Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
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Skin Cancer Classification Using a Hybrid Pre-trained CNN with Random Forest Classifier
No full textSkin cancer, a leading cause of global mortality with 10 million deaths annually, is projected to increase rapidly if not diagnosed early. Traditional diagnosis relies on visual evaluation and histopathology, which are subjective and time-consuming. Recent advances in Convolutional Neural Networks (CNN) enable automated, accurate image analysis for early identification. This study explores pre-trained CNN models, including DenseNet-201, InceptionV3, MobileNet, ResNet50, and VGG16, by modifying them to better identify skin lesions as malignant or benign. The proposed models outperformed the state-of-the-art CNN models evaluated on publicity with traditional test data. The proposed models achieved 94.20% accuracy, which is higher than that of traditional CNN models
Augmentation for Accuracy Improvement of YOLOv8 in Blind Navigation System
Get PDFThis study addresses the critical need for enhanced accuracy in YOLOv8 models designed for visually impaired navigation systems. Existing models often struggle with consistency in object detection and distance estimation under varying environmental conditions, leading to potential safety risks. To overcome these challenges, this research implements a rigorous approach combining data augmentation and meticulous model optimization techniques. The process begins with the meticulous collection of a diverse dataset, essential for training a robust model. Subsequent preprocessing of images in the HSV color space ensures standardized input features, crucial for consistency in model training. Augmentation techniques are then applied to enrich the dataset, enhancing model generalization and robustness. The YOLOv8 model is trained using this augmented dataset, leading to significant enhancements in key performance metrics. Specifically, mean average precision (mAP) improved by 13.3%, from 0.75 to 0.85, precision increased by 10%, from 0.80 to 0.88, and recall rose by 10.3%, from 0.78 to 0.86. Further optimization efforts, including parameter tuning and the strategic integration of a Kalman Filter, notably improved object tracking and distance estimation capabilities. Final validation in real-world scenarios confirms the efficacy of the optimized model, demonstrating its readiness for practical deployment. This comprehensive approach showcases tangible advances in navigational assistance technology, significantly improving safety and reliability for visually impaired users
The Effect of Resampling Techniques on Model Performance Classification of Maternal Health Risks
Get PDFIndonesia's maternal mortality rate was the second highest in ASEAN, reflecting the problem of class imbalance in maternal health data. This research aimed to improve prediction accuracy in the classification of pregnant women's diseases through the application of various resampling methods. The methods used in this research included Synthetic Minority Over-sampling Technique (SMOTE), SMOTE-Edited Nearest Neighbor (SMOTE-ENN), Adaptive Synthetic Sampling (ADASYN), and ADASYN-ENN, using five classification algorithms: Decision Tree, K-Nearest Neighbor (KNN), Naïve Bayes, Random Forest, and Support Vector Machine (SVM). Performance evaluation was carried out using accuracy, precision, recall, and F1-score metrics to determine the best method and algorithm. The results showed that the SMOTE-ENN and ADASYN-ENN methods significantly improved the model's performance in predicting maternal disease. Random Forest and Decision Tree algorithms showed the best results in terms of accuracy and consistency. These findings provided practical guidance for the application of resampling techniques in the classification of pregnant women's health data, which could contribute to improving the quality of maternal health services in Indonesia
Analyzing Reddit Data: Hybrid Model for Depression Sentiment using FastText Embedding
Get PDFDepression, a prevalent mental condition worldwide, exerts a substantial influence on various aspects of human cognition, emotions, and behavior. The alarming increase in deaths attributable to depression in recent years demonstrates the imperative need to address this problem through prevention and treatment interventions. In the era of thriving social media platforms, which have a significant impact on society and psychological aspects, these platforms have become a means for people to express their emotions and experiences openly. Reddit stands out among these platforms as a significant place. The main aim of this study is to examine the feasibility of forecasting individuals' mental states by classifying Reddit articles on depression and non-depression. This work aims to employ deep learning algorithms and word embeddings to analyze the textual and semantic settings of narratives to detect symptoms of depression. The study effectively employed a BiLSTM-BiGRU model that applied FastText word embeddings. The BiLSTM-BiGRU model analyzes information bidirectionally, detecting correlations in sequential data. It is suitable for tasks dependent on input order or for addressing data uncertainties. The Reddit dataset, which contains text concerning depression, achieved an accuracy score of 97.03% and an F1 score of 97.02%
