Indonesian Journal of Statistics and Its Applications <p><strong>Indonesian Journal of Statistics and Its Applications (<a href="">eISSN:2599-0802</a>)&nbsp;(formerly named <a href="" 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, 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 academician 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="" 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> Departemen Statistika, IPB dengan Forum Perguruan Tinggi Statistika (FORSTAT) en-US Indonesian Journal of Statistics and Its Applications 2599-0802 ANALISIS SPASIAL UNTUK MENGIDENTIFIKASI TINGKAT PENGANGGURAN TERBUKA BERDASARKAN KABUPATEN/KOTA DI PULAU JAWA TAHUN 2017 <p>Unemployment is one of the economic problems faced by many countries. In Indonesia, the total workforce has reached 128.06 million and 7.04 million people are unemployed. The indicator to measure unemployment is open unemployment rate (TPT). Java Island becomes the island with the highest TPT, which is 4.04 million people, equivalent to 63.08 percent. The regions that have high TPT rates tend to be in the western region of Java, while the eastern region of Java is moderate. This is an initial allegation of regional influence so spatial analysis needs to be carried out. On the other hand, not many studies have included territorial effects. This study aims to spatially identify the influence of human development index (IPM), labor force particapation rate (TPAK), minimum wage and the dependency ratio on the number of TPT in Java in 2017 with the geographically weighted regression (GWR) method. The results of this study indicate that there are differences in the influence of IPM, TPAK, minimum wage and the dependency ratio on TPT in each area in Java. The most significant independent variables and have a positive relationship are minimum wage. This research also shows that GWR is suitable to be applied in modeling the number of TPT regencies /cities in Java Island in 2017. The results of this study can be used by the government in determining the right policy by looking at regional aspects in overcoming unemployment.</p> Eka Amalia Liza Kurnia Sari ##submission.copyrightStatement## 2019-10-31 2019-10-31 3 3 202 215 10.29244/ijsa.v3i3.240 ANALISIS PENGARUH DAERAH PEMASOK TERHADAP HARGA CABAI MERAH DI DKI JAKARTA MENGGUNAKAN VECTOR ERROR CORRECTION MODEL (VECM) <p>This study aims to analyze the effect of red chili price and production in the supplier area on its prices in DKI Jakarta using the Vector Error Correction Model (VECM). The data used in this study are red chili price and average expenditure per month per capita in DKI Jakarta and red chili price and production in East Java, West Java, and Banten in the period January 2012 to July 2018. The model obtained was VECM (1) the price of red chili in DKI Jakarta. It showed that there was a long-term relationship (cointegration) on the first difference. The results the Forecast Error Variance Decomposition (FEVD) analysis showed that the contributions of the red chili price in DKI Jakarta and West Java, average monthly expense for red chili in DKI Jakarta, red chili production (West Java and Banten) are significant in explaining the behaviour of the red chili price change in DKI Jakarta. The results of the Impulse Response Function (IRF) analysis showed that the shock of the price of chili in DKI Jakarta and West Java in the previous month will increase the price of red chili in DKI Jakarta in the following month. Conversely, the shock of the average monthly expenditure of red chili in DKI Jakarta and red chili production (West Java and Banten) from the previous month will reduce the price of red chili in DKI Jakarta in the following month.</p> Erwandi Erwandi Farit Mochamad Afendi Budi Waryanto ##submission.copyrightStatement## 2019-10-31 2019-10-31 3 3 216 235 10.29244/ijsa.v3i3.276 PEMODELAN CLUSTERWISE REGRESSION PADA STATISTICAL DOWNSCALING UNTUK PENDUGAAN CURAH HUJAN BULANAN <p>Statistical downscaling (SDS) is one of the developing models for rainfall estimation. The SDS model is a regression model used to analyze the relation of global (GCM output) and local data (rainfall). Rainfall has large variance so that clustering is needed to minimize the variance. One of the analytical methods that can be used in clustering rainfall estimation is cluster wise regression. There are three Methods for Clusterwise regression namely Linear Regresion, Finite Mixture Method (FMM) and Cluster-Weighted Method (CWM). This study used GCM outputs data namely CFRSv2 as a covariate. The response variable is rainfall data in four stations such as Bandung, Bogor, Citeko and Jatiwangi from BMKG. The purpose of this study is to increase the accuracy of rainfall estimation using the three methods and compare the clusterwise regression with PCR and PLS models. Based on the value of RMSEP, the clusterwise regression with FMM was the best method to estimate rainfall in four stations.