In this study aims to determine the classification of Alzheimer’s disease, this disease is a dangerous disease that can eliminate memory loss and can even result in a loss of ability to remember. For this reason, early detection of this disease is needed so that it can prepare for medical treatment. In this study the proposed method is to compare several decision tree methods with feature or attribute selection using the Particle Swarm Optimization (PSO) algorithm with the Alzheimer OASIS 2 dataset: Longitudinal Data from kaggle.com. The results of experiments with ten-fold cross validation, by testing the decision tree algorithm before the feature or attribute selection is performed, the highest accuracy value is obtained from the random forest algorithm with a value of 91.15%. The feature selection process is carried out using the PSO algorithm and the experiment is repeated using the Decision tree, the PSO-based random forest algorithm has the highest accuracy value of 93.56% with a kappa value of 0.884. Feature or attribute selection using the PSO algorithm is proven to be able to improve the accuracy of the decision tree algorithm, and is included in the algorithm with a very good range of values.
Time deposits are a product of a financial institution, which is currently increasing. The main target of this time deposit marketing is the old customers of the Bank. To increase the effectiveness of marketing customers are grouped into potential and non-potential customers. This means that potential customers have a greater chance to open a time deposit account. Customer data is taken from the UCI repository, originating from Banks in Portugal. Data is processed with rapidminer software using the Decision Tree method with Particle Swarm Optimization, Naïve Bayes with Particle Swarm Optimization and finally processed using Neural Network with Particle Swarm Optimization. Data processing results were compared and showed that the Naïve Bayes Algorithm with Particle Swarm Optimization had the highest accuracy of 97.04%. Therefore an application designed based on Naive Bayes with Particle Swarm Optimization. From the original attribute consisting of 20, only 9 attributes can be used so that the level of accuracy is high. Attributes used have values more than 0.500, while those that have these values are omitted. The design was created using the Unified Modeling Language (UML) and Visual Basic 6.0 to create an User Interface.
Abstract— Yogyakarta International Airport is a new airport in Yogyakarta, located in Temon, Kulonprogo Regency. Yogyakarta International Airport began operations on May 6, 2019. It was marked by the first landing of Citilink from Halim Perdanakusuma Jakarta. To support airport operations, the Transportation Department also prepares supporting transportation modes, such as airport trains, Damri Buses and, Shuttle Bus to get to the Airport. This mode of transportation connects cities around the airport, such as Purworejo, Magelang, Yogyakarta City, and even Surakarta City. However, because the airport is still relatively new, the information obtained by the public is still minimal. Therefore an informative application is needed for prospective passengers to go to or leave the airport. The application is made based on mobile, by utilizing GPS technology to monitor the position of the vehicle in real-time. This application will make it easier for visitors to travel to areas around YIA Airport.
The Covid 19 vaccination is considered to be the most effective way to prevent the spread of the Corona Virus, in addition to a clean lifestyle such as washing hands, wearing masks, and keeping a distance from other people. Several large vaccine manufacturing companies in the world have issued a product in the form of a Covid-19 vaccine with various levels of effectiveness. The vaccine is still being distributed throughout the world, including Indonesia. The vaccine obtained an emergency distribution permit from the authorized institution and was administered to community groups that meet the requirements. However, during the implementation of the vaccine, many AEFIs (Post Immunization Adverse Events) were found, such as dizziness, fever, headaches, and some even fainted. Although not dangerous but quite disturbing for people with solid activities. Therefore, it is necessary to predict whether participants will get AEFI or not. The data consists of 8 Attributes, after being processed using the J48 Algorithm, the results show that the attributes that have a strong influence are 7 Attributes, while the rest have no major effect. The accuracy level of the prediction model obtained is 91,23% with this level of accuracy, precision 0,918, recall 0,912, F-Measure 0,913 and ROC Area 0,966. It means that the model can be utilized by the parties concerned to then be able to anticipate.
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