Identifikasi Model Untuk Prediksi Penyebaran Penyakit Tuberkulosis Menggunakan Simulated Annealing dan Extreme Learning Machine
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Institusion
Universitas Airlangga
Author
Musfivawati, Mega, -
Subject
QA1-939 Mathematics
Datestamp
2021-10-18 10:22:50
Abstract :
Tuberculosis is an infectious disease caused by bacillus Mycobacterium tuberculosis. Indonesia is the third country with the highest tuberculosis cases in the world, after India and China. This thesis aims to obtain the results of the identification model for predicting the spread of tuberculosis using the Simulated Annealing and Extreme Learning Machine. The process begins with parameter estimation in the model using the Simulated Annealing Algorithm. After obtaining the optimal parameters in the model, the identification and prediction of the model is carried out using the Extreme Learning Machine Algorithm. Model identification and prediction are needed to anticipate and minimize the worst possible consequences of the fluctuation of tuberculosis cases. Based on the implementation and simulation of the data on the spread of Tuberculosis in East Java Province in the form of data per quarter starting from the first quarter of 2002 to the third quarter of 2019, it is obtained that MSE for the identification process is 0,002619 and in the model validation process an error value is obtained of 0,01979. Meanwhile, for the prediction process, it is obtained MSE of 0,02422 and in the prediction process, the error value is 0,01342. Based on the error values that have been obtained, it can be concluded that the identification of models for predicting the spread of tuberculosis using the Simulated Annealing Algorithm and Extreme Learning Machine is able to identify models to predict the spread of tuberculosis in the future well.
Tuberculosis is an infectious disease caused by bacillus Mycobacterium tuberculosis. Indonesia is the third country with the highest tuberculosis cases in the world, after India and China. This thesis aims to obtain the results of the identification model for predicting the spread of tuberculosis using the Simulated Annealing and Extreme Learning Machine. The process begins with parameter estimation in the model using the Simulated Annealing Algorithm. After obtaining the optimal parameters in the model, the identification and prediction of the model is carried out using the Extreme Learning Machine Algorithm. Model identification and prediction are needed to anticipate and minimize the worst possible consequences of the fluctuation of tuberculosis cases. Based on the implementation and simulation of the data on the spread of Tuberculosis in East Java Province in the form of data per quarter starting from the first quarter of 2002 to the third quarter of 2019, it is obtained that MSE for the identification process is 0,002619 and in the model validation process an error value is obtained of 0,01979. Meanwhile, for the prediction process, it is obtained MSE of 0,02422 and in the prediction process, the error value is 0,01342. Based on the error values that have been obtained, it can be concluded that the identification of models for predicting the spread of tuberculosis using the Simulated Annealing Algorithm and Extreme Learning Machine is able to identify models to predict the spread of tuberculosis in the future well.
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