Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
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Ant Colony Optimization for Jakarta Historical Tours: A Comparative Analysis of GPS and Map Image Approaches
No full textThe Traveling Salesman Problem (TSP) is a problem that represents a difficult combinatorial optimization problem starting from practical problems. The ant colony optimization (ACO) algorithm is implemented in several topics, particularly in solving combinatorial optimization problems. ACO is inspired by the behavior of ants in searching for the shortest path between a food source and their nest. In this research, ACO is used to find the best path or traveling salesman problem for museums and historical sites in Jakarta capital city of Indonesia. This research employs an approach based on the location's coordinates or latitude and longitude, while another method depends on coordinate data obtained from a supplied map image. After implementing both models, it can be concluded that the ACO model is not very good at solving TSP using actual coordinates. Meanwhile, the algorithm can quickly find near-optimal paths when using coordinates from a map image. The algorithm generates the optimal path in 11 seconds, reducing the initial distance from 17.938 to 4.430, using 4.731 ants and 75 trips with a distance power of 1. Statistical random variation was also performed, which proved that the algorithm is flexible and reliable when tested under various conditions
Real-Time Location Monitoring and Routine Reminders Based on Internet of Things Integrated with Mobile for Dementia Disorder
No full textThe increasing number of dementia sufferers worldwide demands a new approach to monitoring daily activities and locations to reduce the risk of getting lost. This study develops a real-time location monitoring and routine reminder system based on the Internet of Things (IoT), integrated with a mobile application. The system is designed to assist individuals with dementia, particularly elderly and younger adults with cognitive impairments, in performing daily routines independently, while providing a sense of security for families and caregivers through real-time location tracking features. This technology utilizes GPS for accurate location monitoring, daily activity reminders, and automatic notifications for caregivers in case of deviations from usual routes. The system development includes prototype creation that consisting of a mobile application and IoT tools such as the ESP32 WROOM microcontroller, Ublox Neo6M V2 GPS module, and SIM800L V2 GSM module. Functionality testing and impact evaluation were conducted to assess its effectiveness in improving the quality of life for dementia sufferers and facilitating monitoring for caregivers. With features such as daily reminders, emergency contacts, and real-time data integration, this system is intended not only for dementia patients but also for families and caregivers seeking tools to ensure the safety and comfort of the sufferers. It is expected that this research will enhance the independence of dementia patients in performing daily activities and provide innovative solutions through IoT technology to improve well-being across different age groups
Advancing Hate Speech Detection in Indonesian Language Using Graph Neural Networks and TF-IDF
No full textMost of the hate speech and abusive content on social media, particularly in the Indonesian language, presents significant challenges for content moderation systems. Previous research has applied machine learning models such as Recurrent Neural Networks (RNN), Support Vector Machines (SVM), and Convolutional Neural Networks (CNN) to address this issue. However, these approaches are limited in their ability to capture the relational and contextual nuances inherent in the data, resulting in suboptimal performance. This study introduces an approach by combining Graph Neural Networks (GNN) with Term Frequency-Inverse Document Frequency (TF-IDF) for feature extraction to improve hate speech detection on Twitter (platform X). The dataset consists of 13,169 Indonesian tweets, manually labeled for hate speech and abusive categories. Preprocessing steps include text cleaning, stemming, stop-word removal, and normalization. The GNN model achieved superior results, with accuracy scores of 92.90% for Abusive and 89.78% for Hate Speech, significantly outperforming the RNN model, which achieved accuracy of 86.09% and 86.15%, respectively. This study highlights the advantage of graph-based approaches in capturing complex relationships within text data. Future research can explore expanding datasets to include regional dialects and integrating advanced feature extraction techniques like Word2Vec or BERT. This study establishes a robust framework for improving hate speech detection, offering a valuable contribution to safer digital environments
UDAWA Gadadar: Agent-based Cyber-physical System for Universal Small-scale Horticulture Greenhouse Management System
