DOTS STRATEGIC WITH EXTREME MACHINE LEARNING METHOD IN THE CLASSIFICATION OF DISEASE TRANSMISSION IN TB PATIENTS

Abstract

Nuraini, Diana, Elis Anggeria, Kristina L Silalahi, Ismail Husein, Sajaratud Dur, Sulaiman, Marischa Elveny, Rahmad Syah

Machine Learning Model The type of data to be grouped determines the type similarity measurement techniques are used. The exact image measurements are used for determine the similarity of the two data points in the dataset. Therefore, we studied an image dataset of TB patients is an important step in its treatment to anticipate early detection with the aim of helping the DOTS plan strategy with the community. The results showed that there were 9 men with DOTS and EML algorithms (73.3%). While for women, the DOTS and EML algorithms were 5 people (26.4%). The age was determined between 12 people (25.3%) while those and 7 people (55.5). Sufficient knowledge of DOTS and EML algorithms among 30 people (66.7%). Minimum knowledge is 2 people and middle knowledge is 13 people (34.3%). Based on the final results of the DOTS and EML algorithm, there were 26 people (46.9%) and tall as many as 6 people (54.1%).