Indonesian Journal of Applied Statistics最新文献_第6页

Analisis Ketahanan Hidup Pasien Kanker Paru Menggunakan Regresi Weibull 用Weibull回归分析肺癌患者的生存能力

Indonesian Journal of Applied Statistics Pub Date : 2019-03-13 DOI: 10.13057/IJAS.V1I2.25276

A. Solehah, Mohamat Fatekurohman

引用次数: 3

Peramalan Banyaknya Pengunjung Pantai Glagah Menggunakan Metode Autoregressive Integrated Moving Average Exogenous (ARIMAX) dengan Efek Variasi Kalender

Indonesian Journal of Applied Statistics Pub Date : 2019-03-13 DOI: 10.13057/IJAS.V1I2.26298

Solikhah Novita Intan, E. Zukhronah, Supriyadi Wibowo

引用次数: 0

Back Matter 回到问题

Indonesian Journal of Applied Statistics Pub Date : 2019-03-13 DOI: 10.13057/ijas.v1i2.28570

H. Pratiwi

引用次数: 0

Pendekatan Regresi Data Panel untuk Pemodelan Jumlah Angkatan Kerja dan Penanaman Modal Luar Negeri terhadap PDRB Provinsi di Indonesia 用于为印度尼西亚的PDRB省建模、投资和外国投资的数据面板回归方法

Indonesian Journal of Applied Statistics Pub Date : 2019-03-13 DOI: 10.13057/IJAS.V1I2.26172

M. Syukron, Hafidz Muhammad Fahri

{"title":"Pendekatan Regresi Data Panel untuk Pemodelan Jumlah Angkatan Kerja dan Penanaman Modal Luar Negeri terhadap PDRB Provinsi di Indonesia","authors":"M. Syukron, Hafidz Muhammad Fahri","doi":"10.13057/IJAS.V1I2.26172","DOIUrl":"https://doi.org/10.13057/IJAS.V1I2.26172","url":null,"abstract":"Indonesia is a country with great economic potency. Indonesia has a vast area and abundant natural products, but until now Indonesia is still a developing country. The Indonesian economy is defeated by other countries such as Japan, China and South Korea even by the neighboring country, Singapore. Increasing the national economy can be started from improving the regional economy which can be measured by gross regional domestic product (GRDP). Indonesia will experience a demographic bonus in 2045 so that the population of productive age is expected to contribute a lot to economic growth. The large number of productive age population must be balanced with the availability of jobs so that this momentum can be fully utilized. Foreign investment can be a solution when domestic capital is insufficient in financing economic activities. In addressing this phenomenon, a statistical analysis of panel data regression was conducted to see the relationship between independent variables, namely the number of labor force and realization of foreign investment, and a dependent variable, namely GRDP at constant prices in 2010 for every province in Indonesia. We use time series data in 2015-2017 and cross-sectional data of 34 provinces in Indonesia taken from BPS official website. The estimation result shows that both independent variables partially and fully have a significant effect on the GRDP with an adjusted R2 of 99.86%. Keywords: Labor force; regression; panel data; foreign capital; GRDP.","PeriodicalId":112023,"journal":{"name":"Indonesian Journal of Applied Statistics","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133140237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Analisis Premi Asuransi Jiwa Menggunakan Model Cox Proportional Hazard

Indonesian Journal of Applied Statistics Pub Date : 2019-03-13 DOI: 10.13057/IJAS.V1I2.25280

Firda Anisa Fajarini, Mohamat Fatekurohman

{"title":"Analisis Premi Asuransi Jiwa Menggunakan Model Cox Proportional Hazard","authors":"Firda Anisa Fajarini, Mohamat Fatekurohman","doi":"10.13057/IJAS.V1I2.25280","DOIUrl":"https://doi.org/10.13057/IJAS.V1I2.25280","url":null,"abstract":"Cox proportional hazard model is a regression model that is used to see the factors that cause an event. The survival analysis used in this research is the period of time the client is able to pay the life insurance premium using Cox proportional hazard model with Breslow method.The purpose of this research is to know how sex, age, insured money, job, method of payment of premium, premium, and type of product can influence the level of ability of client to make payment of life insurance premium based on customer data from PT. BRI Life Insurance Branch of Jember in 2007.The result of this research is the final model of Cox proportional hazard obtained from several variables which have significant influence with simultaneous and partial significance test is the variable of insured money (X3), variable of payment method of premium (X5), premium variable (X6) , and insurance product variable (X7) . The four variables are said to have a significant effect on the model, so that the final model of Cox proportional hazard is obtained that consists of the parameter estimation (β) value of each variable Keywords : survival analysis; cox proportional hazard model; breslow method; life insurance.","PeriodicalId":112023,"journal":{"name":"Indonesian Journal of Applied Statistics","volume":"601 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123179601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Penerapan Generalized Cross Validation dalam Model Regresi Smoothing Spline pada Produksi Ubi Jalar di Jawa Tengah

Indonesian Journal of Applied Statistics Pub Date : 2019-03-13 DOI: 10.13057/IJAS.V1I2.26250

