https://journal.stats.id/index.php/ijsa/issue/feed Indonesian Journal of Statistics and Its Applications 2020-12-24T23:55:58+07:00 Agus M Soleh agusms@apps.ipb.ac.id Open Journal Systems <p><strong>Indonesian Journal of Statistics and Its Applications (<a href="http://issn.pdii.lipi.go.id/issn.cgi?daftar&amp;1510202061&amp;1&amp;&amp;2017">eISSN:2599-0802</a>)&nbsp;(formerly named <a href="https://journal.ipb.ac.id/index.php/statistika" target="_blank" rel="noopener">Forum Statistika dan Komputasi</a>),&nbsp;</strong><strong>established since 2017</strong><strong>, </strong>publishes scientific papers in the area of statistical science and the applications. The published papers should be research papers with, but not limited to, the following topics: experimental design and analysis, survey methods and analysis, operation research, data mining, statistical modeling, computational statistics, time series and econometrics, and statistics education.&nbsp;&nbsp; All papers were reviewed by peer reviewers consisting of experts and academicians across universities and agencies.&nbsp;This journal is <strong>nationally accredited&nbsp;(SINTA 3)</strong> by Directorate General of Research and Development Strengthening (DGRDS),&nbsp;Ministry of Research, Technology and Higher Education of the Republic of Indonesia No.: <a href="http://arjuna.ristekdikti.go.id/files/berita/Surat_Pemberitahuan_Hasil_Akreditasi_Jurnal_Ilmiah_Elektronik_Periode_III_Tahun_2019_dan_Lampiran.pdf" target="_blank" rel="noopener">14/E/KPT/2019, dated 10 May 2019</a>.&nbsp;</p> <p><strong>Scope:</strong><br>Indonesian Journal of Statistics and Its Applications is a refereed journal committed to the Statistics and its applications.</p> <p><strong>Issue</strong><em>&nbsp;</em><strong>Released</strong>:&nbsp;<em>28 February (No 1),&nbsp; 30 June (No 2), and 31 October (No 3).&nbsp;</em></p> https://journal.stats.id/index.php/ijsa/article/view/333 KAJIAN PENGARUH PENAMBAHAN INFORMASI GEROMBOL TERHADAP PREDIKSI AREA NIRCONTOH PADA DATA BINOMIAL 2020-05-21T03:09:18+07:00 Beny Trianjaya benyt84@gmail.com Anang Kurnia akstk29@gmail.com Agus M Soleh agusms@apps.ipb.ac.id <p>Employment data is one of the important indicators related to the development progress of a country. Labor conditions in the territory of Indonesia can only be compared between times through the Survei Angkatan Kerja Nasional (Sakernas) data. Data generated from Sakernas and published by BPS is the number of employed and unemployed. The obstacle in estimating the semester unemployment rate at the regency/municipality level is the lack of a number of examples. One of the indirect estimates currently developing is small area estimation (SAE). This study developed the generalized linear mixed model (GLMM) by adding cluster information and examines the development of modifications with several model scenarios. The purpose of this study was to develop a prediction model for basic GLMM on a small area approach by adding cluster information as a fixed effect or random effect. The simulation results show that Model-2, a model that adds a fixed effect k-cluster and also adds a mean from the estimated effect of random areas in the sample area, is the best model with the smallest relative bias (RB) and Relative root mean squares error (RRMSE). This model is better than the basic GLMM model (Model-0) and Model-1 (a model which only adds a mean from the estimated random effect area in the sample area). Model-2 is applied to estimate the proportion of unemployed sub-district level in Southeast Sulawesi Province. Estimating the proportion of unemployed with calibration Model-2 produced an estimated aggregation of the unemployment proportion of Southeast Sulawesi Province at 0.0272. These results are similar to BPS (0.0272). Thus, the results of the estimated proportion of unemployment at the sub-district level with a calibration Model-2 can be said to be feasible to use.</p> 2020-12-24T00:00:00+07:00 Copyright (c) 2020 Indonesian Journal of Statistics and Its Applications https://journal.stats.id/index.php/ijsa/article/view/584 PENGEMBANGAN MODEL PERAMALAN SPACE TIME 2020-11-05T05:54:59+07:00 Evita Choiriyah evita_choiriyah@apps.ipb.ac.id Utami Dyah Syafitri utamids@apps.ipb.ac.id I Made Sumertajaya imsjaya.stk@gmail.com <p>Based on Statistics Indonesia (BPS) South Sulawesi is one of the national rice granary province. There are three regions, Bone, Wajo, and Gowa that contribute to the high production of rice in South Sulawesi. However, rice production in Indonesia especially South Sulawesi often declined sharply due to climate disturbances, such as drought or flood. Therefore, Indonesia's government should provide a forecast related to rice production accurately to ensure the availability of food stocks as an integral part of national food security. Moreover, rainfall as climate factors should be included to produce an appropriate forecast model that can be expected to generate the estimation of the rice production data accurately. This research focused on comparing the forecasting model of rice production data by SARIMAX and GSTARIMAX model and used rainfall as explanatory variables. The SARIMAX model is a multivariate time series forecasting model that can accommodate the seasonal components. In contrast, the GSTARIMAX model, which is equipped with an inverse distance spatial weight matrix, is a space-time forecasting model that involves interconnection between locations. The GSTARIMAX model built for rice production forecasting in Bone, Wajo, and Gowa is GSTARIMAX (2,1,0)(0,1,1)12. Rainfall as an explanatory variable was significant at each location. The comparison of rice production forecasting models for the next six periods in four locations showed that the GSTARIMAX model provided more stable forecasting results than the SARIMAX model, viewed from the average MAPE value of the GSTARIMAX mode in each location.