Get PDFDigitalization in agriculture is becoming increasingly important for improving efficiency and sustainability, but small-scale farmers often face difficulties in adopting digital technologies because of various constraints. This study proposes an open-source intelligent system platform called UDAWA (Universal Digital Agriculture Workflow Assistant) to assist small-scale farmers in digitizing greenhouse management processes. The first variant of this platform, UDAWA Gadadar, was designed as a cyber-physical agent to control and monitor greenhouse instruments. UDAWA Gadadar was built using a 5C architecture approach and farmer-centric design thinking, utilizing an ESP32 microcontroller and a power sensor module to ensure performance and energy efficiency. The UDAWA Gadadar prototype was tested in a small-scale greenhouse with promising results, with an average remaining memory of 175 KB in the non-SSL mode and 122 KB in the SSL mode. Cost analysis indicates that this platform is relatively affordable for small-scale farmers, with a total component cost of USD 33.7 per unit. A decision matrix analysis involving five different greenhouse models in Pancasari Village, Buleleng Regency, Bali, showed that UDAWA Gadadar has high relevance and potential for adoption, particularly in models GH3 and GH5, with compatibility scores of 0.27. This study contributes to the development of appropriate and accessible digitalization solutions for small-scale agriculture, with future work focusing on developing other physical agent variants and a digital twin for enhanced cultivation simulations
XGBoost Algorithm for Cervical Cancer Risk Prediction: Multi-dimensional Feature Analysis
Get PDFCervical cancer continues to pose a significant global health challenge, with early detection remaining the cornerstone for effective intervention. This study is situated at the intersection of clinical oncology and computational intelligence, exploring the potential of gradient-boosting algorithms to overcome the limitations of conventional screening methodologies. An XGBoost model was developed to predict cervical cancer risk. This model incorporates demographic, behavioral, and clinical parameters. The model was developed using data from 858 patients at the Hospital Universitario de Caracas. The preprocessing pipeline was designed to address the complexities inherent in medical data, including strategic management of missing values and standardizing heterogeneous features. The model demonstrated an overall accuracy of 96.3%, with a sensitivity of 66.7% and a specificity of 97.6%. This performance profile indicates adept navigation of the delicate balance between missed diagnoses and unnecessary interventions. Feature importance analysis revealed a multifaceted risk landscape, where screening test results contributed substantial predictive power (approximately 60%), complemented by demographic and behavioral factors, including age, reproductive history, and contraceptive usage patterns. The confusion matrix analysis revealed the clinical implications of the model predictions, demonstrating a promising positive predictive value of 55.0% despite the pronounced class imbalance. These findings suggest that ensemble learning approaches can effectively synthesize diverse patient data into meaningful risk assessments, potentially enhancing screening efficiency through personalized stratification. Future research directions include prospective validation across diverse populations, integration of longitudinal data, and further exploration of explainable AI techniques to bridge the gap between algorithmic predictions and clinical implementation
Face Dermatological Disorder Identification with YoloV5 Algorithm
Get PDFDermatological disorders are common in humans. The accurate identification of skin diseases is paramount for determining the most efficacious treatment. This system can screen images of skin diseases on the face and provide analysis results in the form of object detection. Dermatological disorders of the face are classified into six categories: acne nodules, melasma, filiform warts, milia, papules, and pustules. The YoloV5 algorithm was selected because of its effectiveness in live-detection tasks. The image-enhancement process involves the implementation of two methodologies: sharpening and histogram equalization. The former adjusts the brightness values whereas the latter adjusts the contrast values. The dataset comprised 1,223 images of skin diseases, with 947 images allocated for training and 276 for validation. The optimal mAP of the filiform wart class was determined to be 87.6%, with values of 76.7% for pustules, 72% for papules, 71% for milia, 68% for nodules, and 38.2% for melasma, representing the lowest value. The low mAP of melasma was attributed to the abstract image data type and complexity of localization. The congruence of object features and disparity in data variance has the potential to influence outcomes
The Effect of Hyperparameters on Faster R-CNN in Face Recognition Systems
Get PDFFace recognition is one of the main challenges in the development of computer vision technology. This study aims to develop a face recognition system using a Faster R-CNN architecture, optimized through hyperparameter tuning. This research utilizes the "Face Recognition Dataset" from Kaggle, which comprises 2,564 face images across 31 classes. The development process involves creating bounding boxes using the LabelImg application and implementing the Grid Search method. The Grid Search is applied with predefined hyperparameter combinations (3 epochs [10, 25, and 50] × 3 learning rates [0.001, 0.0001, and 0.00001] × 3 optimizers [SGD, Adam, and RMS], resulting in 27 models). The evaluation metrics used were accuracy, precision, recall, and F1-score. The experimental results show that the selection of hyperparameters significantly affects the model performance. Based on the experimental results, the combination of the learning rate 0.00001, 50 epochs, and Adam optimizer yielded the highest accuracy and improvement of 8.33% compared to the baseline model. The results indicate that hyperparameter optimization enhances the ability of the model to recognize faces. Compared to conventional models, a Faster R-CNN performs better in detecting faces more accurately. Future research could further enhance the face recognition efficiency and accuracy by exploring other deep learning architectures and more advanced hyperparameter optimization techniques
