Indonesian Journal of Applied Statistics
Indonesian Journal of Applied Statistics (IJAS) is a journal published by Universitas Sebelas Maret, Surakarta, Indonesia. This journal is published twice every year, in May and November. The editors receive scientific papers on the results of research, scientific studies, and problem solving research using statistical method. Received papers will be reviewed to assess the substance of the material feasibility and technical writing.
Articles
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Articles
Penerapan Model Geographically Weighted Regression(GWR) Pada Produksi Ubi Jalar

Susanti, Yuliana

Indonesian Journal of Applied Statistics Vol 1, No 1 (2018)
Publisher : Universitas Sebelas Maret

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Abstract

Sweet potatoes are a major source of carbohydrate, after rice, corn, and cassava. Sweet potato is consumed as an additional or side meal, except in Irian Jaya and Maluku, sweet potato is used as staple food. The main problem faced in increasing sweet potato production is still relies on certain areas, namely Java Island, as the main producer of sweet potato. Differences in production is what often causes the needs of sweet potato in various regions can not be fulfilled and there is a difference price of sweet potato. To fulfill the needs of sweet potato in Java, mapping areas of sweet potato production need to be made so that areas with potential for producing sweet potato can be developed while areas with insufficient quantities of sweet potato production may be given special attention. Due to differences in production in some areas of Java which depend on soil conditions, altitude, rainfall and temperatures, a model of sweet potato production will be developed using the GWR model. Based on the Geographically weighted regression model for each regencies / cities in Java Island, it can be concluded that the largest sweet potato production coming from Kuningan with R2 equal 99.86%.Keywords : Geographically weighted regression, model, sweet potato

Pengeluaran Pariwisata dan Karakteristik Sosial Demografi Rumah Tangga di Provinsi Jawa Tengah

Subanti, Sri, Hakim, Arif Rahman

Indonesian Journal of Applied Statistics Vol 1, No 1 (2018)
Publisher : Universitas Sebelas Maret

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Abstract

The study about tourism expenditure had been one of the important things in the formulation of tourism development, such as marketing analysis, strategies, and policies. Based on this condition, the purpose of our paper wants to know about the determinants of tourism expenditure at households level based on their demographic characteristics. The findings of this paper, (1) the important factors affecting household tourism expenditure are marital status, sex, household income per capita, education for heads of households, the length of study for household members in average, number of households, urban-rural, and industrial origin for head of household; (2) variables that are positively related to tourism expenditure are marital status, age, education, number of household, household income per capita, the length of study for household members in average, urban-rural, and home ownership. This paper suggest that the local governments should give an attention on households demographic characteristics to formulate the tourism marketing and the tourism policies.Keywords : tourism expenditure, demographic characteristics, households

Deteksi Krisis Keuangan di Indonesia Berdasarkan Indikator Nilai Tukar Riil Menggunakan Model SWARCH (2,3)

Sugiyanto, Sugiyanto, Zukhronah, Etik, Retnosari, Dewi

Indonesian Journal of Applied Statistics Vol 1, No 1 (2018)
Publisher : Universitas Sebelas Maret

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Abstract

The financial crisis that hit Asia in mid-1997 began with the financial crisis in Thailand which then spread to Indonesia. The impact of the financial crisis in Indonesia is so severe that a crisis detection system is needed. The financial crisis detection system can be done by simple monitoring of macroeconomic indicators such as real exchange rate. Excessive real exchange rate is predicted to have a great chance of crisis.The result shows that the real exchange rate from January 1990 to June 2013 has heteroscedasticity effect and there are structural changes so it can be modeled using SWARCH model (2,3) with ARMA (1.0) as conditional average model and ARCH (3) as model conditional variance. The inferred probabilities value of the SWARCH (2,3) model in February 1998 of 1 and July 1998 of 0.9968 over 0.5 indicates that the period is in a high volatile condition indicating a crisis. The SWARCH model (2.3) based on the real exchange rate indicator was able to capture the high volatile conditions in February 1998 and July 1998 as the impact of the 1997 Asian financial crisis.Keywords : Deteksi, krisis keuangan, nilai tukar riil, SWARCH

