Penerapan Naïve Bayes Untuk Prediksi Missing Imputation Pada Data Pernyebaran Demam Berdarah Dengue

Septianingrum, Anik Nur Novitasari Eka (2016) Penerapan Naïve Bayes Untuk Prediksi Missing Imputation Pada Data Pernyebaran Demam Berdarah Dengue. Diploma thesis, Politeknik Negeri Jember.

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Abstract

Dengue fever Dengue or abbreviated DBD is a disease caused by dengue virus by Aedes Aegypti mosquitoes intermediary. There are four types of dengue virus that is the 1st DEN, the 2nd DEN, 3rd DEN and 4th DEN, each of them can lead to dengue fever, either mild or fatal. Based on the survey of Jember Public health Office that recorded during January 2015 from 300 cases of disease, seven of them died. In previous research that the fuzzy method can be a solution to looking at the potential spread of the disease in Jember Regency DBD. Parameters that used among others is rainfall (CH), rainy days (HH), larva-free number (ABJ) and house index (HI). Data that used is taken from 2009 until 2012 from 31 districts. However, the deficiency in the study is did not contains a ways to complete missing data imputation. In the fact, survey data or data in the field is often incomplete. With the existence of these problems, then a prediction system of imputation of missing data on the spread of dengue fever by using methods of Naïve Bayes is made. The data is refers to the empty prediction data and that data is used as a training data, so it can be further processed. This research has been successfully predicting missing data inputation using existing data, so that the empty data can be completed with a high-level accuracy.

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