Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
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Analysis and Development of Eight Deep Learning Architectures for the Classification of Mushrooms
Get PDFOne food item that is easy to find in nature is the mushroom. In terms of form and characteristics, mushrooms are similar. Arranging mushrooms into groups so that poisonous and non-poisonous ones can be separated is important. Real-time analysis of mushrooms is still not used very often. Previous studies focused primarily on performance and accuracy, ignoring architectural computing and a significant amount of data preprocessing. The data set used is more laboratory-conditioned. This will impede the process of widespread implementation. The study suggests changes to eight current architectures: Modified DenseNet201, DenseNet121, VGG16, VGG19, ResNet50, InceptionNetV3, MobileNet, and EfficientNet B1. The development of this architecture took place within the areas of classification and hyperparameter learning. In contrast to the other eight architectures, the MobileNet architecture exhibits the lowest computational performance and highest accuracy, according to the comparison results. When the confusion matrix is used for evaluation, an accuracy of 82.7% is achieved. Modified MobileNet has the best speed because it keeps a lower computation architecture and cuts down on unnecessary preprocessing. This means that many people can use smartphones with more realistic data conditions to make it work
Comparison of Madaline and Perceptron Algorithms on Classification with Quantum Computing Approach
Get PDFThe fundamental problem in this research is to explore a more profound understanding of both performance and efficiency in quantity computing. Successful implementation of algorithms in computational computing environments can unlock the potential for significant improvements in information processing and neural network modeling. This research focuses on developing Madaline and Perceptron algorithms using a quantum approach. This study compares the two algorithms regarding the accuracy and epoch of the test results. The data set used in this study is a lens data set. There are four attributes: 1) patient age: young, prepresbyopia, presbyopia 2) eyeglass prescription: myopia, hypermetropia, 3) astigmatic: no, yes. 4) tear production rate: reduced, normal. There are three classes: 1) patients must have hard contact lenses installed, 2) patients must have soft contact lenses installed, and 3) patients cannot have contact lenses installed. The number of data is 24 data. The result of this research is the development of the Madaline and Perceptron algorithms with a quantum computing approach. Comparing these algorithms shows that the best accuracy is the Perceptron algorithm, namely 100%. In comparison, Madaline is 62.5%, and the smallest epoch is the Madaline algorithm, namely 4 epochs, while the smallest Perceptron epoch is 317. This research significantly contributes to the development of computing and neural networks, with potential applications extending from data processing to more accurate modeling in artificial intelligence, data analysis, and understanding complex patterns
Design and Implementation of the Shortest Path Navigation in Samosir District using Branch and Bound Algorithm
No full textSamosir has a large area and several tourist attractions, making it difficult for visitors to explore the island. Unknowing the route can make the journey less fun and waste time. In general, tourists seek to know the fastest way to a tourist location to save time and money while on vacation. As a result, we require an application that will offer directions to the shortest path. This research aims to develop a web-based application that may display a map of the shortest travel to a tourist site. This website will display a map that marks the route from the origin point to the destination point. The Branch and Bound algorithm is used to determine the shortest path. The Python libraries OSMnx, Folium, and NetworkX modify paths and show a route map with OpenStreetMap. The error value of the distance between the branch, the bound algorithm, and Google Maps is used to obtain the RMSE accuracy value. The RMSE value is 3.02 and the MAPE value is 0.0023 indicating that the application produced already has a good implementation prototype. Furthermore, there is no significant distinction between the appearance of maps that implement OpenStreetMap and Google Maps
A Middleware Applications Design for Health Information Sharing