Bootstrap Residual Ensemble Methods for Estimation of Standard Error of Parameter Logistic Regression To Hypercolesterolemia Patient Data In Health Laboratory Yogyakarta

S.W., Fransiska Grace, Handajani, Sri Sulistijowati, Martini, Titin Sri

Indonesian Journal of Applied Statistics Vol 1, No 1 (2018)
Publisher : Universitas Sebelas Maret

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Abstract

Logistic regression is one of regression analysis to determine the relationship between response variable that have two possible values and some predictor variables. The method used to estimate logistic regression parameters is the maximum likelihood estimation (MLE) method. This method will produce a good estimate of the parameters if the estimation results have a small standard error.In a research, the characteristics of good data must be representative of the population. If the samples taken in small size they will cause a large standard error value. Bootstrap is a resampling method that can be used to obtain a good estimate based on small data samples. Small data will be resampling so it can represent the population to obtain minimum standard error. Previous studies have discussed resampling bootstrap on residuals as much as b times. In this research we will be analyzed resampling bootstrap on the error added to the dependent variable and take the average parameter estimation ensemble logistic regression model resampling result. Next we calculate the standard value error logistic regression parameters bootstrap results.This method is applied to the hypercholesterolemic patient status data in Health Laboratory Yogyakarta and after bootstrapping, the standard error produced is smaller than before the bootstrap resampling.Keywords : logistic regression, standard error, bootstrap resampling, parameter estimation ensemble

A Robust Regression by Using Huber Estimator and Tukey Bisquare Estimator for Predicting Availability of Corn in Karanganyar Regency, Indonesia

Pratiwi, Hasih, Susanti, Yuliana, Handajani, Sri Sulistijowati

Indonesian Journal of Applied Statistics Vol 1, No 1 (2018)
Publisher : Universitas Sebelas Maret

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Abstract

Linear least-squares estimates can behave badly when the error distribution is not normal, particularly when the errors are heavy-tailed. One remedy is to remove influential observations from the least-squares fit. Another approach, robust regression, is to use a fitting criterion that is not as vulnerable as least squares to unusual data. The most common general method of robust regression is M-estimation. This class of estimators can be regarded as a generalization of maximum-likelihood estimation. In this paper we discuss robust regression model for corn production by using two popular estimators; i.e. Huber estimator and Tukey bisquare estimator.Keywords : robust regression, M-estimation, Huber estimator, Tukey bisquare estimator

Back Matter Vol 1 No 1

Pratiwi, Hasih

Indonesian Journal of Applied Statistics Vol 1, No 1 (2018)
Publisher : Universitas Sebelas Maret

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Abstract

Analisis Faktor-Faktor yang Mempengaruhi Penyebaran Penyakit Demam Berdarah Dengue (Dbd) di Provinsi Jawa Tengah dengan Metode Spatial Autoregressive Model dan Spatial Durbin Model

Taryono, Arkadina Prismatika Noviandini, Ispriyanti, Dwi, Prahutama, Alan

Indonesian Journal of Applied Statistics Vol 1, No 1 (2018)
Publisher : Universitas Sebelas Maret

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Abstract

Dengue Hemorrhagic Fever is one of the major public health problems in Indonesia. From year to year, DHF causes Extraordinary Event in most parts of Indonesia, especially Central Java. Central Java consists of 35 districts or cities where each region is close to each other. Spatial regression is an analysis that suspects the influence of independent variables on the dependent variables with the influences of the region inside. In spatial regression modeling, there are spatial autoregressive model (SAR), spatial error model (SEM) and spatial autoregressive moving average (SARMA). Spatial durbin model is the development of SAR where the dependent and independent variable have spatial influence. In this research dependent variable used is number of DHF sufferers. The independent variables observed are population density, number of hospitals, residents and health centers, and mean years of schooling. From the multiple regression model test, the variables that significantly affect the spread of DHF disease are the population and mean years of schooling. Moran’s I test results stated that there are spatial dependencies between dependent and independent variables. The best model produced is the SAR model because it has the smallest AIC value of 49.61