</p> 2020-12-25T00:00:00+07:00 Copyright (c) 2020 Indonesian Journal of Statistics and Its Applications https://journal.stats.id/index.php/ijsa/article/view/525 PENGGEROMBOLAN TWEET BADAN NASIONAL PENANGGULANGAN BENCANA INDONESIA PERIODE AGUSTUS 2018 FEBRUARI 2019 MENGGUNAKAN TEXT MINING 2020-12-16T06:06:03+07:00 Windyana Pusparani windywindaay@gmail.com Agus M Soleh agusms@apps.ipb.ac.id Akbar Rizki akbar.ritzki@gmail.com <p>Twitter is a popular social media platform for communicating between its users by writing short messages in limited characters, called tweets. Extracting data information that has non-structured form and huge-sized, usually known as text mining. Badan Nasional Penanggulangan Bencana Indonesia (@BNPB_Indonesia) is the official twitter account of the government agency in the field of disaster management that uses twitter to share much information about disasters that have occurred in Indonesia. This study aims to determine the characteristics of all tweets and to group the types of tweets that they shared based on the similarity of its content. The data used in the study came from BNPB Indonesia's tweets with the period of taking tweets 6th of August 2018 to 16th of February 2019. The cluster result obtained by the k-Means method was 4 groups. The characteristics of the first cluster contained information about the weather conditions in Yogyakarta, the second cluster was about the source and magnitude of an earthquake, and the third group was about the occurrence of earthquakes in Lombok. However, the fourth group characteristic couldn’t be specifically identified because there was no clear distinction between other tweets in its members.</p> 2020-12-25T00:00:00+07:00 Copyright (c) 2020 Indonesian Journal of Statistics and Its Applications https://journal.stats.id/index.php/ijsa/article/view/654 DAMPAK KEBIJAKAN PEMERINTAH TERHADAP PEMBANGUNAN EKONOMI D. I. YOGYAKARTA 2001-2017 2020-11-26T05:20:52+07:00 Anissa Dika Larasati anissa.dika@bps.go.id Vera Lisna veralisna@bps.go.id <p>Economic development includes increasing economic growth and alleviating poverty. D.I Yogyakarta is a province with the lowest economic growth and per capita income compared to other provinces in Java. Besides, it has the highest poverty rate. With this condition, it is feared that economic development and economic contribution in D.I Yogyakarta which are relatively low are difficult to increase. This study aims to analyze the simultaneous relationship between indicators of economic development in the province of D.I Yogyakarta, explores the variables that influence it, and perform policy simulations to improve economic development. The indicators used to describe economic growth in this study are Regional Gross Domestic Product (regional GDP), household consumption, and community savings in banks. While the indicators that are used to reflect the poverty level are the percentage of poor people. The estimation method used is simultaneous Two-Stage Least Squares (2SLS) equation system which consisted of three structural equation and one identity equation using the historical data from the year 2001-2017. The results of the simulation show a 6% increase in government expenditure can improve economic growth to 5.41% and reduce the percentage of poor people by 0.41% points.</p> 2020-12-25T00:00:00+07:00 Copyright (c) 2020 Indonesian Journal of Statistics and Its Applications https://journal.stats.id/index.php/ijsa/article/view/689 KAJIAN VARIANCE MEAN RATIO PADA SIMULASI SEBARAN DATA BINOMIAL NEGATIF 2020-11-24T09:02:53+07:00 Choirun Nisa choir19cca@gmail.com Muhammad Nur Aidi nuraidi18081960@gmail.com I Made Sumertajaya imsjaya.stk@gmail.com <p>The negative binomial distribution is one of the data collection counts that focuses on success and failure events. This study conducted a study of the distribution of negative binomial data to determine the characterization of the distribution based on the value of Variance Mean Ratio (VMR). Simulation data are generated based on negative binomial distribution with a combination of <em>p</em> and <em>n</em> parameters. The results of the VMR study on negative binomial distribution simulation data show that the VMR value will be smaller when the <em>p-value</em> is large and the VMR value is more stable as the sample size increases. Simulation data of negative binomial distribution when <em>p</em>≥0.5 begins to change data distribution to the distribution of Poisson and binomial. The calculation VMR value can be used as a reference for detecting patterns of data count distribution.