WEATHER CLASSIFICATION USING NAIVE
BAYES CLASSIFICATION
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Institusion
Universitas Katolik Soegijapranata
Author
PRATAMA, ANTONIUS ANGGA ANDRI
Subject
Information Systems
Datestamp
2018-06-21 04:28:00
Abstract :
ABSTRACT The changes of the weather attract scientists to conduct research on weather classification. Some factors could influence in the change of the weather such temperature, humidity and rainfall. This project uses data mining method to classify the weather. There are many data mining methods that can be used to classify the weather. And this project will analyze one of data mining method that is naive bayes classification to do weather classification. An aspect that being reference to be analyzed in this project is finding dominant parameter that affect in weather classification. There are eight parameters to be used on this project that is temperature, air pressure, humidity, dew point, wind speed, visibility, heat index, and weather condition. Based on the implementation and testing, the order of the parameter that affect in change of weather from the most dominant is visibility, heat index, pressure, humidity, temperature, dew point, and wind speed. While the highest accuracy from system is 65,9%, with precision reached 44,776%, and recall reached 40,4% using parameter visibility and heat index. keyword: weather classification, data mining, naive bayes classification
ABSTRACT The changes of the weather attract scientists to conduct research on weather classification. Some factors could influence in the change of the weather such temperature, humidity and rainfall. This project uses data mining method to classify the weather. There are many data mining methods that can be used to classify the weather. And this project will analyze one of data mining method that is naive bayes classification to do weather classification. An aspect that being reference to be analyzed in this project is finding dominant parameter that affect in weather classification. There are eight parameters to be used on this project that is temperature, air pressure, humidity, dew point, wind speed, visibility, heat index, and weather condition. Based on the implementation and testing, the order of the parameter that affect in change of weather from the most dominant is visibility, heat index, pressure, humidity, temperature, dew point, and wind speed. While the highest accuracy from system is 65,9%, with precision reached 44,776%, and recall reached 40,4% using parameter visibility and heat index. keyword: weather classification, data mining, naive bayes classification
File :
14.K1.0078 ANTONIUS ANGGA ANDRI PRATAMA.COVER.pdf
14.K1.0078 ANTONIUS ANGGA ANDRI PRATAMA.BAB I.pdf
14.K1.0078 ANTONIUS ANGGA ANDRI PRATAMA.BAB II.pdf
14.K1.0078 ANTONIUS ANGGA ANDRI PRATAMA.BAB III.pdf
14.K1.0078 ANTONIUS ANGGA ANDRI PRATAMA.BAB IV.pdf
14.K1.0078 ANTONIUS ANGGA ANDRI PRATAMA.BAB V.pdf
14.K1.0078 ANTONIUS ANGGA ANDRI PRATAMA.BAB VI.pdf
14.K1.0078 ANTONIUS ANGGA ANDRI PRATAMA.DAFTAR PUSTAKA.pdf
14.K1.0078 ANTONIUS ANGGA ANDRI PRATAMA.LAMPIRAN.pdf
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