TIME SERIES ANALYSIS FOR FORESCASTING THE NUMBER OF TUBERCULOSIS IN KENDARI CITY 2018-2023

noviati novi 1 Indonesia
1 a:1:{s:5:"en_US";s:29:"stikes mandala waluya kendari";}
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Published 2019-12-04
Vol. 1 No. 1 (2019) PAGES 72-79
DOI 10.36566/ijhsrd/Vol1.Iss1/8
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Tuberculosis is a contagious disease that is still a problem in the world today, not only in developing countries but also in developed countries. That is what happened in Kendari City in 2012 - 2017. Efforts made to prevent the increasing number of tuberculosis in the future is to make predictions. This study aims to study time series analysis in predicting the incidence of tuberculosis based on sex and working area of ??health centers in Kendari City in 2018-2022. This type of research is quantitative descriptive using the series analysis. Sources of research data obtained from the Kendari City Health Agency in Southeast Sulawesi Province consisted of data on pulmonary TB cases in which sex and working area of ??Puskesmas in 2012 - 2017 in Kendari City would be processed and analyzed by time series using the trend method into 3 linear trend models, quadratic trends and exponential trends. The results showed the best model for prediction of pulmonary TB cases in Kendari City was the quadratic model. Based on the number of cases predicted to increase in the period 2018 to 2022, with an average decline with an average decrease of 79 cases in men and 286 cases in women. Pulmonary TB cases based on puskesmas area are predicted to increase in 2018 until 2022 with the highest average increase in Kemaraya puskesmas area. While the average decline in cases is highest in the Mata Puskesmas area. It is expected to be able to be information for policy makers, so that prevention and early promotion efforts can be made for the community.

TIME SERIES ANALYSIS FOR FORESCASTING THE NUMBER OF TUBERCULOSIS IN KENDARI CITY 2018-2023. (2019). INDONESIAN JOURNAL OF HEALTH SCIENCES RESEARCH AND DEVELOPMENT (IJHSRD), 1(1), 72-79. https://doi.org/10.36566/ijhsrd/Vol1.Iss1/8
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Section
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Keywords
Tuberculosis, Time, Series, Prediction

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