Integration Waterfall and Scrum Methodology in The Development of SIMARGA Web Application
No full textThis research explores the integration of waterfall and scrum methodologies in the development of the SIMARGA web application. Integration aims to maximize the strengths of each methodology, with Waterfall contributing to planning, analysis, and initial system structure, while Scrum supports the creation of product backlog and implementation of Scrum events. The combination results in a structured and methodical product management process, simplifying task lists, and improving efficiency. The achievement of good efficiency and effectiveness in task execution is facilitated by leveraging both methodologies, reducing waste. Progress in product development and team productivity is measured through daily meetings, evaluations, and sprint reviews. Emphasis is placed on fostering strong relationships among the Scrum team, customers, and stakeholders, promoting effective communication and collaboration. However, challenges are identified in team commitment to daily meetings, which are potentially influenced by their involvement in additional business activities. Future efforts should focus on improving technological resources and maintaining software to achieve the product goals initially outlined. The effectiveness of this product development initiative can be measured using metrics such as views and user data, particularly in the Kabupaten Pegunungan Bintang
Utilization of the Convolutional Neural Network Method for Detecting Banana Leaf Disease
No full textBanana leaf diseases such as Sigatoka, Cordana, and Pestalotiopsis pose a significant threat to banana productivity, with implications for food security and the global economy. Early detection of this disease is an important step to reduce its spread and maintain crop yield stability. This research utilizes the Convolutional Neural Network (CNN) method to detect banana leaf diseases based on image analysis of infected and healthy leaves. The dataset used includes 937 images consisting of four main categories, namely healthy leaves, Sigatoka, Cordana, and Pestalotiopsis. The dataset is processed through augmentation to increase data diversity and quality. The CNN model was applied for classification, with evaluation results reaching 92.85% accuracy, 95.73% recall, 93.52% precision, and 94.60% F1-score. This research contributes to the development of Artificial Intelligence-based technology for applications in the agricultural sector, especially in supporting farmers to detect banana leaf diseases quickly, accurately and efficiently. The research results also provide recommendations for exploring additional data augmentation and increasing dataset variety to improve model detection performance in the future. This shows CNN's potential in supporting sustainable agriculture in the modern era
Improving Algorithm Performance using Feature Extraction for Ethereum Forecasting
Get PDFEthereum is a cryptocurrency that is now the second most popular digital asset after Bitcoin. High trading volume is the trigger for the popularity of this cryptocurrency. In addition, Ethereum is home to various decentralized applications and acts as a link for Decentralized Finance (DeFi) transactions, Non-Fungible Tokens (NFTs) and the use of smart contracts in the crypto space. This study aims to improve the performance of the forecasting algorithm by using feature extraction for Ethereum price forecasting. The algorithms used are neural networks, deep learning, and support vector machines. The research methodology used is Knowledge Discovery in Databases. The data set used comes from the yahoo.finance.com website regarding Ethereum prices. The results show that the neural network Algorithm is the best Algorithm compared to Deep Learning and support vector machine. The root mean square error value for the neural network before feature selection is 93,248 +/- 168,135 (micro average: 186,580 +/- 0,000) Linear Sampling method and 54,451 +/- 26,771 (micro average: 60,318 +/- 0,000) Shuffled Sampling method. Then after feature selection, the root mean square error value improved to 38,102 +/- 31,093 (micro average: 48,600 +/- 0,000) using the Shuffled Sampling metho
RAFT: An IoT-Based Nutrition Monitoring System for Bok Choy Hydroponics Plants
