javanese-speaking community. By emphasizing the significance of Javanese as a widely spoken yet under-resourced language, this study underscores the importance of innovative technological solutions in safeguarding linguistic diversity and cultural heritage. Through its contributions, the research seeks to address the pressing need for language preservation and revitalization, particularly in the context of low-resource languages like Javanese
Hand Sign Recognition of Indonesian Sign Language System SIBI Using Inception V3 Image Embedding and Random Forest
No full textThis paper presents a sign language recognition system for the Indonesian Sign Language System SIBI using image embeddings combined with a Random Forest classifier. A dataset comprising 5280 images across 24 classes of SIBI alphabet symbols was utilized. Image features were extracted using the Inception V3 image embedding, and classification was performed using Random Forest algorithms. Model evaluation conducted through K-Fold cross-validation demonstrated that the proposed model achieved an accuracy of 59.00%, an F1-Score of 58.80%, a precision of 58.80%, and a recall of 59.00%. While the performance indicates room for improvement, this study lays the groundwork for enhancing sign language recognition systems to support the preservation and broader adoption of SIBI in Indonesia
Application of VGG16 in Automated Detection of Bone Fractures in X-Ray Images
No full textThe purpose of this research is to determine whether or not a deep learning model called VGG16 can automatically identify bone fractures in X-ray pictures. The dataset, sourced from Kaggle, includes 10,522 images of human hand and foot bones, which underwent preprocessing steps such as normalization and resizing to 224x224 pixels to enhance data quality. The study utilizes the VGG16 architecture, pre-trained on ImageNet, as a base model, with transfer learning applied to adapt the model for fracture detection by fine-tuning its weights. This architecture consists of five blocks of convolutional and max-pooling layers to effectively extract and enhance information from the images for precise classification. The training and testing phases utilized an 80:20 split of the data, employing binary cross-entropy as the loss function and the Adam optimizer for efficient weight updates. The model achieved high performance, with an accuracy of 99.25%, precision of 98.62%, recall of 98.88%, and an F1-score of 99.16% over 25 epochs with a batch size of 128. Experimental results indicate that smaller batch sizes generally enhance accuracy and reduce loss values, with batch sizes of 128 and 16 yielding optimal performance. The study's findings underscore the potential of VGG16 in improving diagnostic accuracy and reliability in medical imaging, providing a robust tool for fracture detection. Future research should continue exploring hyperparameter optimization to further enhance model performance while balancing computational efficiency
Comparison of Transfer Learning Model Performance for Breast Cancer Type Classification in Mammogram Images
No full textGlobally, breast cancer is the type of cancer that most women suffer from. Early detection of breast cancer is very important because there is a big chance of cure. Mammography screening makes it possible to detect breast cancer early. The study of computer-assisted breast cancer diagnosis is gaining increasing attention. Breast cancer comes in two forms: benign cancer and malignant cancer. advances in deep learning (DL) technology and its use to overcome obstacles in medical imaging, and classification using a number of transfer learning models to identify the type of breast cancer (malignant, benign, or normal). This work conducted a thorough comparison analysis of eight prevalent pre-trained CNN algorithms (VGG16, ResNet50, AlexNet, MobileNetV2, ShuffleNet, EfficientNet-b0, EfficientNet-b1, and EfficientNet-b2) for breast cancer classification. In this study, we permonData is divided into training, testing, and validation. Using the publicly accessible mini-DDSM dataset, we assess the proposed architecture. were used to measure the classification accuracy (Acc). For genBased on test results, the best accuracy was obtained using EfficientNetb2 with an accuracy value of 94% for training data and 98% for test data on mammogram images
Forecasting Stock Returns Using Long Short-Term Memory (LSTM) Model Based on Inflation Data and Historical Stock Price Movements
Full text linkThe stock market is crucial for economic growth and development, offering profit opportunities that attract investors worldwide. However, its inherent volatility necessitates the inclusion of macroeconomic indicators like inflation, which can affect stock valuation and investor behavior. This study explores predicting stock returns using a Long Short-Term Memory (LSTM) model by incorporating inflation data, historical stock price movements, and calculated returns as input features. The dataset was split into 80% for training and 20% for testing, with hyperparameter tuning conducted using the RMSprop optimizer under varying batch sizes and epoch settings. Experimental results show that the configuration using RMSprop with a batch size of 8 and 200 epochs delivered the best performance, achieving a Root Mean Squared Error (RMSE) of 0.0167 and a Mean Absolute Percentage Error (MAPE) of 25.89%. These results represent a significant improvement over alternative configurations and previous benchmarks. This study underscores the importance of including inflation as a predictive variable, enhancing the model's accuracy. The findings highlight the relevance of incorporating macroeconomic factors into stock return forecasting, providing valuable insights for investors and financial analysts seeking data-driven strategies in decision-making processes