</p> Victor Pandapotan Butar-butar Agus M Soleh Aji H Wigena ##submission.copyrightStatement## 2019-10-31 2019-10-31 3 3 236 246 10.29244/ijsa.v3i3.310 PENINGKATAN AKURASI KLASIFIKASI INTERAKSI FARMAKODINAMIK OBAT BERBASIS SELEKSI PASANGAN OBAT TAKBERINTERAKSI <p>Identifying the pharmacodynamics drug-drug interaction (PD DDI) is needed since it can cause side effects to patients. There are two measurements of drug interaction performance, namely the golden standard positive (GSP) which is the drug pairs that interact pharmacodynamics and golden standard negative (GSN), which is a drug pairs that do not interact. The selection of GSN in the previous which studies were only selected randomly from a list of drug pairs that do not interact. The random selection is feared to contain drug pairs that actually interact but have not been recorded. Therefore, in this study the determination of GSN was carried out by, first, grouping drug pairs included in the GSP using the DP-Clus algorithm with certain values of density and cluster properties. Then the drugs in different group would be paired and only the drug pairs in the GSN list are selected. It was found that our new proposed classification method increases the AUC value compared to the results obtained by random selection of GSN.</p> Hilma Mutiara Winata Farit Mochamad Afendi Anwar Fitrianto ##submission.copyrightStatement## 2019-10-31 2019-10-31 3 3 247 259 10.29244/ijsa.v3i3.327 HUBUNGAN AKREDITASI DAN UJIAN NASIONAL PADA SEKOLAH NEGERI DENGAN GENERALIZED STRUCTURED COMPONENT ANALYSIS <p>There are several views and tendencies that distinguish between schools and madrasas in several aspects, one of them is the curriculum. Madrasah as islamic educational institution contains more religious lessons compared to public schools. As a result, madrasah are considered less able to provide good result in educational achievement. Overall, the education system which is based on National Education Standards (SNP) is used for assessing the educational accreditation. SNP is the minimum criterion of education system in Indonesia can be evaluated from the National Examination (UN). As latent variable, SNP is measured through 124 items as variable indicators. One of methods which is used to measure the relationship among latent variables, and latent variables with their indicator variables is structural equation modeling (SEM). A component-based SEM is called Generalized Structured Component Analysis (GSCA). GSCA analysis based on measurement model, there were 9 indicators were not significant, in which 1 indicator of standard of education and staff (SPT), 5 indicators on standard of infrastructure (SSP), and 3 indicators on standard of cost (SB). Evaluation of the structural model, it was found that the path coefficient of standard of content (SI) to UN was not significant and standard of competency (SKL) given the biggest direct effect to UN. The overall goodness of fit model showed that the total variance that can be explained of all indicators and latent variables in evaluating model of accreditation and national examinations was 63.9%. The difference in the percentage of accreditation status between schools and madrasas shows different UN results. In the 2017-2018 period, MTsN had a higher percentage of accredited schools, in line with that the average MTsN UN obtained was better than that of SMP in all types of subjects.</p> Rezi Wahyuni Budi Susetyo Anwar Fitrianto ##submission.copyrightStatement## 2019-10-31 2019-10-31 3 3 260 271 10.29244/ijsa.v3i3.342 DAMPAK REDENOMINASI TERHADAP INFLASI INDONESIA: PENANGANAN MISSING MENGGUNAKAN METODE CASE DELETION, PMM, RF DAN BAYESIAN <p>Indonesia is the country with the third largest currency digit after Vietnam and Zimbabwe. In 2010, Indonesia conveyed a discourse on the application of rupiah redenomination, but in its implementation it was necessary to estimate the economic factors that would be affected, especially inflation, where inflation was one of the decisive indicators of the success of the redenomination policy of the currency. To estimate the impact of redenomination on inflation, Indonesia can reflect on the historical data of countries that have implemented the policy. Based on historical data, a model can be applied to Indonesia. Historical data includes macroeconomic variables and forms of government. To get a model with better precision, complete data needs to be considered. The historical missing will make the inferencing obtained invalid and important information that can be used for analysis also diminishes. The case deletion method, mean matching predictive, random forest, and bayesian linear regression can be used to handle it. The results showed that there were 38.18% missing data from total observations and the case deletion method as the best method. Then the condition of hyperinflation, economic growth, and the index of government forms significantly impacted inflation after the implementation of redenomination. So, if Indonesia applies redenomination between the period 2010-2017, with the classification accuracy of 64.71%, it is estimated that it will have a negative impact because the inflation will increase after redenomination is implemented.