scholarly journals PENGGUNAAN ANALISIS KLASTER K-MEANS DALAM PEMODELAN REGRESI SPASIAL PADA KASUS TUBERKULOSIS DI JAWA TIMUR TAHUN 2017

2020 ◽  
Vol 4 (1) ◽  
pp. 164-178
Author(s):  
Hardani Prisma Rizky ◽  
Wara Pramesti ◽  
Gangga Anuraga
Keyword(s):  
Moving Average ◽  
Spatial Regression ◽  
P Value ◽  
Autoregressive Moving Average ◽  
Test Results ◽  
Tuberculosis Patients ◽  
Weighting Matrix ◽  
Regression Approach ◽  
Spatial Dependencies ◽  
Spatial Autoregressive

Tuberculosis (TB) is a contagious infectious disease caused by the bacterium Mycobacterium tuberculosis which can attack various organs, especially the lungs. TB if left untreated or incomplete treatment can cause dangerous complications to death. East Java Province has the second-highest TB case after West Java Province. Therefore we need statistical modeling to analyze the factors that influence TB in East Java Province. The data used in this study were sourced from data from BPS and East Java Provincial Health Offices in 38 districts/cities in East Java Province in 2017. Analysis of data using the OLS regression approach only looked at variable factors but was unable to know the effects of territory. So to overcome this, a spatial regression approach is used by comparing the weight of Queen Contiguity and the results of the k-means cluster analysis to obtain the best model. Based on the results of the analysis, the spatial aspects of the data have met the assumptions of spatial dependencies using the Moran's I test with a p-value of 0.000001295. The weighting matrix used is the k-means cluster weighting matrix k = 2. The test results obtained by the Spatial Autoregressive Moving Average (SARMA) model selected as the best model with the value of the deterrence coefficient (R2) and Akaike Info Criterion (AIC), 87.10% and 586.69. The factors that significantly influence the number of Tuberculosis patients in each district/city in East Java are population density (X2) and the number of healthy houses (X9).

scholarly journals PEMODELAN INDEKS PEMBANGUNAN MANUSIA DI PROVINSI JAWA TENGAH TAHUN 2017 MENGGUNAKAN ANALISIS REGRESI SPASIAL

2020 ◽  
Vol 11 (1) ◽  
pp. 45
Author(s):  
Wahyuni Alwi ◽  
Jajang Jajang ◽  
Nunung Nurhayati
Keyword(s):  
Regression Models ◽  
Health Workers ◽  
Moving Average ◽  
Spatial Regression ◽  
Influence Factors ◽  
P Value ◽  
Linear Regression Models ◽  
Spatial Error ◽  
Central Java ◽  
Spatial Autoregressive

This research discussed about model of Human Development Index (HDI) in Central Java with spatial regression analysis. and identify  variables that give significant influence. First, analyze the influence factors based on result of p-value from t test in multiple linear regression models. Then, made spatial weight matrix with queen continguity method. After that, estimate spatial regression models, namely spatial autoregressive (SAR), Spatial error models (SEM), and spatial autoregive moving average (SARMA) and  choose the best model based on minimum AIC value. The results showed that SAR was the best spatial regression model and the significant variables was the gross enrollment rates at senior high schools, the health workers, and the district minimum wages. All of them that give positive influences. The variable that give biggest influence for HDI was the health workers. Full Article

scholarly journals PEMODELAN JUMLAH TINDAK KRIMINALITAS DI PROVINSI JAWA TIMUR DENGAN ANALISIS REGRESI SPATIAL AUTOREGRESSIVE AND MOVING AVERAGE

2018 ◽  
Vol 7 (4) ◽  
pp. 346
Author(s):  
NI MADE LASTI LISPANI ◽  
I WAYAN SUMARJAYA ◽  
I KOMANG GDE SUKARSA
Keyword(s):  
Population Density ◽  
Regression Model ◽  
Average Length ◽  
Moving Average ◽  
Spatial Regression ◽  
Higher Order ◽  
Spatial Effect ◽  
Spatial Regression Model ◽  
Spatial Autoregressive ◽  
Total Average

One of spatial regression model is spatial autoregressive and moving average (SARMA) which assumes that there is a spatial effect on dependent variable and error. SARMA can analyze the spatial effect on the higher order. The purpose of this research is to estimate the model of the total crime in East Java along with factors that affect it. The results show that the model can describe total crime in East Java is SARMA(0,1). The factors that influence the total crime  are population density (), poverty total (), average length of education at every regency/city and error from the neigbors.

scholarly journals PEMODELAN REGRESI SPASIAL INVESTASI LUAR NEGERI YANG MASUK KE INDONESIA

2020 ◽  
Vol 14 (4) ◽  
pp. 543-556
Author(s):  
Muktar Redy Susila ◽  
Rizfanni Cahya Putri ◽  
Dian Arini
Keyword(s):  
Lagrange Multiplier ◽  
Foreign Investment ◽  
Spatial Regression ◽  
P Value ◽  
Domestic Investment ◽  
West Java ◽  
Investment Funds ◽  
Foreign Countries ◽  
Spatial Dependencies ◽  
Monthly Salary

