Dengue Hemorrhagic Fever (DHF) is a dangerous disease that may cause death within a short time and spread quickly. East Java is the province with the second-highest number of dengue cases in Indonesia. Blitar is one of the contributors to the high number of cases in East Java, with a CFR of 1.2% and Ponggok sub-district is one of the dengue-prone areas. Programs that have been promoted by the government are the 3M Plus Movement and the Eradication of Mosquito Nests (PSN). However, dengue disease still occurs every year because the community lacks the awareness to run these programs regularly and continuously. The purpose of community service through the 65th Airlangga University KKN-BBM program in the Health Sector were 1) increasing knowledge and awareness about DHF disease and its prevention, 2) improving skills in monitoring larvae independently, 3) reducing dengue transmission rates and improving the environmental health of the Candirejo Village community. The method used were the dissemination of knowledge about DHF and its prevention along with jumantik and PSN 3M Plus training. The target of this activity was elementary school students and the surrounding community. The results of the evaluation showed that there were differences in the level of knowledge before and after the socialization of DHF. In addition, the jumantik training was able to encourage students to independently carry out larvae monitoring. The community actively participated and was very enthusiastic in the implementation of PSN. Overall, all the programs carried out are appropriate, easy, and effective ways to prevent DHF.
Bayesian point estimation is an estimation method based on prior selection and loss function. In Objective Bayesian estimation are chosen prior to Jeffrey and used intrinsic discrepancy loss functions based on the Kullback-Leibler divergence equation which will have a minimum effect of data on the posterior distribution. The objective Bayesian point estimator provides estimates of population parameters based solely on the assumed population distribution and data. The goal of this paper is to estimate parameters from the exponential distribution on type II censored data using the objective Bayesian and bootstrap methods. The bootstrap method is used to resampling and built a confidence intervals for parameters whhich will be estimated. The methods were applied on the life-time data of 63 patients of chronic renal failure and the initial diagnosis was non-diabetic disease with bootstrap methods using 10, 100, and 1000 times used in this study. So that the bigger bootstrap samples rendered the estimated value θ̂ will be better and the result confidence interval ranges narrower.
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