Indonesian Journal of Statistics and Its Applications <p><strong>Indonesian Journal of Statistics and Its Applications (<a href="">eISSN:2599-0802</a>):</strong>&nbsp;diterbitkan berkala 2 (dua) kali dalam setahun yang memuat tulisan ilmiah yang berhubungan dengan bidang statistika dan aplikasinya. &nbsp;Artikel yang dimuat berupa hasil penelitian bidang statistika dan aplikasinya dengan topik (tapi tidak terbatas): rancangan dan analisis percobaan, metodologi survey dan analisis, riset operasi, data mining, pemodelan statistika, komputasi statistika, time series dan ekonometrika, serta pendidikan statistika.</p> <p>&nbsp;</p> en-US (Agus M Soleh) (Agus M Soleh) Fri, 30 Nov 2018 00:00:00 +0000 OJS 60 BINOMIAL REGRESSION IN SMALL AREA ESTIMATION METHOD FOR ESTIMATE PROPORTION OF CULTURAL INDICATOR <p>In sampling survey, it was necessary to have sufficient sample size in order to get accurate direct estimator about parameter, but there are many difficulties to fulfill them in practice. Small Area Estimation (SAE) is one of alternative methods to estimate parameter when sample size is not adequate. This method has been widely applied in such variation of model and many fields of research. Our research mainly focused on study how SAE method with binomial regression model is applied to obtained estimate proportion of cultural indicator, especially to estimate proportion of people who appreciate heritages and museums in each regency/city level in West Java Province. Data analysis approach used in our research with resurrected data and variables in order to be compared with previous research. The result later showed that binomial regression model could be used to estimate proportion of cultural indicator in Regency/City in Indonesia with better result than direct estimation method.</p> Yudistira Yudistira, Anang Kurnia, Agus Mohamad Soleh ##submission.copyrightStatement## Fri, 30 Nov 2018 00:00:00 +0000 PENGGEROMBOLAN DESA/KELURAHAN BERDASARKAN INDIKATOR KEMISKINAN DENGAN MENERAPKAN ALGORITMA TSC DAN K-PROTOTYPES <p>Statistic Indonesia (BPS) noted that in 2014 there were 3.270 villages in Nusa Tenggara Timur Province. Most of them have a high percentage of poverty. Therefore, the village clustering based on poverty indicators is very important. The clustering algorithm that can be used on large data size and with mixed variables are Two Step Cluster (TSC) and K-Prototypes. The purpose of this research is to compare of TSC and K-Prototypes algorithm for village clustering in Nusa Tenggara Timur Province based on poverty indicators. The data were taken from 2014 village potential data (PODES 2014) collected by BPS. The best selection criteria for the cluster is the minimum ratio between variance within groups and variance between groups. The result showed that the best clustering algorithm was TSC which had the smallest ratio (2.6963). The best clustering showed that villages in Nusa Tenggara Timur Province divided into six groups with different characteristics.</p> Andrew Donda Munthe, I Made Sumertajaya, Utami Dyah Syafitri ##submission.copyrightStatement## Fri, 30 Nov 2018 00:00:00 +0000 MODELLING THE NUMBER OF NEW PULMONARY TUBERCULOSIS CASES WITH GEOGRAPHICALLY WEIGHTED NEGATIVE BINOMIAL REGRESSION METHOD <p>Tuberculosis (TB) is an infectious disease caused by Mycobacterium Tuberculosis. Untill now, TB is still one of the main problems in many countries, especially developing countries. Indonesia ranked second as the country with the highest TB cases in the world in 2015, where most cases were found in Java. This study was conducted to model the number of new pulmonary TB cases in Java by considering the spatial aspects using Geographically Weighted Negative Binomial Regression (GWNBR). GWNBR method was chosen &nbsp;because the data used in this study are overdispered. The result showed that the population density and percentage of healty homes were not significantly influential in each region. While the number of puskesmas, the percentage of smokers, the percentage of good PHBS, the percentage of diabetes mellitus, and the percentage of less IMT were significant in some regions. In general, the GWNBR model was better for modelling the number of new pulmonary TB cases than negative binomial regression and GWPR.</p> Tsuraya Mumtaz, Agung Priyo Utomo ##submission.copyrightStatement## Fri, 30 Nov 2018 00:00:00 +0000 ESTIMASI KEBUTUHAN IMPOR DAGING SAPI UNTUK KONSUMSI RUMAH TANGGA DI INDONESIA MENGGUNAKAN REGRESI ROBUST <p>Beef import to Indonesia always gets pros and cons. The government argue that we need it to reduce the high price of beef due to the scarcity. On the other hand, Indonesia is an agrarian country with a lot of cattle farms. We should be able to meet the needs of beef from domestic production without import. The aim of this study is to get the best model for household consumption of beef at the district level, and use the model to estimate the import needs. This study uses data from Statistics Indonesia, both the raw data of National Sosio-economic Survey (SUSENAS) and beef production in district level. The methods of analysis is a robust regression model. The results is robust regression fit the data well. For households need, estimation of household consumption of beef is lower than domestic production. So that, Indonesia does not need to import beef for household need.</p> Ratnasari Ratnasari, Ray Sastri ##submission.copyrightStatement## Fri, 30 Nov 2018 00:00:00 +0000 GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSION DENGAN FUNGSI KERNEL FIXED GAUSSIAN PADA KEMISKINAN JAWA TENGAH <p>Poverty alleviation is a problem faced by many countries in the world, included Indonesia. Poverty in Indonesia still relatively high. Poverty is one indicator of welfare. In general, the decline in poverty means that people's welfare increasing. Poverty is a multi-dimensional problem, which involves various microeconomic and macroeconomic factors, including the influence of the surrounding region. Modeling with geographically weighted regression (GWR) accommodates heterogeneous effects of independent variables on the dependent variable and produces a local parameter estimates. Central Java has the second highest poverty rate among provinces in Java. This study will model poverty in Central Java with a model that accommodates the influence of the surrounding region, named Geographically Weighted Logistic Regression (GWLR). Poverty modeling in Central Java with GWLR, in general, literacy rates (AMH), per capita GRDP, and Labor Force Participation Rate (TPAK) significantly affected poverty in Central Java with values that varied between districts / cities.</p> Wulandari Wulandari ##submission.copyrightStatement## Fri, 30 Nov 2018 00:00:00 +0000