One of the important factors in the growth of a country's economy is investment. Sources of investment are divided into two, namely foreign investment and domestic investment. There are many benefits of foreign investment. These benefits are such as for development, employment, tax revenue, increasing foreign exchange, international trade, strengthening friendship with foreign countries and much more. The purpose of this research is to modeling of foreign investment in Indonesia using spatial regression. The predictor variables used are the number of labor and the net monthly salary. Based on the results of research analysis obtained information that the West Java province is the highest province to get investment funds from abroad. The highest net monthly salary in Indonesia is DKI Jakarta province. The highest number of labor in Indonesia is West Java Province. Based on the results of the Lagrange Multiplier test which showed a significant Lagrange Multiplier (lag) with a p-value < α = 5%. That's shows for the number of labor, the net monthly salary, and the foreign investment in Indonesia fulfill the spatial dependencies assumption. Based on the spatial regression model shows the number of labor, the net monthly salary, and the weighting has a significant impact on foreign investment in Indonesia.

scholarly journals PEMODELAN JUMLAH KASUS PNEUMONIA BALITA DI JAWA TIMUR MENGGUNAKAN REGRESI SPATIAL AUTOREGRESSIVE MOVING AVERAGE

2019 ◽  
Vol 8 (3) ◽  
pp. 236
Author(s):  
MADE NARYMURTI WIDYASTUTI ◽  
I GUSTI AYU MADE SRINADI ◽  
MADE SUSILAWATI
Keyword(s):  
Regression Analysis ◽  
Health Services ◽  
Moving Average ◽  
Coefficient Of Determination ◽  
Autoregressive Moving Average ◽  
Spatial Autoregressive ◽  
Significant Factors

The purpose of this study is to model and determine the factors that significantly influence the number of toddler pneumonia cases in East Java Province. Modeling the number of toddler pneumonia cases was conducted using spatial autoregressive moving average (SARMA) regression analysis. The results showed that the best model to modeling was SARMA (1.1) with the AIC value is  and the coefficient of determination ( is . The significant factors that affect the number of these cases are the number of toddler receiving complete basic immunization and the number of toddler receiving health services in each district/city.

Using The Fully Modified M Method In Estimating The Effect Of Cultivated Area On Barley Production In Iraq For The Period 1970-2018

2020 ◽  
Vol 2020 (66) ◽  
pp. 101-110
Author(s):  
. Azhar Kadhim Jbarah ◽  
Prof Dr. Ahmed Shaker Mohammed
Keyword(s):  
Regression Model ◽  
Moving Average ◽  
Estimation Method ◽  
Estimation Methods ◽  
Autoregressive Moving Average ◽  
Moving Average Models ◽  
Error Terms ◽  
Relationship Of ◽  
The Relationship ◽  
Barley Crop

The research is concerned with estimating the effect of the cultivated area of barley crop on the production of that crop by estimating the regression model representing the relationship of these two variables. The results of the tests indicated that the time series of the response variable values is stationary and the series of values of the explanatory variable were nonstationary and that they were integrated of order one ( I(1) ), these tests also indicate that the random error terms are auto correlated and can be modeled according to the mixed autoregressive-moving average models ARMA(p,q), for these results we cannot use the classical estimation method to estimate our regression model, therefore, a fully modified M method was adopted, which is a robust estimation methods, The estimated results indicate a positive significant relation between the production of barley crop and cultivated area.

Agreement between Two Commonly Used Pulp Tests in Determining Pulp Vitality

2020 ◽  
Author(s):  
Tazeen Zehra
Keyword(s):  
Cold Test ◽  
P Value ◽  
Kappa Statistics ◽  
Test Results ◽  
Pulp Tissue ◽  
High Agreement ◽  
Qualitative Variables ◽  
Kappa Value ◽  
Tingling Sensation ◽  
Pulp Vitality

Background: The pulp tissue is confined within hard tissue boundaries and cannot be examined directly for health evaluation. Hence, its assessment is usually based on sensibility tests. Accurate diagnosis of the pulpal status requires evaluation with combination of sensibility tests. The objective of this study was to assess the mutual agreement between the electric pulp test and cold test in determining pulp vitality. Methods: Total 106 patients, aged 18 to 30 years, attending the dental OPD of Operative Dentistry/Endodontics department; Dr. Ishrat-ul-Ibad Institute of Oral Health Sciences, Karachi were selected for the study. The cold and electric pulp tests were used to determine the presence or absence of tingling sensation. Teeth were marked as vital in presence and non-vital in absence of any tingling or painful response. Agreement between these two tests was calculated using SPSS-18. To calculate the mean and standard deviation, descriptive statistics were applied for quantitative variables. For qualitative variables, frequencies and percentages were calculated. Kappa statistics was applied for agreement between electric pulp and cold test. Results: The cold test showed that 70.8% of the teeth were vital and 29.2% were necrotic. The electrical pulp test showed that 74.5% were vital and 25.5% were necrotic. The outcome showed 83.0% agreement between the tests; with kappa value of 0.574 which provided sufficient agreement with p-value=0.000. Conclusion: There was a high agreement found between the cold test and electric pulp test. Assessment of pulp vitality would be more accurate when EPT and Endo frost are used in combination.

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