Enhancing Stroke Prediction with Logistic Regression and Support Vector Machine Using Oversampling Techniques
Full text linkStroke is a significant health concern that can result in both death and disability, making the early identification of risk factors crucial. Previous studies on stroke prediction have been limited by inadequate handling of class imbalance, lack of comprehensive feature selection, and parameter optimization, with accuracy rates usually below 80%. This study compares the performance of Logistic Regression (LR) and Support Vector Machine (SVM) algorithms combined with different oversampling methods—SMOTE, Borderline-SMOTE, ADASYN, Random Over Sampling (ROS), and Random Under Sampling (RUS)—on a stroke prediction dataset. Correlation-based feature selection identified age, hypertension, and heart disease as significant predictors. GridSearchCV with 10-fold cross-validation was used for hyperparameter optimization, and performance was evaluated using precision, recall, accuracy, and ROC curves. The results showed that SVM significantly outperformed Logistic Regression across all sampling methods. SVM+ROS achieved the highest performance with perfect recall (100%), precision of 97.18%, and accuracy of 98.56% (AUC: 0.9857), whereas SVM + Borderline-SMOTE offered balanced performance with a recall of 94.99%, precision of 95.06%, and accuracy of 95.17% (AUC: 0.9512). LR + Borderline-SMOTE performed the best with an accuracy of 84.98% (AUC: 0.8503), significantly better than previous studies. This improved accuracy shows significant clinical benefits, potentially reducing missed stroke diagnoses by identifying thousands of additional at-risk patients in large-scale screening programs. Healthcare providers should consider implementing SVM with ROS in critical care settings, where potentially missed stroke cases have severe consequences. Simultaneously, SVM with Borderline-SMOTE may be more appropriate for resource-constrained environments
Language Processing for Detecting Fake News on Twitter Using a Long Short-Term Memory Architecture
No full textThe rapid spread of misinformation on social media platforms, particularly X (formerly Twitter), poses a significant challenge to public trust and democratic integrity. Fake news is often crafted to deceive readers and manipulate public opinion, especially in political contexts such as the 2024 Regional Head Elections (Pilkada 2024). Although various measures have been proposed to mitigate this issue, achieving an effective balance between controlling misinformation and preserving free speech remains a challenge. This study aims to address this problem by developing a fake news detection model based on Natural Language Processing (NLP) and Long Short-Term Memory (LSTM). The dataset used in this study was collected from public tweets related to Pilkada, with Kompas.com serving as the validation source to verify content authenticity. Experimental results show that the proposed LSTM model outperformed traditional classification methods, achieving a precision, recall, and F1-score of 0.95, along with an overall accuracy of 94.90%. Confusion matrix analysis further confirmed the reliability of the model by demonstrating low misclassification rates. This study contributes to the advancement of AI-driven hoax detection systems, offering an automated and scalable solution for combating misinformation in political discourse
Handling Imbalance in Javanese Manuscript Character Dataset using Skeleton-based Balancing Generative Adversarial Networks
No full textJavanese script is an important part of Indonesia’s cultural heritage, representing cultural values from the past. However, recognizing and classifying Javanese characters within manuscripts is challenging due to the limited availability of data and uneven distribution of character classes. The decline in formal use of Javanese script has drastically reduced the pool of manuscript samples, causing certain characters to appear rarely and skewing class frequencies. Existing methods that utilize Generative Adversarial Networks (GANs) attempt to address this problem. However, they often struggle to generate characters that are both consistent and visually accurate in terms of structural details. To address these issues, this study introduces a skeleton-based balancing GAN (SkelBAGAN), which improves the structural details of the previous method for generating characters. The proposed method introduces three main enhancements: (i) a layer for extracting the character skeleton structure, (ii) an optimized pretrained network using an autoencoder for learning the skeleton distribution, and (iii) refinement of the evaluation function, preserving both the distribution and structural fidelity in the adversarial process. The performance of the proposed model is evaluated against previous methods using the Fréchet Inception Distance (FID) to assess distribution quality and the Structural Similarity Index Measure (SSIM) to evaluate structural fidelity. The results indicate that the proposed methods outperform previous methods in balancing the FID and SSIM metrics. The integration of all enhancements in SkelBAGAN achieves the lowest FID, indicating improved generative quality while maintaining competitive SSIM values. The qualitative study indicates that SkelBAGAN outperforms previous methods in character generation. These results highlight how the skeleton-based improvement of the quality of generated characters enhances the recognition performance for underrepresented Javanese characters in imbalanced datasets. Ultimately, this work contributes to the broader effort to preserve the Javanese script as a vital element of Indonesia’s cultural identity