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INDONESIA
Indonesian Journal of Statistics and Its Applications
Published by Institut Pertanian Bogor
ISSN : 25990802     EISSN : 25990802     DOI : -
Core Subject : Science, Education,
Computer Science & IT Mathematics Other
Indonesian Journal of Statistics and Its Applications (eISSN:2599-0802): diterbitkan berkala 2 (dua) kali dalam setahun yang memuat tulisan ilmiah yang berhubungan dengan bidang statistika dan aplikasinya. 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.
Arjuna Subject : -
Articles 163 Documents
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BINOMIAL REGRESSION IN SMALL AREA ESTIMATION METHOD FOR ESTIMATE PROPORTION OF CULTURAL INDICATOR Yudistira Yudistira; Anang Kurnia; Agus Mohamad Soleh
Indonesian Journal of Statistics and Applications Vol 2 No 2 (2018)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v2i2.63

Abstract

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.
PENGGEROMBOLAN DESA/KELURAHAN BERDASARKAN INDIKATOR KEMISKINAN DENGAN MENERAPKAN ALGORITMA TSC DAN K-PROTOTYPES Andrew Donda Munthe; I Made Sumertajaya; Utami Dyah Syafitri
Indonesian Journal of Statistics and Applications Vol 2 No 2 (2018)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v2i2.169

Abstract

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.
MODELLING THE NUMBER OF NEW PULMONARY TUBERCULOSIS CASES WITH GEOGRAPHICALLY WEIGHTED NEGATIVE BINOMIAL REGRESSION METHOD Tsuraya Mumtaz; Agung Priyo Utomo
Indonesian Journal of Statistics and Applications Vol 2 No 2 (2018)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v2i2.175

Abstract

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 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.
ESTIMASI KEBUTUHAN IMPOR DAGING SAPI UNTUK KONSUMSI RUMAH TANGGA DI INDONESIA MENGGUNAKAN REGRESI ROBUST Ratnasari Ratnasari; Ray Sastri
Indonesian Journal of Statistics and Applications Vol 2 No 2 (2018)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v2i2.186

Abstract

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.
GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSION DENGAN FUNGSI KERNEL FIXED GAUSSIAN PADA KEMISKINAN JAWA TENGAH Wulandari Wulandari
Indonesian Journal of Statistics and Applications Vol 2 No 2 (2018)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v2i2.189

Abstract

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.
ANALISIS AMMI DENGAN RESPON GABUNGAN PADA UJI STABILITAS TANAMAN PADI GOGO DI KABUPATEN PACITAN Abdullah Ilman Fahmi; Rahma Anisa; Anang Kurnia; Indonesian Journal of Statistics and Its Applications IJSA
Indonesian Journal of Statistics and Applications Vol 3 No 1 (2019)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v3i1.173

Abstract

Gogo rice is one of the results of various rice cultivation development by planting in a dry land. Gogo rice is expected to give yield a better production of paddy in dry rice fields. The varieties Inpago 7, Inpago 8, Inpago 8 IPB, Inpago 9, Inpago 10, Situ Gintung, Situ Patenggang, Situ Bagendit, Gajah Mungkur, Slengreng TG, Slegreng GK, Srijaya, Towuti, Merah Wangi, dan Inpari 24 were used in this study. This study aims to identify the Gogo rice varieties that are stable and superior in six Pacitan Garden Experimental Plant locations based on a combined response using the AMMI method. The AMMI analysis combines an additive variety analysis as the main effects of treatment with multiple principle component analysis by bilinier modeling for interaction effect. This study used two combined responses, which described the plant productivity and the resistancy. The result of this study explained that some varieties, Inpago 8, Inpago 10, and Situ Patenggang, were stable varieties in all planting location based on the combined responses. According to productivity stability and plant resistancy superior gogo rice variety is Inpago 8 and Inpago 10.
KAJIAN MODEL PERAMALAN KUNJUNGAN WISATAWAN MANCANEGARA DI BANDARA KUALANAMU MEDAN TANPA DAN DENGAN KOVARIAT Isti Rochayati; Utami Dyah Syafitri; I Made Sumertajaya; Indonesian Journal of Statistics and Its Applications IJSA
Indonesian Journal of Statistics and Applications Vol 3 No 1 (2019)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v3i1.171