</p> Windri Wucika Bemi Rani Nooraeni ##submission.copyrightStatement## 2019-10-31 2019-10-31 3 3 272 286 10.29244/ijsa.v3i3.360 PEMODELAN AUTOREGRESIF SPASIAL MENGGUNAKAN BAYESIAN MODEL AVERAGING UNTUK DATA PDRB JAWA <p>Economic data always contains spatial effects. Gross Regional Domestic Product (GRDP) in Java is one of economic data that describes spatial dependence between adjacent districts/cities. The method that is suitable for modeling GDRP is spatial regression with spatial dependence on lags that is spatial autoregressive. GDRP prediction used the Bayesian Model Averaging (BMA) method. The ten autoregressive spatial model that have highest posterior probability was chosen to determined the BMA model by posterior probability. The explanatory variables used in this study were (1) mean years of schooling (2) life expectancy (3) income per capita (4) local revenue (5) number of workers (6) district minimum salary. The results showed that the number of workers was chosen as a predictor for the ten models. The model that have highest posterior probability probability is 0.54 which contains five explanatory variables that are mean years of schooling, income per capita, local revenue, number of workers and district minimum salary and the pseudo R2 of the model is 0.696.</p> Sarimah Sarimah Anik Djuraidah Aji H Wigena ##submission.copyrightStatement## 2019-10-31 2019-10-31 3 3 287 294 10.29244/ijsa.v3i3.376 PEMODELAN STATISTICAL DOWNSCALING DENGAN PEUBAH DUMMY BERDASARKAN TEKNIK CLUSTER HIERARKI DAN NON- HIERARKI UNTUK PENDUGAAN CURAH HUJAN <p>Statistical downscaling (SD) is a statistical technique used to predict local scale rainfall based on global atmospheric circulation. The global scale climate variable used is precipitation from GCM (Global Circulation Model). However, the precipitation data of GCM outputs have a large dimension, giving rise to multicollinearity in the data. This problem is handled by the Principal Component Regression (PCR) method. In addition, the SD models have heterogeneous error variances. The dummy variable is added to the PCR models to solve the problem. Hierarchical (k-means) and non-hierarchical cluster techniques (average linkage, median linkage, and ward linkage) are used in modeling to determine rainfall data groups. Furthermore, the group formed is the basis of the formation of dummy variables. This study aims to estimate local rainfall data in Pangkep district as a salt-producing area in South Sulawesi. There are 4 dummy variables based on the 5 groups formed. Dummy variables are able to improve predictions from the PCR models. R2 values of the PCR-dummy models (ranging from 89.89% to 95.58%) are relatively higher than the PCR models (ranging from 55.87% to 57.61%). This result is also consistent with the model validation stage. The PCR-dummy models based on non-hierarchical cluster techniques (k-means) are better than the PCR-dummy models based on cluster hierarchy techniques. In general, the best model is the PCR-dummy model of the non-hierarchical cluster technique (k-means ) and involves 4 main components.</p> Sitti Sahriman Anisa Kalondeng Vieri Koerniawan ##submission.copyrightStatement## 2019-10-31 2019-10-31 3 3 295 309 10.29244/ijsa.v3i3.471 HAZARD RATES AND RESTRICTED MEAN SURVIVAL TIME <p>Restricted Mean Survival Time (RMST) is well-established, but underutilized measure that can be interpreted as the average event-free survival time up to a pre-specified time point. In the last decade RMST received substantial attention and was advocated as an alternative for the Hazard Rate when the proportionality assumption is not met. Currently studies with time-to-evet outcomes routinely report survival curves and hazard rates. Research planning assumes extraction of comparative effect measures and variances that facilitates sample size calculations. Here we assessed the possibility of extracting clinically meaningful effect size estimates for RMST based research plans from studies that report survival curves and hazard rates. This assessment was based on simulations using Exponential and Weibull distributions. The simulations suggest that under certain conditions meaningful RMST effect size estimates can be extrapolated form published hazard rates. However, in cases when the proportionality assumption is in doubt (i.e. when RMST have most utility) extraction of meaningful estimates is not feasible.