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Indonesian Journal of Statistics and Its Applications

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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): diterbitkan berkala 2 (dua) kali dalam setahun yang memuat tulisan ilmiah yang berhubungan dengan bidang statistika dan aplikasinya. Artikel yang dimuat berupa hasil penelitian bidang statistika dan aplikasinya dengan topik (tapi tidak terbatas): rancangan dan analisis percobaan, metodologi survey dan analisis, riset operasi, data mining, pemodelan statistika, komputasi statistika, time series dan ekonometrika, serta pendidikan statistika.

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Articles 17 Documents

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Search results for , issue "Vol 4 No 1 (2020)" : 17 Documents clear

PEMODELAN GEOGRAPHICALLY WEIGHTED REGRESSION (GWR) PADA PERSENTASE KRIMINALITAS DI PROVINSI JAWA TIMUR TAHUN 2017 Dessy Wulandari Syahputri Yusuf; Elvira Mustikawati Putri Hermanto; Wara Pramesti
Indonesian Journal of Statistics and Applications Vol 4 No 1 (2020)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Crime is everything that exists in Indonesia. Based on BPS data in 2018, East Java Province ranks first in the Province of North Sumatra and the Special Capital Region of Jakarta. This research was conducted to determine the factors that support crime in each Regency / City of East Java Province. The method used in this research is Weighted Geographic Regression (GWR). Geographically Weighted Regression (GWR) is one of the statistical methods used to model variable responses with regional or area-based predictor variables. Based on the GWR results, it is recognized as a variable Population Density Percentage (X1), Open Unemployment Rate (X2), Poor Population (X3), Population who are Victims of Drug Abuse (X4), Human Development Index (X5), and Married Human Population (X6) ) importance in the city of Surabaya. The coefficient of determination (R2) and AIC from GWR is better than the OLS model. This refers to the optimal R2 and AIC values of 91.40% and 129.293.

PENGGUNAAN ANALISIS KLASTER K-MEANS DALAM PEMODELAN REGRESI SPASIAL PADA KASUS TUBERKULOSIS DI JAWA TIMUR TAHUN 2017 Hardani Prisma Rizky; Wara Pramesti; Gangga Anuraga
Indonesian Journal of Statistics and Applications Vol 4 No 1 (2020)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Tuberculosis (TB) is a contagious infectious disease caused by the bacterium Mycobacterium tuberculosis which can attack various organs, especially the lungs. TB if left untreated or incomplete treatment can cause dangerous complications to death. East Java Province has the second-highest TB case after West Java Province. Therefore we need statistical modeling to analyze the factors that influence TB in East Java Province. The data used in this study were sourced from data from BPS and East Java Provincial Health Offices in 38 districts/cities in East Java Province in 2017. Analysis of data using the OLS regression approach only looked at variable factors but was unable to know the effects of territory. So to overcome this, a spatial regression approach is used by comparing the weight of Queen Contiguity and the results of the k-means cluster analysis to obtain the best model. Based on the results of the analysis, the spatial aspects of the data have met the assumptions of spatial dependencies using the Moran's I test with a p-value of 0.000001295. The weighting matrix used is the k-means cluster weighting matrix k = 2. The test results obtained by the Spatial Autoregressive Moving Average (SARMA) model selected as the best model with the value of the deterrence coefficient (R2) and Akaike Info Criterion (AIC), 87.10% and 586.69. The factors that significantly influence the number of Tuberculosis patients in each district/city in East Java are population density (X2) and the number of healthy houses (X9).

ROBUST SPATIAL REGRESSION MODEL ON ORIGINAL LOCAL GOVERNMENT REVENUE IN JAVA 2017 Winda Chairani Mastuti; Anik Djuraidah; Erfiani Erfiani
Indonesian Journal of Statistics and Applications Vol 4 No 1 (2020)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Spatial regression measures the relationship between response and explanatory variables in the regression model considering spatial effects. Detecting and accommodating outliers is an important step in the regression analysis. Several methods can detect outliers in spatial regression. One of these methods is generating a score test statistics to identify outliers in the spatial autoregressive (SAR) model. This research applies a robust spatial autoregressive (RSAR) model with S- estimator to the Original Local Government Revenue (OLGR) data. The RSAR model with the 4-nearest neighbor weighting matrix is the best model produced in this study. The coefficient of the RSAR model gives a more relevant result. Median absolute deviation (MdAD) and median absolute percentage error (MdAPE) values in the RSAR model with 4-nearest neighbor give smaller results than the SAR model.