Abstract

Foreign tourist arrivals could be considered as time series data. Modelling these data could make use of internal and external factors. The techniques employed here to model these time series data are SARIMA, SARIMAX, VARIMA, and VARIMAX. SARIMA is a model for seasonal data and VARIMA is a model for multivariate time series data. If some explanatory variables are incorporated and have significant influence on the response, the former two models become SARIMAX and VARIMAX respectively. Three stages of creating the model are model identification, parameter estimation, and model diagnostics. The variables used in this study were foreign tourist visits, international passenger arrivals, inflation rates, currency exchange rates, and Gross Regional Domestic Product (GRDP) over the period of 2010-2017. All four models fulfill their model assumptions and therefore could be applied. The best model of foreign tourist arrivals was VARIMA with the value of MAPE testing data = 6.123.
DETERMINAN INISIASI MENYUSU DINI (IMD) WANITA USIA 15-49 TAHUN DI INDONESIA (ANALISIS DATA SDKI 2012) Nur Aini; Budyanra Budyanra; Indonesian Journal of Statistics and Its Applications IJSA
Indonesian Journal of Statistics and Applications Vol 3 No 1 (2019)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v3i1.176

Abstract

Early Breastfeeding Initiation (EBI) is one of the most effective ways to reduce neonatal mortality in Indonesia. Implementation of EBI in Indonesia in 2012 is still in the "adequate" category according to World Health Organization (WHO) and is in "less" category according to International Baby Food Action Network (IBFAN). Implementation of EBI in Indonesia is still under other ASEAN Association countries such as Philippines, Cambodia and Myanmar. The low application of the EBI is thought to be influenced by maternal factors as well as environmental factors. This study aims to determine the factors that affect the status of EBI in Indonesia and see a general description of the status of EBI based on its characteristics. The data used are raw data of IDHS 2012 and analyzed using logistic regression model of proportional partial ordinal odds. The results obtained are the variables of antenatal care visit, maternal working status, place of residence, place of delivery, method of delivery, and parity are determinant of EBI status in Indonesia.
DETERMINAN PEMILIHAN MODA TRANSPORTASI PEKERJA KOMUTER JABODEBATEK DENGAN MODEL REGRESI LOGISTIK MULTINOMIAL MULTILEVEL Hernanto Adwiluvito; Indonesian Journal of Statistics and Its Applications IJSA
Indonesian Journal of Statistics and Applications Vol 3 No 1 (2019)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v3i1.184

Abstract

The BPS noted that commuters in Jabodetabek had increased by 400 thousand people from 2001 to 2014. The BPS also recorded that around 81,3% of the commuters in Jabodetabek were workers. A growing number of commuter workers in Jabodetabek makes transportation is very important to support the connection of suburban area and workplace in Jakarta. The result showed that 73% of the commuter workers used private transportation, 19% used ground public transportation and the rest of commuter workers used train. This research use Jabodetabek Commuter Survey 2014 as the main source data to shed light on how socioeconomic factors and spatial attributes affect the selection of a primary mode of transportation for commuter workers. Using multilevel multinomial logistic regression, the result confirm that the age, sex, marital status, ownership of vehicle, travel distance and time have a significant effect in explaining train choice. Furthermore, the result also showed that the age, sex, marital status, income, ownership of vehicle, travel distance and cost are found to be significant in explaining ground public transportation choice.
KAJIAN EFEK SPASIAL KASUS DIFTERI DENGAN GEOGRAPHICALLY WEIGHTED NEGATIVE BINOMIAL REGRESSION (GWNBR) Diva Arum Mustika; Rani Nooraeni; Indonesian Journal of Statistics and Its Applications IJSA
Indonesian Journal of Statistics and Applications Vol 3 No 1 (2019)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v3i1.185

Abstract

Diphtheria is an infectious disease caused by the Corynebacterium diphtheriae bacteria. Indonesia is the country with the most cases of diphtheria in Southeast Asia and ranks third in the world. In 2016, cases of diphtheria increased by 65 percent and became Extraordinary Events (KLB) in Indonesia, even though during 2013 to 2015 the number of cases of diphtheria has decreased. The province that has the highest number of diphtheria cases in Indonesia in 2016 is East Java. Diphtheria is centered and spread in certain districts / cities in East Java Province so that there are indications of spatial effects in the spread of diphtheria. Because data on the number of diphtheria cases overdispersed and indicated spatial effects in its spread, the main method used in this study was Geographically Weighted Negative Binomial Regression (GWNBR). This method will be compared with other alternative methods namely Poisson regression method and Negative Binomial Regression to get the best modeling. Based on the AIC value of each model it can be concluded that the best method for modeling the number of diphtheria cases is GWNBR. The modeling results with GWNBR show that there is indeed a spatial influence on the number of diphtheria cases and risk factors in East Java Province in 2016. The percentage of DPT-HB3 / DPT-HB-Hib3 immunization coverage is not significant in all observation areas, while the percentage of drug and vaccine availability is significant at entire observation area.

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