Indonesian Journal of Statistics and Its Applications
http://journal.stats.id/index.php/ijsa
<p><strong>Indonesian Journal of Statistics and Its Applications (<a href="http://u.lipi.go.id/1510202061">eISSN:2599-0802</a>) (formerly named <a href="http://journal.ipb.ac.id/index.php/statistika" target="_blank" rel="noopener">Forum Statistika dan Komputasi</a>), </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, 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. All papers were reviewed by peer reviewers consisting of experts and academician across universities and agencies. This journal is <strong>nationally accredited (SINTA 3)</strong> by Directorate General of Research and Development Strengthening (DGRDS), 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>. </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>Departemen Statistika, IPB dengan Forum Perguruan Tinggi Statistika (FORSTAT)en-USIndonesian Journal of Statistics and Its Applications2599-0802ANALISIS VARIABEL-VARIABEL YANG MEMPENGARUHI PERTUMBUHAN EKONOMI DI PROVINSI KEPULAUAN BANGKA BELITUNG TAHUN 2008-2015
http://journal.stats.id/index.php/ijsa/article/view/194
<p>Economic growth of a region can assess from various aggregate sizes, one of them is GDRP (Gross Regional Domestic Product). Based on theory, GDRP can influenced by several variables, including world commodity prices which have the largest share of GDP, labor force participation rate (LFPR), Human Development Index (HDI), income inequality, open unemployment rate and percentage of the poor. In 2015 Bangka Belitung Islands Province GRDP had a share of around 0.5 percent of Indonesia's GDP. The largest share is West Bangka Regency with 11.46 trillion rupiahs, while the smallest one is East Belitung with 6.112 trillion rupiahs.To find out picture of economic growth and the influence of variable prices of palm oil commodities, LFPR, HDI income inequality, open unemployment and the percentage of the poor on economic growth in the Bangka Belitung Islands Province 2008-2015, the method used is descriptive analysis and panel data regression.The best model for estimating GDRP growth in Bangka Belitung Islands Province in 2008-2015 is the fix effect model with <em>Seemingly Uncorrelated Regression</em> Method. With alpha 5 percent, the variables that significantly influence economic growth are HDI, the percentage of the poor, labor force participation rate (LFPR), income inequality, open unemployment rate and world commodity prices.economic growth</p>Syamsu Pratama
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2019-06-302019-06-303212413810.29244/ijsa.v3i2.194MODELING OF THE PERCENTAGE OF AIDS SUFFERERS IN EAST JAVA PROVINCE WITH NONPARAMETRIC REGRESSION APPROACH BASED ON SPLINE TRUNCATED ESTIMATOR
http://journal.stats.id/index.php/ijsa/article/view/209
<p>Acquired Immune Deficiency Syndrome (AIDS) is a set of symptoms and infection or a syndrome that arise due to damage to the human immune system. AIDS is a health problem that often occurs in developing countries, including in Indonesia. East Java Province was ranked first in the highest number of AIDS sufferers in Indonesia ever reported from 1987-2016 as many as 16,911 people out of a total of 86,780 people. In order to overcome AIDS cases, it is necessary to know the factors that influence it. Data on the percentage of AIDS sufferers and their predictor variables have irregular data patterns or do not match in certain patterns, then the method that can solve these problems is by using the nonparametric regression based on spline truncated estimator. A spline truncated estimator is a segmented polynomial function that has better flexibility because there are knot points indicating changes in data behaviour patterns. The data that used in this study is a secondary data in 2016 obtained from the East Java Provincial Health Office. The results showed that the determination coefficient (R<sup>2</sup>) based on the best model of 93.84%. This shows that the variables of health facilities, blood donors, health workers, condom users, and residents of 25-29 years are able to explain 93.84% of the percentage of AIDS sufferers in East Java Province in 2016.</p>Nadia MurbaraniYolanda SwastikaAnanda DwiBaktiar ArisNur Chamidah
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2019-06-302019-06-303213914710.29244/ijsa.v3i2.209PENGGUNAAN SUPPORT VECTOR REGRESSION DALAM PEMODELAN INDEKS SAHAM SYARIAH INDONESIA DENGAN ALGORITME GRID SEARCH
http://journal.stats.id/index.php/ijsa/article/view/172