</p> 2020-12-25T00:00:00+07:00 Copyright (c) 2020 Indonesian Journal of Statistics and Its Applications https://journal.stats.id/index.php/ijsa/article/view/709 PERBANDINGAN MODEL GARCH SIMETRIS DAN ASIMETRIS PADA DATA KURS HARIAN 2020-12-08T05:51:41+07:00 Isna Shofia Mubarokah isna_shofia@apps.ipb.ac.id Anwar Fitrianto anwarstat@gmail.com Farit M Affendi fmafendi@gmail.com <p>ARCH and GARCH models are widely used in financial data to describe its volatility pattern. The models assume the positive and negative return residual gives the same or symmetric influence on its volatility. However, in reality, this assumption is frequently violated, which is called heteroscedasticity. Therefore, to deal with heteroscedasticity and asymmetric data, the asymmetric GARCH models, which are EGARCH and GJR-GARCH models are used. This research aims to compare the models between symmetric and asymmetric GARCH to make financial data modeling. It uses daily data on three foreign exchange rates for IDR including IDR/CNY, IDR/JPY, and IDR/USD. The data series to be used here are from January 4, 2016, to January 20, 2020. This research method is started by selecting the best mean model for each data. Based on the best mean model, then modeling the mean and variance function are simultaneously conducted using the GARCH model. To test whether there was an asymmetric effect on the data, a Lagrange multiplier test was applied on the residuals of the GARCH model. The results show that the asymmetric effect was found in the IDR/CNY and IDR/JPY exchange rates. To overcome this asymmetric effect, EGARCH and GJR-GARCH model were applied to the two exchange rates. Then the two models are compared to find out which volatility model is better. Using AIC and BIC we find EGARCH as the best model for IDR/CNY exchange rates daily return and GJR-GARCH as the best model for IDR/JPY exchange rates daily return.</p> 2020-12-25T00:00:00+07:00 Copyright (c) 2020 Indonesian Journal of Statistics and Its Applications https://journal.stats.id/index.php/ijsa/article/view/716 ANALISIS JUMLAH KASUS MALARIA DI WILAYAH SUMATERA MENGGUNAKAN GEOGRAPHICALLY WEIGHTED ZERO-INFLATED POISSON REGRESSION (GWZIPR) 2020-12-16T05:38:44+07:00 Rahmat Kevin Praditia rahmatkevin3@gmail.com Dian Agustina dianagustina@unib.ac.id Dyah Setyo Rini dyah.setyorini@unib.ac.id <p>A method that can be used if there is a spatial factor and if overdispersion happens in a count data is Geographically Weighted Zero-Inflated Poisson Regression (GWZIPR). This research aimed to analyze the number of malaria cases in every regency/city of Sumatra Land using the GWZIPR method and distribution mapping of factors affecting the number of malaria cases in Sumatra Land. Data involved in this research was the number of malaria cases as the response variable and the predictor variable as a percentage of households that have access to proper sanitation, a percentage of households that have access to proper water resources, and a percentage of the number of public health centers. The results were for each area which had distinctive models based on significant variables. The distribution mapping of factors affecting the number of malaria cases in every regency/city was commonly divided into three groups based on significant variables on ln and logit models. The mapping did not shape a spreading pattern or each regency/city in that group because the geographical locations were close to each other. GWZIPR method in this research was better than the ZIP Regression method because it produced the least AIC value.</p> 2020-12-25T00:00:00+07:00 Copyright (c) 2020 Indonesian Journal of Statistics and Its Applications https://journal.stats.id/index.php/ijsa/article/view/724 PEMODELAN STATISTICAL DOWNSCALING DENGAN LASSO DAN GROUP LASSO UNTUK PENDUGAAN CURAH HUJAN 2020-12-09T07:09:21+07:00 M. Yunus myunus@uinjambi.ac.id Asep Saefuddin asaefuddin@apps.ipb.ac.id Agus M Soleh agusms@apps.ipb.ac.id <p>One of the rainfall prediction techniques is the Statistical Downscaling Modeling (SDS). SDS modeling is one of the applications of modeling with covariates conditions that are generally large and not independent. The problems that will be encountered is the problem of ill-conditional data i.e multicollinearity and the high correlation between variables. The case of highly correlated data causes a linear regression coefficient estimators obtained to have a large variance. This research was conducted to make the statistical downscaling modeling using the lasso and group lasso for the prediction of rainfall. Group of the covariate scenario is applied based on the adjacent area, the high correlation between covariates and correlation between covariates and responses, and also the addition of dummy variables. Scenario six (grouping which is done by considering the covariates that have a positive correlation to the response is divided into 3 groups, 1 individual and the covariates that are negatively correlated with the response are divided into 2 groups, 1 individual) is better than the other scenarios in linear modeling without a dummy. Then, linear modeling with a dummy is better than without a dummy for both techniques. In linear modeling with a dummy, the Group lasso technique can be considered more in SDs modeling, because the difference in the RMSEP statistical value and the correlation coefficient value is significant.</p> 2020-12-25T00:00:00+07:00 Copyright (c) 2020 Indonesian Journal of Statistics and Its Applications