</p> Szilard Nemes ##submission.copyrightStatement## 2019-10-31 2019-10-31 3 3 310 319 10.29244/ijsa.v3i3.520 THE BEST GLOBAL AND LOCAL VARIABLES OF THE MIXED GEOGRAPHICALLY AND TEMPORALLY WEIGHTED REGRESSION MODEL <p>Geographically and temporally weighted regression (GTWR) is a method used when there is spatial and temporal diversity in an observation. GTWR model just consider the local influences of spatial-temporal independent variables on dependent variable. In some cases, the model not only about local influences but there are the global influences of spatial-temporal variables too, so that mixed geographically and temporally weighted regression (MGTWR) model more suitable to use. This study aimed to determine the best global and local variables in MGTWR and to determine the model to be used in North Sumatra’s poverty cases in 2010 to 2015. The result show that the Unemployment rate and labor force participation rates are global variables. Whereas the variable literacy rate, school enrollment rates and households buying rice for poor (raskin) are local variables. Furthermore, Based on Root Mean Square Error (RMSE) and Akaike Information Criterion (AIC) showed that MGTWR better than GTWR when it used in North Sumatra’s poverty cases.</p> Nuramaliyah Nuramaliyah Asep Saefuddin Muhammad Nur Aidi ##submission.copyrightStatement## 2019-10-31 2019-10-31 3 3 320 330 10.29244/ijsa.v3i3.564 KLASIFIKASI PENYAKIT PNEUMONIA MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK DENGAN OPTIMASI ADAPTIVE MOMENTUM <p>Pneumonia is an infection of the bacterium Streptococcus pneumoniae which causes inflammation in the air bag in one or both lungs. Pneumonia is a disease that can spread through the patient's air splashes. Pneumonia can be dangerous because it can cause death, therefore it is necessary to have early detection using chest radiograph images to determine the symptoms of pneumonia. Diagnosis using a chest radiograph image manually by medical personnel or a doctor requires a long time, even difficult to detect pneumonia disase. Convolutional neural network (CNN) is a deep learning method that adopts the performance of human brain neurons called neural network and convolution functions to classify images. CNN can also help classify pneumonia based on chest radiograph images. This study used data from Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images for Classification as many as 5860 images entered into two classes, namely normal and pneumonia, then 2400 data samples were taken using simple random sampling. This study uses adaptive momentum optimization (Adam) which serves to improve the accuracy of the model. Adam optimization is a development of existing optimizations such as Stochastic gradient descent (SGD), AdaGard, and RMSProp. The classification results of the models built were 99.98% for training data with 100 epochs, and accuracy in the test data was 78% which means that the model was able to qualify 78% of the test data into normal classes and pneumonia appropriately.</p> Lingga Aji Andika Hasih Pratiwi Sri Sulistijowati Handajani ##submission.copyrightStatement## 2019-10-31 2019-10-31 3 3 331 340 10.29244/ijsa.v3i3.560 PEMODELAN PENGARUH IKLIM TERHADAP ANGKA KEJADIAN DEMAM BERDARAH DI KOTA AMBON MENGGUNAKAN METODE REGRESI GENERALIZED POISSON <p>Dengue Hemorrhagic Fever (DHF) is one of the dreaded diseases of the transition season. DHF is a disease found in tropical and subtropical regions that caused by Dengue virus which is transmitted through Aedes mosquitoes. According to the World Health Organization (WHO) data, it is stated that Indonesia is the country with the highest dengue fever case in Southeast Asia. The incidence of dengue fever in Indonesia tends to increase in the middle of the rainy season, and one of the regions in Indonesia with the high level of rainfall intensity is Ambon City. DHF cases in Ambon city increase from year to year due to the last five years the intensity of rainfall is very high. Therefore, this study aims to identify climate factors that affect the incidence of DHF in Ambon City by using Generalized Poisson Regression method. Generalized Poisson Regression is appropriately considered to analyze the causing factors DHF incidence because the rating case of DHF is usually the count data that following the Poisson distribution. The results showed that the smallest AIC value for the Generalized Poisson Regression model was 75.842 with significant variables is DHF in the city of Ambon were one month earlier, air humidity, rainfall, and air humidity two months earlier.</p> Ferry Kondo Lembang Eysye Alchi Nara Francis Yunito Rumlawang Mozart Winston Talakua ##submission.copyrightStatement## 2019-10-31 2019-10-31 3 3 341 351 10.29244/ijsa.v3i3.474