PENGEMBANGAN ANALISIS GEROMBOL BERHIRARKI DENGAN KETERGANTUNGAN SPASIAL PADA INDIKATOR MAKRO SOSIAL EKONOMI DI KABUPATEN/KOTA PROVINSI SULAWESI TENGAH Iman Setiawan; Nur’eni Nur’eni; Sritasarwati Putran
Indonesian Journal of Statistics and Applications Vol 4 No 1 (2020)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

This paper develops how the hierarchical clustering analysis uses multivariate variables with spatial dependence on macro social-economic indicator data in Regency/City Central Sulawesi Province. Macro social-economic indicator data used in this paper are the number of criminal cases, per capita expenditure, population density, and Human Development Index of Regency/City of Central Sulawesi Province in 2018. To answer this question, Macro social-economic indicator data was reduced to a new variable using principal component analysis. The new variable was used to identify spatial dependency using the Moran index test. Spatial weight, that meets the Moran index test on the alternative hypothesis (there is a spatial dependency between locations), was used as the spatial dependency distance. Cluster analysis using two distance including variable and spatial dependency distance. The results showed that neighboring Regency/City are in the same cluster (spatial dependency occasion). So that there are five clusters Regency/City in Central Sulawesi Province.

COMPARISON OF K-MEANS CLUSTERING METHOD AND K-MEDOIDS ON TWITTER DATA Cahyani Oktarina; Khairil Anwar Notodiputro; Indahwati Indahwati
Indonesian Journal of Statistics and Applications Vol 4 No 1 (2020)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

The presidential election is one of the political events that occur in Indonesia once in five years. Public satisfaction and dissatisfaction with political issues have led to an increase in the number of political opinion tweets. The purpose of this study is to examine the performance of the k-means and k-medoids method in the Twitter data and to tweet about the presidential election in 2019. The data used in this study are primary data taken from Muhyi's research, then mining the text against data obtained. Because this data has been processed by Muhyi to analyze the electability of the 2019 presidential candidate pairs, for this journal needs a preprocessing was carried out to analyze the tendency of tweets to side with the candidate pairs of one or two. The difference in the pre-processing of this research with previous research is that there is a cleaning of duplicate data and normalizing. The results of this study indicate that the optimal number of clusters resulting from the k-means method and the k-medoid method are different.

KAJIAN SIMULASI PERBANDINGAN METODE REGRESI KUADRAT TERKECIL PARSIAL, SUPPORT VECTOR MACHINE, DAN RANDOM FOREST Asep Andri Fauzi; Agus M. Soleh; Anik Djuraidah
Indonesian Journal of Statistics and Applications Vol 4 No 1 (2020)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Highly correlated predictors and nonlinear relationships between response and predictors potentially affected the performance of predictive modeling, especially when using the ordinary least square (OLS) method. The simple technique to solve this problem is by using another method such as Partial Least Square Regression (PLSR), Support Vector Regression with kernel Radial Basis Function (SVR-RBF), and Random Forest Regression (RFR). The purpose of this study is to compare OLS, PLSR, SVR-RBF, and RFR using simulation data. The methods were evaluated by the root mean square error prediction (RMSEP). The result showed that in the linear model, SVR-RBF and RFR have large RMSEP; OLS and PLSR are better than SVR-RBF and RFR, and PLSR provides much more stable prediction than OLS in case of highly correlated predictors and small sample size. In nonlinear data, RFR produced the smallest RMSEP when data contains high correlated predictors.

GROWTH EXTERNALITIES ON THE ENVIRONMENTAL QUALITY INDEX OF EAST JAVA INDONESIA, SPATIAL ECONOMETRICS MODEL OF STIRPAT Rahma Fitriani; Herman Cahyo Diartho; Septya Hadiningrum
Indonesian Journal of Statistics and Applications Vol 4 No 1 (2020)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

East Java has shown strong economic growth, which negatively affects its environmental quality. Analysis of the functional relationship between economic growth and environmental quality is important to direct the growth without further deteriorate the environmental quality in this area. It is assumed that growth produces some externalities on environmental quality. The spread of technological information, economic productivity, population growth or investment, can be the source of the growth externalities. The objective of this study is to test the significance of the involved growth externalities on East Java’s environmental quality. Using spatial data, the externalities are accommodated in a spatial version of the STIRPAT model. It is estimated using per city/regency 2015 data. The analysis indicates that local density, local agricultural productivity, neighboring density, and neighboring mining activity significantly affect the local environmental quality. The latter two are the main sources of the growth externalities.

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