Get PDFThe Internet of Things (IoT) plays a crucial role in technology advancements, especially in the agricultural sector, such as hydroponics. Manual monitoring of parameters such as nutrient levels, pH, and water levels in plants consumes farmers' time and energy and increases the risk of crop failure. This research aims to evaluate the effectiveness of using IoT RAFT (Remote Automated Farming Technology) system for farmers, particularly hydroponic bok choy farmers, to monitor and control plant nutrient levels and the development process using waterfall as a research methodology. The parameters tested in this research include the height of the bok choy, the number of leaves, and the weight of the harvest of the bok choy. We conducted this research on 14 plants for one harvest period, then we used linear regression to determine the growth rate by calculating the slope. The results show that the plant height, the number of leaves, and the harvest weight using the IoT RAFT system are 0.5897 cm/day, 0.6391 leaves/day, and 216.43 grams, respectively. We also compared the IoT RAFT system with a non-IoT bok choy growing method, and we concluded that our IoT RAFT system has a better growth rate compared to the non-IoT bok choy growing method
Development of an Early Warning System Using Social Media for Flood Disaster
Get PDFThis research paper introduces an innovative prototype system that uses IoT technologies to monitor floodwater levels. Integration of an ultrasonic sensor, ESP8266 microcontroller, Arduino IDE, and the ThingSpeak platform aims to establish a robust flood monitoring solution. The paper provides a thorough exploration of the system's background, the problem it addresses, the methodology employed, and the obtained results, along with insights into future research directions. The study meticulously describes the design, implementation and programming code for data collection and transmission within the system. Through extensive field testing and meticulous data analysis, the paper evaluates the precision and effectiveness of the proposed flood monitoring solution. In particular, research underscores the advantages of IoT, emphasizing real-time data collection, logging, and analysis as essential components for efficient flood management. Additionally, the paper elucidates step-by-step instructions for configuring Telegram notifications through the ThingSpeak React app, enhancing the practical applicability of the developed system. The research effectively highlights the potential of IoT in flood monitoring, showcasing its superior accuracy and effectiveness compared to traditional methods. By demonstrating the feasibility and advantages of IoT in the context of flood monitoring, this study contributes valuable information, enriching existing knowledge, and paving the way for future advances in the field. Research encourages the continued exploration of advanced techniques to strengthen flood monitoring and management strategies. Ultimately, this work presents a comprehensive IoT-based prototype for floodwater monitoring, offering valuable information and fostering the promising role of IoT technologies in this critical domain
Utilization of Household Organic Waste into Biogas and Integrated with IoT
No full textThe increase in population impacts several environmental sectors, particularly the use of natural gas energy for household needs, such as LPG (Liquefied Petroleum Gas). This has resulted in the depletion of natural gas reserves and a rise in LPG imports. Additionally, the growing population contributes to the accumulation of household waste, which can lead to excessive leachate production and greenhouse gas emissions. This issue is particularly concerning in developing countries like Indonesia due to its negative environmental impact. This research aims to provide a solution and contribute to reducing household waste accumulation by utilizing organic waste to create renewable energy in the form of biogas as an alternative to LPG. Biogas is produced through the fermentation of organic waste. Nutrient-rich fluids containing sugar can enhance the performance of methanogenic bacteria in biogas formation. In this study, we conducted nutritional testing on molasses and coconut water to determine which nutrients optimize biogas production efficiency by monitoring the pressure of the generated biogas. Generally, biogas comprises methane and carbon dioxide. It is important to note that excessive methane can lead to explosions, while high carbon dioxide levels contribute to greenhouse gas emissions. The quantities of methane and carbon dioxide produced during biogas generation can be influenced by temperature and humidity. Therefore, monitoring pressure, temperature, humidity, methane, and carbon dioxide levels in the biogas production process using the Internet of Things (IoT) is a prudent approach. The results indicate that a substrate mixed with molasses produces biogas at twice the pressure compared to coconut water. Furthermore, optimal biogas production with ideal methane and carbon dioxide levels, occurs at temperatures between 25-35°C under high humidity conditions. This suggests that mesophilic methanogenic bacteria thrive in tropical climates