PREDIKSI MISSING IMPUTATION UNTUK DATA PENYEBARAN MENGGUNAKAN NAÏVE BAYES

Arifianto, Aji Seto and Hartadi, Didit Rahmat and Novitasari, Anik Nur PREDIKSI MISSING IMPUTATION UNTUK DATA PENYEBARAN MENGGUNAKAN NAÏVE BAYES. Jurnal Teknologi Informasi dan Terapan (J-TIT), 3 (1). ISSN 2354-838X

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Abstract

Dengue fever is a disease caused by dengue virus by Aedes Aegypti mosquitoes intermediary. There are four dengue virus serotypes, called DEN-1, DEN-2, DEN-3, and DEN-4, each of them can lead to dengue fever, either mild or fatal. Based on a survey of public health in Jember, recorded during January 2015 there were 300 cases of dengue fever, and seven of them died. Previous research using fuzzy method to determined the potential spread of Dengue in Jember. Para meters that used is the amount of rain in a period, the amount of rain in one month, free amount of larva and house index. Data that used is taken from 2009 until 2012 from 31 districts. The weaknesses in this study were not contain a way to resolve the imputation of missing data. In fact, survey data is often incomplete. Based on these issues, it will be created a prediction system of imputation of missing data on the spread of dengue by using Naïve Bayes method. The data refers to the prediction of the mi ssing data and the data were used as training data, so it can be processed further. This research has been successfully predicting missing data imputation using existing data, so that the missing data can be completed with high degree of accuracy.

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