No full textThe interoperability between electronic health records (EHR) and electronic medical records (EMR) from various healthcare facilities for comprehensive patient care is important. However, integrating such systems, including the need for interoperability standards, data privacy, and security, is a highly challenging task, especially since patient rights in data access must be considered. The primary problem addressed is the challenge of integrating electronic health records (EHR) and electronic medical records (EMR) within various healthcare facilities to ensure comprehensive patient care while maintaining data privacy, security, and adherence to patient rights. This work presents an innovative application for consolidating patient health records from various medical facilities. Facilitates seamless data access, improving the efficiency of healthcare care delivery. The GGD approach was used in developing the prototype to ensure that the delivered product was able to meet the user requirements. Four phases are divided into six stages used in this method: research, modeling, requirements definition, framework definition, refinement, and support. The evaluation involved two phases, back-end and front-end testing, using white-box and black-box testing. White-box testing delivers an average frame-rendering rate of up to 56 fps, and black-box testing has shown 100% successful results in the given task. In conclusion, the Med-OID prototype was successfully developed. Integrates and securely transmits medical records across various healthcare services, demonstrating significant potential to enhance personalized medicine and healthcare coordination. The evaluations underscored the robustness of the prototype and its ability to improve interoperability and data sharing in healthcare systems
Quantum Perceptron: A Novel Approach to Predicting Unemployment Levels in North Sumatra Province
No full textThe application of Quantum Computing to improve the perceptron algorithm in unemployment prediction is a new aspect of this research. This study focuses on unemployment, which is a big challenge for the young generation in Indonesia, especially in the North Sumatra region. This research applies the quantum perceptron method to provide an alternative solution in predicting the unemployment rate. The data used in this analysis comes from the North Sumatra Central Statistics Agency and includes published unemployment rates (TPT) for individuals aged 15 years and over from 2017 to 2023. This research uses seven variables ranging from x1 to x7 to produce accurate data. Quantum perceptron methods offer specific advantages over traditional methods, including higher computing speeds and the ability to handle greater data complexity. This analysis aims to identify unemployment patterns and trends in North Sumatra and provide more accurate predictions by applying the quantum perceptron method. Although the results of this research are still limited to analysis, this research shows promising results and opens up opportunities for further, more in-depth research. This research is limited to predicting unemployment rates in North Sumatra. The use of quantum computing using the quantum perceptron method shows great potential for application to various other socio-economic problems in the future. This research contributes by introducing a new approach that utilizes quantum technology to improve prediction accuracy in economic analysis
Analysis of Sulawesi Earthquake Data from 2019 to 2023 using DBSCAN Clustering
No full textSulawesi is a region in Indonesia known for its significant seismic activity, and its history of impactful earthquakes makes it an area of crucial importance for in-depth analysis. This study analyses earthquake occurrence data in the Sulawesi region from 2019 to 2023 using clustering methods with the DBSCAN algorithm. The utilization of the DBSCAN algorithm was chosen for its ability to cluster data based on spatial density, well-suited for analyzing the spatial patterns of earthquakes. DBSCAN is known for its effectiveness in identifying spatial clusters, especially in handling data with undefined density patterns. The primary aim of this research is to identify spatial earthquake occurrence patterns, classify regions with similar earthquake occurrence rates, describe the characteristics of the resulting spatial clusters, and identify seismic gap areas. The results of analysis and clustering using the DBSCAN algorithm have identified clusters with earthquake depth characteristics, which are expected to make a significant contribution to mapping and understanding earthquake vulnerability and distribution in this region. These findings can aid in more effective disaster mitigation planning, support sustainable development efforts, and enhance earthquake preparedness and response in Sulawesi. This study contributes to a better understanding of earthquake patterns and potential seismic gaps in Sulawesi, which is crucial for developing improved risk mitigation strategies and supporting sustainable development policies
Comparative Study of Earthquake Clustering in Indonesia Using K-Medoids, K-Means, DBSCAN, Fuzzy C-Means and K-AP Algorithms
