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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) (formerly named Forum Statistika dan Komputasi), established since 2017, 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. All papers were reviewed by peer reviewers consisting of experts and academicians across universities and agencies
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 192 Documents
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DAMPAK REDENOMINASI TERHADAP INFLASI INDONESIA: PENANGANAN MISSING MENGGUNAKAN METODE CASE DELETION, PMM, RF DAN BAYESIAN Windri Wucika Bemi; Rani Nooraeni
Indonesian Journal of Statistics and Applications Vol 3 No 3 (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.v3i3.360

Abstract

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.
PEMODELAN AUTOREGRESIF SPASIAL MENGGUNAKAN BAYESIAN MODEL AVERAGING UNTUK DATA PDRB JAWA Sarimah Sarimah; Anik Djuraidah; Aji H Wigena
Indonesian Journal of Statistics and Applications Vol 3 No 3 (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.v3i3.376

Abstract

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.
PEMODELAN STATISTICAL DOWNSCALING DENGAN PEUBAH DUMMY BERDASARKAN TEKNIK CLUSTER HIERARKI DAN NON- HIERARKI UNTUK PENDUGAAN CURAH HUJAN Sitti Sahriman; Anisa Kalondeng; Vieri Koerniawan
Indonesian Journal of Statistics and Applications Vol 3 No 3 (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.v3i3.471

Abstract

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.
PEMODELAN PENGARUH IKLIM TERHADAP ANGKA KEJADIAN DEMAM BERDARAH DI KOTA AMBON MENGGUNAKAN METODE REGRESI GENERALIZED POISSON Ferry Kondo Lembang; Eysye Alchi Nara; Francis Yunito Rumlawang; Mozart Winston Talakua
Indonesian Journal of Statistics and Applications Vol 3 No 3 (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.v3i3.474

Abstract

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.
KAJIAN REGRESI KEKAR MENGGUNAKAN METODE PENDUGA-MM DAN KUADRAT MEDIAN TERKECIL Khusnul Khotimah; Kusman Sadik; Akbar Rizki
Indonesian Journal of Statistics and Applications Vol 4 No 1 (2020)
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.v4i1.502

Abstract

Regression is a statistical method that is used to obtain a pattern of relations between two or more variables presented in the regression line equation. This line equation is derived from estimation using ordinary least squares (OLS). However, OLS has limitations that are highly dependent on outliers data. One solution to the outliers problem in regression analysis is to use the robust regression method. This study used the least median squares (LMS) and multi-stage method (MM) robust regression for analysis of data containing outliers. Data analysis was carried out on generation data simulation and actual data. The simulation results of regression analysis in various scenarios are concluded that the LMS and MM methods have better performance compared to the OLS on data containing outliers. MM method has the lowest average parameter estimation bias, followed by the LMS, then OLS. The LMS has the smallest average root mean squares error (RMSE) and the highest average R2 is followed by the MM then the OLS. The results of the regression analysis comparison of the three methods on Indonesian rice production data in 2017 which contains 10% outliers were concluded that the LMS is the best method. The LMS produces the smallest RMSE of 4.44 and the highest R2 that is 98%. MM's method is in the second-best position with RMSE of 6.78 and R2 of 96%. OLS method produces the largest RMSE and lowest R2 that is 23.15 and 58% respectively.
IMPLEMENTASI TRANSFORMASI FOURIER UNTUK TRANSFORMASI DOMAIN WAKTU KE DOMAIN FREKUENSI PADA LUARAN PURWARUPA ALAT PENDETEKSIAN GULA DARAH SECARA NON-INVASIF Umam Hidayaturrohman; Erfiani Erfiani; Farit M Afendi
Indonesian Journal of Statistics and Applications Vol 4 No 2 (2020)
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.v4i2.504