T. D. Wahyuningsih, S. Handajani, Diari Indriati

{"title":"Penerapan Generalized Cross Validation dalam Model Regresi Smoothing Spline pada Produksi Ubi Jalar di Jawa Tengah","authors":"T. D. Wahyuningsih, S. Handajani, Diari Indriati","doi":"10.13057/IJAS.V1I2.26250","DOIUrl":"https://doi.org/10.13057/IJAS.V1I2.26250","url":null,"abstract":"Sweet Potato is a useful plant as a source carbohydrates, proteins, and is used as an animal feed and ingredient industry. Based on data from the Badan Pusat Statistik (BPS), the production fluctuations of the sweet potato in Central Java from year to year are caused by many factor. The production of sweet potato and the factors that affected it if they are described into a pattern of relationships then they do not have a specific pattern and do not follow a particular distribution, such as harvest area, the allocation of subsidized urea fertilizer, and the allocation of subsidized organic fertilizer. Therefore, the production model of sweet potato could be applied into nonparametric regression model. The approach used for nonparametric regression in this study is smoothing spline regression. The method used in regression smoothing spline is generalized cross validation (GCV). The value of the smoothing parameter (λ) is chosen from the minimum GCV value. The results of the study show that the optimum λ value for the factors of harvest area, urea fertilizer and organic fertilizer are 5.57905e-14, 2.51426e-06, and 3.227217e-13 that they result a minimum GCV i.e 2.29272e-21, 1.38391e-16, and 3.46813e-24. Keywords: Sweet potato; nonparametric; smoothing spline; generalized cross validation.","PeriodicalId":112023,"journal":{"name":"Indonesian Journal of Applied Statistics","volume":"206 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132284730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Pengeluaran Pariwisata dan Karakteristik Sosial Demografi Rumah Tangga di Provinsi Jawa Tengah 旅游支出和家庭社会人口学特征爪哇省中部

Indonesian Journal of Applied Statistics Pub Date : 2018-09-20 DOI: 10.13057/IJAS.V1I1.24120

S. Subanti, A. Hakim

引用次数: 0

Penerapan Model Geographically Weighted Regression(GWR) Pada Produksi Ubi Jalar 地理加权回归模型（GWR）在乌比贾拉尔产品中的应用

Indonesian Journal of Applied Statistics Pub Date : 2018-09-20 DOI: 10.13057/IJAS.V1I1.24114

Y. Susanti

{"title":"Penerapan Model Geographically Weighted Regression(GWR) Pada Produksi Ubi Jalar","authors":"Y. Susanti","doi":"10.13057/IJAS.V1I1.24114","DOIUrl":"https://doi.org/10.13057/IJAS.V1I1.24114","url":null,"abstract":"Sweet potatoes are a major source of carbohydrate, after rice, corn, and cassava. Sweet potato is consumed as an additional or side meal, except in Irian Jaya and Maluku, sweet potato is used as staple food. The main problem faced in increasing sweet potato production is still relies on certain areas, namely Java Island, as the main producer of sweet potato. Differences in production is what often causes the needs of sweet potato in various regions can not be fulfilled and there is a difference price of sweet potato. To fulfill the needs of sweet potato in Java, mapping areas of sweet potato production need to be made so that areas with potential for producing sweet potato can be developed while areas with insufficient quantities of sweet potato production may be given special attention. Due to differences in production in some areas of Java which depend on soil conditions, altitude, rainfall and temperatures, a model of sweet potato production will be developed using the GWR model. Based on the Geographically weighted regression model for each regencies / cities in Java Island, it can be concluded that the largest sweet potato production coming from Kuningan with R2 equal 99.86%.Keywords : Geographically weighted regression, model, sweet potato","PeriodicalId":112023,"journal":{"name":"Indonesian Journal of Applied Statistics","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134102899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bootstrap Residual Ensemble Methods for Estimation of Standard Error of Parameter Logistic Regression To Hypercolesterolemia Patient Data In Health Laboratory Yogyakarta 日惹卫生实验室高胆固醇血症患者资料参数逻辑回归标准误差估计的自举残差集成方法

Indonesian Journal of Applied Statistics Pub Date : 2018-09-19 DOI: 10.13057/ijas.v1i1.24086

S. W. F. Grace, S. Handajani, T. S. Martini

{"title":"Bootstrap Residual Ensemble Methods for Estimation of Standard Error of Parameter Logistic Regression To Hypercolesterolemia Patient Data In Health Laboratory Yogyakarta","authors":"S. W. F. Grace, S. Handajani, T. S. Martini","doi":"10.13057/ijas.v1i1.24086","DOIUrl":"https://doi.org/10.13057/ijas.v1i1.24086","url":null,"abstract":"Logistic regression is one of regression analysis to determine the relationship between response variable that have two possible values and some predictor variables. The method used to estimate logistic regression parameters is the maximum likelihood estimation (MLE) method. This method will produce a good estimate of the parameters if the estimation results have a small standard error.In a research, the characteristics of good data must be representative of the population. If the samples taken in small size they will cause a large standard error value. Bootstrap is a resampling method that can be used to obtain a good estimate based on small data samples. Small data will be resampling so it can represent the population to obtain minimum standard error. Previous studies have discussed resampling bootstrap on residuals as much as b times. In this research we will be analyzed resampling bootstrap on the error added to the dependent variable and take the average parameter estimation ensemble logistic regression model resampling result. Next we calculate the standard value error logistic regression parameters bootstrap results.This method is applied to the hypercholesterolemic patient status data in Health Laboratory Yogyakarta and after bootstrapping, the standard error produced is smaller than before the bootstrap resampling.Keywords : logistic regression, standard error, bootstrap resampling, parameter estimation ensemble","PeriodicalId":112023,"journal":{"name":"Indonesian Journal of Applied Statistics","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115937739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Robust Regression by Using Huber Estimator and Tukey Bisquare Estimator for Predicting Availability of Corn in Karanganyar Regency, Indonesia 利用Huber估计量和Tukey bissquared估计量的稳健回归预测印尼Karanganyar县玉米可得性

Indonesian Journal of Applied Statistics Pub Date : 2018-09-19 DOI: 10.13057/IJAS.V1I1.24090

H. Pratiwi, Y. Susanti, S. Handajani

引用次数: 1