<p>Indonesia as the largest Muslim population country in the world is a very potential market for sharia stocks. Sharia stocks performance can be seen from the Indonesia Sharia Stock Index (ISSI). Stock index modeling is conducted to determine the factors that affect the stock index or to predict the value of the stock index. Modeling using regression analysis is based on assumptions that do not always match with the characteristics of stock data that fluctuate. Support Vector Regression (SVR) method is a non-parametric approach based on machine learning. The problem often encountered in the analysis using SVR is to determine the optimal parameters to produce the best model. The determination of the optimal parameters can be solved by using the grid search algorithm. The purpose of this research is to make ISSI model using SVR with grid search algorithm with independent variable BI Rate, money supply, and exchange rate (USD / IDR). The best SVR model was obtained using weekly data with a total of 343 periods as well as a linear kernel with parameters ε = 0.03 and C = 2. The evaluation of the best model SVR is RMSE of 2.289 and correlation value of 0.873.</p>Galih Hedy SaputraAji Hamim WigenaBagus Sartono
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2019-06-302019-06-303214816010.29244/ijsa.v3i2.172PENENTUAN NILAI AMBANG BATAS SEBARAN PARETO TERAMPAT DENGAN MEASURE OF SURPRISE
http://journal.stats.id/index.php/ijsa/article/view/284
<p>Extreme rainfall can result in natural disasters such as floods and landslides. These natural disasters will cause damage and losses to the surrounding environment. Prevention of damage from natural disasters can be done by extreme rainfall estimation. Estimates of extreme rainfall are based on Generalized Pareto Distribution (GPD) which requires threshold value information. The threshold value can be determined by two methods, namely Mean Residual Life Plot (MRLP) and Measure of Surprise (MOS). The purpose of this study is to determine and compare the threshold values of MRLP and MOS. The data used are 10-day and monthly rainfall data. The results of this study indicate that the procedure of MOS is shorter and easier than that of MRLP. Based on the cross validation result, the log-likelihood value of MOS is larger than that of MRLP, then MOS is better than MRLP.</p>Yumna KarimahAji Hamim WigenaAgus Mohamad Soleh
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2019-06-302019-06-303216116810.29244/ijsa.v3i2.284PENERAPAN CYLINDRICAL DAN FLEXIBLE SPACE TIME SCAN STATISTIC DALAM MENGIDENTIFIKASI KANTONG KEMISKINAN DI PULAU JAWA TAHUN 2011-2015
http://journal.stats.id/index.php/ijsa/article/view/274
<p> The Indonesian government formed the National Team for the Acceleration of Poverty Reduction (TNP2K) to eradicate poverty. TNP2K requires identification of priority areas or poverty hotspots so that the program can be targeted. Scan statistic is one of the most widely used methods to identify poverty hotspots. Cylindrical STSS uses cylindrical scanning windows while most geographical areas are not circular. Flexible STSS is able to detect poverty hotspots in a flexible form. This study aims to identify poverty hotspots using Cylindrical and Flexible STSS then compare the results of both and then determine the best STSS method. Cylindrical STSS tends to have wider hotspots than Flexible STSS. There are a number of districts that are not eligible to be included as poverty Flexible STSS is able to produce better poverty hotspots by not including these districts Poverty hotspots produced by Flexible STSS have higher LLR values. The more suitable STSS method has optimal K values and high suitability with TNP2K priority areas. Cylindrical STSS has an optimal K value when K = 8 and 9. Flexible STSS has a constant LLR value. Flexible STSS has a higher LLR value than Cylindrical STSS at each K value. Flexible STSS with K = 9 has optimal K and high suitability with TNP2K priority areas so that it is the more suitable STSS method to identify poverty hotspots in Java.</p>Zaima NurrusydahErfiani ErfianiBagus Sartono
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2019-06-302019-06-303216918310.29244/ijsa.v3i2.274PEMODELAN DATA TERSENSOR KANAN MENGGUNAKAN ZERO INFLATED NEGATIVE BINOMIAL DAN HURDLE NEGATIVE BINOMIAL
http://journal.stats.id/index.php/ijsa/article/view/247
<p>Health is a very important thing for humanity. One way to look at a person's health condition is through the number of unhealthy days which can also shows the productivity of the community in a region. Modeling the number of unhealthy days which are examples of count data can be done using Poisson regression. Problems that are often faced in data counts are overdispersion and excess zero. Poisson regression cannot be applied to data that experiences both of these. Zero Inflated Negative Binomial and Hurdle Negative Binomial modeling was performed on data with 2 conditions, uncensored and censored. The explanatory variables used are gender, age, marital status, education level, home ownership status and rural-urban status. According to the results of the AIC and RMSE calculation, Zero Inflated Negative Binomial on censored data showed the best performance for estimating the number of unhealthy days.</p>Kusni Rohani RumahorboBudi SusetyoKusman Sadik
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2019-06-302019-06-303218420110.29244/ijsa.v3i2.247