No full textIndonesiaβs frequent earthquakes, caused by its position at the convergence of multiple tectonic plates, Indonesia's frequent earthquakes, caused by its position at the convergence of multiple tectonic plates, necessitate precise seismic zone identification to improve disaster preparedness. This research evaluates the effectiveness of five clustering algorithmsβK-Medoids, K-Means, DBSCAN, Fuzzy C-Means, and K-Affinity Propagation (K-AP)βfor analyzing earthquake data from January 2017 to January 2023. Using a dataset from BMKG encompassing 13,860 seismic events, each algorithm was assessed based on Silhouette Score and Cluster Purity metrics. Results indicated that K-Means provided the best balance, forming six clusters with a Silhouette Score of 0.3245 and Cluster Purity of 0.7366, making it the most suitable for seismic zone analysis. K-Medoids closely followed with a Silhouette Score of 0.3158 and Cluster Purity of 0.7190. Although DBSCAN effectively handled noise, its negative Silhouette values indicated poor clustering quality. Fuzzy C-Means and K-AP underperformed, with K-AP generating an impractically high number of clusters (196) and the lowest Silhouette Score (0.2550). This study offers a novel, comprehensive comparison of clustering algorithms for Indonesian earthquake data, emphasizing a dual-metric evaluation approach. By identifying K-Means as the most effective algorithm, provides valuable insights for disaster mitigation and seismic risk analysis
IoT Security: Botnet Detection Using Self-Organizing Feature Map and Machine Learning
No full textThe rapid advancement of Internet of Things (IoT) technology has created potential for progress in various aspects of life. However, the increasing number of IoT devices also raises the risk of cyberattacks, particularly IoT botnets often exploited by attackers. This is largely due to the limitations of IoT devices, such as constraints in capacity, power, and memory, necessitating an efficient detection system. This study aims to develop a resource-efficient botnet detection system by using the Self-Organizing Feature Map (SOFM) dimensionality reduction method in combination with machine learning algorithms. The proposed method includes a feature engineering process using SOFM to address high-dimensional data, followed by classification with various machine learning algorithms. The experiments evaluate performance based on accuracy, sensitivity, specificity, False Positive Rate (FPR), and False Negative Rate (FNR). Results show that the Decision Tree algorithm achieved the highest accuracy rate of 97.24%, with a sensitivity of 0.9523, specificity of 0.9932, and a fast execution time of 100.66 seconds. The use of SOFM successfully reduced memory consumption from 3.08 GB to 923MB. Experimental results indicate that this approach is effective for enhancing IoT security in resource-constrained devices
The Impact of Feature Extraction in Random Forest Classifier for Fake News Detection
No full textThe pervasive issue of fake news spreading rapidly on online platforms. causing a concerning dissemination of misinformation. The influence of fake news has become a pressing social problem, shaping public opinion in important events such as elections. This research focuses on detecting and classifying fake news using the Random Forest algorithm by investigating the impact of feature extraction techniques on classification accuracy, this study specifically employs the TF-IDF method. For this purpose, we used 44,898 English-language articles from the ISOT fake news dataset. The dataset is cleaned using tokenization and stemming then split into 75% training and 25% testing. The TF-IDF vectorizer technique was applied to convert text into numeric as feature extraction. This study has implemented a Random Forest classifier to predict real and fake news. The proposed model contributes to overall classification precision by comparing it to the existing models. This fake news detection highlights the efficacy of the TF-IDF vectorizer and Random Forest combination which achieved an impressive accuracy rate of 99.0%. This contribution highlights an effective strategy for combating misinformation through precise text classification
The Memory Efficiency in a Receptionist Robot's Face Recognition System Using LBPH Algorithm
No full textThis research aims to develop a memory-efficient face recognition system for a receptionist robot using the Local Binary Patterns Histogram (LBPH) algorithm. Given the computational limitations of the Raspberry Pi, the system utilizes optimization techniques including grayscale conversion, noise reduction, and contrast adjustment to enhance processing efficiency. Testing demonstrates that the face recognition accuracy achieves 80.5% to 85.5% in offline mode, and 72% to 81% in real-time mode, with variations due to lighting conditions and facial expressions. The robot's servo motors exhibit a response time between 1.945 and 3.561 seconds, enabling responsive and interactive user engagement. The results suggest practical benefits for deploying face recognition in resource-constrained environments, enhancing the efficiency of robotic receptionist applications