Obesity Status Prediction Through Artificial Intelligence and Balanced Label Distribution Using SMOTE
Get PDFObesity, a global health challenge influenced by genetic and environmental factors, is characterized by excessive body fat that increases the risk of various diseases. With over two billion individuals affected worldwide, addressing this issue is crucial. This study investigated the application of Artificial Intelligence (AI) to predict obesity status using a dataset of 1,610 individuals, including demographic and anthropometric data. Four AI algorithms were analyzed: Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), Random Forest, and Support Vector Machine (SVM). The Synthetic Minority Over-Sampling Technique (SMOTE) was applied to address dataset imbalance. The results demonstrate that SMOTE significantly enhanced the models' performance, especially in recall and F1-score for minority classes, such as obesity. Random Forest achieved the highest accuracy (92%) and recall (92%) post-SMOTE. The ANN showed substantial improvement in recall, increasing from 77% to 89%, whereas the SVM achieved the highest precision (89%), minimizing false positives. Despite these improvements, KNN remained the least effective. The findings underscore the critical role of SMOTE in improving AI model accuracy for obesity prediction and highlight Random Forest as the most reliable algorithm for clinical decision-making. Limitations, such as dataset representativeness, suggest future research directions, including expanding data diversity and advanced feature selection techniques. This study provides valuable insights into leveraging AI and preprocessing methods for obesity management.
 
Evaluating Steganography Detection in JPEG Images Using Gaussian Mixture Model and Cryptographic Keys
No full textThis study introduces a novel approach that integrates Gaussian Mixture Models (GMM) with MD5 hash-based verification to detect hidden messages embedded via Least Significant Bit (LSB) steganography in JPEG images. Unlike previous methods, the proposed dual-layer technique combines probabilistic modeling with data integrity verification. The model was trained and evaluated using a dataset comprising both original and stego-JPEG images. The experimental results achieved an accuracy of 78.67% and a precision of 89.15%, indicating good class separation between stego and non-stego images (AUC-ROC = 0.8659). However, the recall rate of 69.70% suggests that there is room for improvement in detecting all stego instances. Although MD5 is a hash function rather than an encryption algorithm, it effectively aids in identifying data anomalies resulting from message embedding. Overall, this lightweight approach offers a practical solution for steganalysis and can be further enhanced through the integration of hybrid deep learning techniques in future research
Evaluating the Accuracy of a Hybrid Neural Model with RBF-Polynomial Kernel for Rainfall Prediction: A Comparative Analysis of Trainlm and Trainrp Functions
No full textAccurate rainfall prediction is crucial for effective water management and disaster mitigation. This study introduces a novel hybrid neural model that employs a fourth-degree polynomial kernel and provides the first empirical comparison of the trainlm and trainrp functions to enhance forecasting accuracy. This study explored the application of a neural network algorithm with RBF-Polynomial (degree 4) kernel for training and testing data in rainfall forecasting. This study focused on monthly rainfall data collected from Mataram City, Indonesia. We developed a hybrid BP-RVM algorithm as the main algorithm that offers a predictive approach to compare the trainlm and trainrp functions. We conducted 20 trials with combinations of learning, momentum, and gamma-RBF at internal values of 0.01-0.9. The training results from trainrp with more than 118 iterations yielded the best performance with learning rate 0.8 and momentum 0.2; MSE value of 2,236.25 and RMSE of 47.29. These results indicate a relatively low error rate for the proposed method. In contrast, the trainlm method, which only requires 18 iterations with a learning rate of 0.6 and momentum of 0.4, produces an MSE of 2,689.25 and RMSE of 51.86, showing its efficiency in reducing the computation time but with a slightly higher error rate than trainrp. Overall, the trainrp method was more accurate in capturing actual rainfall patterns with lower error rates, whereas the trainlm method exhibited good stability but greater sensitivity to parameter variations. This comparative analysis highlights the potential of trainrp to achieve more precise rainfall predictions within the study area