Cluster analysis is included in the method of multivariate analysis of interdependence. Cluster analysis is a multivariate technique that classifies objects into different groups between one group and another group. This research is applied to the case of education indicators, education is important for improving the quality of human resources. Educational indicators are a measuring tool used to see how well the quality of education. Educational indicators are classified using average linkage and median linkage. The results of the analysis showed that the median linkage obtained a standard deviation ratio value of 0.061 smaller than the standard deviation ratio average linkage value of 0.078. The method that has the smallest ratio is the method with the best performance. So that grouping City Districts in Sulawesi based on education indicators in 2017 is better to use the median linkage and obtained 5 clusters formed.

The analysis of the phenomenon of extreme values of climate, especially rainfall is very important for government to reduce the negative impacts. Global circulation model (GCM) is an important data in the climate system because it can provide information about the climate in the future on a large scale. Techniques to reduce the size of the spatial scale using statistical downscaling (SD). SD modeling method requires a more flexible alternative to the assumption that the resulting models can be used to describe the climate events. Generalized additive model (GAM) is a method that accommodates the influence of linear and nonlinear in extreme rainfall events. The methodology is applied to forecast montly extreme rainfall in Indramayu District.

Backpropagation is one of the supervised training methods that causes an error in the output produced. Backpropagation neural networks will be carried out in 3 stages, namely feedforward from input training patterns, backpropagation from errors related to adjustment of weights. Updating the weight is done when the training results obtained have not been converged. The value of the goal error (MSE) is 0.0070579 which is achieved at epochs to 99994 from the provisions of 100000 iterations. Based on the plot regression, the training data resulted in a correlation coefficient value of up to 0.55321. The correlation coefficient value is concluded that the greater the R value produced, the better the level of accuracy in face identification carried out in this study

Hypertension is a chronic disease difficult to manage well, and is ranked first in Sleman, Yogyakarta, Indonesia. This study aimed to determine the relationship between the factors of demography, comorbidity, medication, lifestyle, and access to health services related to the quality of life of people with hypertension and its complications. The study was conducted in a cross-sectional manner using data from the Sleman Health and Demographic Surveillance System from 2015 to 2018 in cycles 3 and 2 with the inclusion criteria of hypertensive patients and their complications aged 25 years or older obtained by using total sampling of 532 people. Measurement of quality of life was carried out using the Short Form 12v2 2a and 2b questionnaires presented in the physical component summary (PCS) and mental component summary (MCS). Data analysis was done using the Mann-Whitney test and Kruskal-Wallis test. The results showed factors related to the quality of life in PCS were variables of gender, age, diagnosis of hypertension and its complications, the presence of comorbidities, fatty foods, and drug consumption in the last 2 weeks, while factors related to the MCS were education and occupation seen from the p value < 0.05. Quality of life with hypertension and its complications influence and decrease the patient's physical more than mental condition.

Field rice is a rice plants that is planted in a sedentary or shifting location. This study aims to forecast field rice production using the Multiplicative Decomposition method of moving average, and to determine the size of forecasting accuracy using Tracking signal, data used is the data from Central Sulawesi Province Field rice production in 2008-2016 obtained from the Agriculture Service of Central Sulawesi Province The research procedure is begun by analyzing the components of decomposition, namely the components of trend (T), seasonal (S), cyclic (C) and random components (I) then multiplies the value of these components. Forecasting results using the decomposition method helping by the Minitab 18 application in 2017 show that the pattern of the data contains a declining trend with the equation Yt = 1895.60 - 7.97 × t, and has a strong seasonal pattern with the expected pattern of data that increases or decreases in certain months such as March, April, August and December. The forecasting results obtained are at the control limit of Tracking signal which is between -4 to +4 that means the forecasting of rice production in the province of Central Sulawesi in 2017 using the moving average Multiplicative Decomposition method is valid