Abstract

Diabetes mellitus is the result of changes in the body caused by a decrease of insulin performance which is characterized by an increase of blood sugar level. Detection of blood sugar can be done with Invasive methods or non-invasive methods. However, non-invasive methods are considered better because they can check early, faster and accurate. The prototype output is values of intensity in the time domain, thus fourier transformation is very much needed to transform into the frequency domain. In this study, Fourier transformation methods used are Discrete Fourier Transform (DFT), Fast Fourier Transform Radix-2, and Fast Fourier Transform Radix-4. Evaluation for the best method is done by comparing the processing speed of each method. The FFT Radix-4 method is more effective to perform the transformation into the frequency domain. The average processing speed with the FFT Radix-4 method reaches 2.67×105 nanoseconds, and this is much faster 5.06×106 nanoseconds than the FFT Radix-2 method and 2.40×107 nanoseconds faster than the DFT method.
PENERAPAN ANALISIS LASSO DAN GROUP LASSO DALAM MENGIDENTIFIKASI FAKTOR-FAKTOR YANG BERHUBUNGAN DENGAN TUBERKULOSIS DI JAWA BARAT Stephan Chen; Khairil Anwar Notodiputro; Septian Rahardiantoro
Indonesian Journal of Statistics and Applications Vol 4 No 1 (2020)
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.v4i1.510

Abstract

Tuberculosis is the deadliest infectious disease in Indonesia, and West Java is a province with the largest number of tuberculosis cases in Indonesia. This research was conducted to identify variables and groups of variables that could explain the number of tuberculosis cases in West Java. The data used has many explanatory variables, and these variables form groups. LASSO and group LASSO analysis can be used for variables selection and handle data that has many explanatory variables, and group LASSO analysis can be used on data with grouped variables. The results of the LASSO analysis, variables that can explain the number of tuberculosis cases in West Java are the number of people with disabilities, the number of pharmacy staff, the number of malnourished people, the number of people working and the number of cities. According to the group LASSO analysis, the variables that can explain the number of tuberculosis cases in West Java are variables in the health and environmental groups. The government can focus on these factors if they want to reduce the number of tuberculosis cases in West Java.
A REPEATED CROSS-SECTIONAL MODEL FOR ANALYZING UNEMPLOYMENT DATA IN BOGOR Ulfah Sulistyowati; Khairil Anwar Notodiputro; I Made Sumertajaya
Indonesian Journal of Statistics and Applications Vol 4 No 2 (2020)
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.v4i2.513

Abstract

In general, the form of data encountered in statistical problems is panel data and cross-sectional data. There are times in certain conditions, the data formed in the form of a combination of panel data with cross-sectional data, which is commonly referred to as repeated cross-sectional data. Repeated cross-sectional data is often done in research with individual observations. In this study, a repeated cross-sectional analysis was carried out using a fixed influence model with observations in the form of an area (village) in Bogor, West Java to analyze unemployment factors. The results obtained are that ongoing village development affects the unemployment rate in Bogor
HAZARD RATES AND RESTRICTED MEAN SURVIVAL TIME Szilard Nemes
Indonesian Journal of Statistics and Applications Vol 3 No 3 (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.v3i3.520

Abstract

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.
ANALISIS KURVA ROC PADA MODEL LOGIT DALAM PEMODELAN DETERMINAN LANSIA BEKERJA DI KAWASAN TIMUR INDONESIA Muhammad Rizqi Fachrian Nur; Siskarossa Ika Oktora
Indonesian Journal of Statistics and Applications Vol 4 No 1 (2020)
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.v4i1.524

Abstract

Binary logistic regression is used for probability modeling or to predict binary response variables (Success / Failure) from one or more explanatory variables that are continuous or categorical. In carrying out this analysis, there are several ways to test the suitability of the resulting model, and one of them is the area under the ROC curve. The application of the analysis method in this study is the determinant of the elderly population to work. The population of the elderly in Indonesia is increasing every year. Many views that the elderly depend on other residents, especially in terms of the economy. However, if seen from the percentage of elderly working in Indonesia, it is increasing, including the elderly in KTI. The purpose of this study is to determine the characteristics of the elderly in KTI, know the factors that influence the decision of the elderly population to work in KTI and find out the tendency of variables that affect the decision of the elderly to work in KTI. The data used are raw data from Badan Pusat Statistik (BPS) was Survei Sosial Ekonomi Nasional (Susenas) Kor March 2018. This study using descriptive analysis methods and binary logistic regression. The results are that the variables that significantly influence the decisions of the elderly to work are residence, gender, age, education, family status, marital status, health complaints, and health insurance. Elderly who has characteristics residing in rural, male sex, classified as young elderly (60-69 years old), has the highest level of elementary school education, has the status of KRT in his family, is married, has no complaints health, and not having health insurance will have a greater tendency to decide to work.

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