State Junior High School 1 SIMPATI is a school located in Pasaman, West Sumatra. Schools are teaching institutions for students and are supervised by educators or teachers. The quality of education is very necessary for the progress of the school. For this reason, at the end of each semester at SMP N 1 Simpati an assessment is carried out for teacher perfor-mance, this assessment is to measure the quality and evaluate the performance of every teacher who teaches at the school. Analysis of teacher performance assessment using the Multi Attribute Utility Theory (MAUT) method. By applying the Multi Attribute Utility Theory (MAUT) method in the assessment process, the final result is ranked from the highest alternative value to the lowest alternative value. This Decision Support System is built on a Web-based basis with the PHP program-ming language and the Code Igniter framework. And the UAT test has been carried out on the system with 90% results, which means the application can be accepted for use, as well as blackbox testing where the application can run according to its function and is suitable for use in a teacher performance assessment decision support system
Many People were less concerned with lung health, it caused people identified as suffering from lung diseases. Early symptoms that often appear was cough that took a long time and could be the beginning of more severe disease. Therefore it was necessary to create application that could detect suspected person contracted lung disease. The applications were made by using artificial neural network with Backpropagation with initial input data, symptoms by patients of lung diseases. The symptoms were 22, and kind of lung diseases as a diagnosis were asthma, pneumonia, pulmonary tuberculosis and lung cancer. It used medical records of lung disease as much as 110 data. Network training uses 3 different architectures [input neurons ; hidden neurons ; output neurons], liked [22; 22 ; 2], [22 ; 33 ; 2] and [22 ; 43 ; 2]. Performance measurement was carried out on two types of training data and testing data distribution, namely comparison 90:10 and 80:20. The Parameters values were used namely learning rate 0.1, 0.3, 0.5, 0.7 and 0.9. The number of epoch was used, that is 15 epoch, 25 epoch and 35 epoch.Based on the tests performed, it was obtained an accuracy system on the 90:10 data comparison of 82% and the 80:20 data ratio of 82% as well. Thus, backpropagation method could be applied in detecting suspected lung diseases.
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