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<![CDATA[Estimasi Model Regresi Kuantil Spline Kuadratik pada Data Trombosit dan Hematokrit Pasien DBD ]]>
<![CDATA[Bunga Aprilia]]>;
<![CDATA[Anna Islamiyati]]>;
<![CDATA[Anisa Anisa]]>;
<![CDATA[Nirwan Ilyas]]>
<![CDATA[ ESTIMASI: Journal of Statistics and Its Application Vol. 1, No. 2, Juli, 2020 : Estimasi ]]>
Publisher : <![CDATA[Hasanuddin University ]]>
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DOI: 10.20956/ejsa.v1i2.9264
<![CDATA[Nonparametric quantile regression is used to estimate the regression function when assumptions about the shape of the regression curve are unknown. It is only assumed to be subtle by involving quantile values. One estimator in nonparametric regression is spline. The segmented properties of the spline provide more flexibility than ordinary polynomials. Therefore, the nature of the spline makes it possible to adapt more effectively to the local characteristics of a function or data. This study proposes to get the results of the estimation platelet count model to the hematocrit value of DHF. The optimal model obtained from the estimation of quadratic spline quantile regression is at quantile 0.5 with one knot and the GCV value is 41.5. The results of the estimation show that there is a decrease in platelet counts as the percentage of hematocrit increase.]]>
<![CDATA[Pemodelan Regresi Nonparametrik Spline Poisson Pada Tingkat Kematian Bayi di Sulawesi Selatan ]]>
<![CDATA[Novilia Jao]]>;
<![CDATA[Anna Islamiyati]]>;
<![CDATA[Nurtiti Sunusi]]>
<![CDATA[ ESTIMASI: Journal of Statistics and Its Application Vol. 3, No. 1, Januari, 2022 : Estimasi ]]>
Publisher : <![CDATA[Hasanuddin University ]]>
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DOI: 10.20956/ejsa.vi.11997
<![CDATA[Poisson regression analysis is a method used to analyze the relationship between predictor variables and response variables with a Poisson distribution. However, not all data have an orderly pattern, so the Poisson regression is not appropriate to use. To solve this problem, a multivariable Poisson nonparametric regression with a spline truncated estimator was used. In this research, the estimation parameters of multivariable Poisson nonparametric regression was applied to data of infant mortality rates in South Sulawesi in 2017. The infant mortality rate can be measured from the number of infant deaths under one year. The method of selecting the optimal knot point uses the Generalized Cross Validation (GCV) method. The best model is formed on a linear spline model with one knot point. Based on the estimation of the parameters formed, it shows that the variable number of babies with low birth weight (x1) and the number of infants who are exclusively breastfed (x3) significantly affect the number of infant deaths. Keywords: GCV, Multivariable Nonparametric Regression, Poisson, Spline Truncated, Total Infant Mortality.]]>
<![CDATA[Analisis Perubahan Berat Badan Balita dengan Estimator Penalized Spline Kuadratik ]]>
<![CDATA[Muhammad Jayzul Usrah]]>;
<![CDATA[Anna Islamiyati]]>;
<![CDATA[Anisa]]>
<![CDATA[ ESTIMASI: Journal of Statistics and Its Application Vol. 3, No. 2, Juli, 2022 : Estimasi ]]>
Publisher : <![CDATA[Hasanuddin University ]]>
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DOI: 10.20956/ejsa.vi.11459
<![CDATA[Nonparametric regression is a regression approach that is used when one of the parametric assumptions are not fulfilled. One of the estimators in nonparametric regression is penalized spline. The growth pattern of toddler that varied each month of observation make the suitable regression approach is nonparametric penalized spline regression because of its high flexibility. This study aims to obtain an estimate of the growth model for toddler in South Sulawesi. The optimal model obtained with a minimum GCV value of 4.87E-05 using two point knots that is 14 and 56 with lamda 100. The estimation results show that there are 3 intervals of change patterns in the growth of toddler in South Sulawesi]]>
<![CDATA[Regresi Spline Polynomial Truncated Biprediktor untuk Identifikasi Perubahan Jumlah Trombosit Pasien Demam Berdarah Dengue ]]>
<![CDATA[Anna Islamiyati]]>
<![CDATA[ Al-Khwarizmi : Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam Vol 7, No 2 (2019): Al-Khwarizmi: Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam had Accredi ]]>
Publisher : <![CDATA[Prodi Pendidikan Matematika FTIK IAIN Palopo ]]>
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DOI: 10.24256/jpmipa.v7i2.799
<![CDATA[Abstract:This paper is a longitudinal study using a nonparametric regression model to identify changes in platelet count from dengue fever. Changes in platelet counts were analyzed based on treatment time and hematocrit count factors. The estimator method proposed is spline polynomial truncated bipredictor. Based on the results of the simultaneous model estimation, we obtained GCV = 714.72 and R2 = 95.9%, it means the model is feasible to explain and identify changes in platelet count based on the time of treatment and the number of hematocrit from DBD patients. Based on the data, there are four patterns of platelet change based on time of treatment and three patterns of platelet change based on hematocrit that are different from each other.Abstrak:Paper ini merupakan studi longitudinal dengan menggunakan model regresi nonparametrik untuk mengidentifikasi perubahan jumlah trombosit demam berdarah. Perubahan jumlah trombosit dianalisis berdasarkan faktor waktu perawatan dan jumlah hematokrit. Metode estimator yang diusulkan adalah spline polynomial truncated bi prediktor. Berdasarkan hasil taksiran model simultan diperoleh GCV = 714,72 dan R2 = 95,9%, artinya model layak untuk menjelaskan dan mengidentifikasi perubahan jumlah trombosit berdasarkan waktu perawatan dan jumlah hematokrit pasien DBD. Berdasarkan data, terdapat empat pola perubahan trombosit berdasarkan waktu perawatan dan tiga pola perubahan trombosit berdasarkan hematokrit yang berbeda satu sama lain.]]>
<![CDATA[Model Data Kepemilikan Asuransi Kesehatan di Indonesia Berdasarkan Status Pekerjaan Melalui Analisis Regresi Logistik Biner Dua Level ]]>
<![CDATA[Marsya Anggun Prisila]]>;
<![CDATA[Anna Islamiyati]]>;
<![CDATA[Andi Kresna Jaya]]>
<![CDATA[ Contemporary Mathematics and Applications (ConMathA) Vol. 4 No. 2 (2022) ]]>
Publisher : <![CDATA[Universitas Airlangga ]]>
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DOI: 10.20473/conmatha.v4i2.39354
<![CDATA[Regresi logistik biner dua level merupakan metode analisis regresi yang digunakan untuk menganalisis hubungan antara satu variabel respon yang berupa data kualitatif dikotomi dengan beberapa variabel prediktor, dari data yang berstruktur hirarki. Penelitian ini bertujuan untuk mendapatkan model data kepemilikan asuransi kesehatan di Indonesia berdasarkan status pekerjaan melalui analisis regresi logistik biner dua level. Metode yang digunakan adalah regresi logistik biner dua level dengan model random intercept menggunkan maximum likelihood estimation pada data kepemilikan asuransi kesehatan di Indonesia. Berdasarkan hasil taksiran model diperoleh bahwa status pekerjaan berpengaruh terhadap kepemilikan asuransi kesehatan di Indonesia dan 2.99 kali berpeluang memiliki asuransi kesehatan dibanding penduduk yang tidak memiliki pekerjaan.]]>
<![CDATA[Estimasi Model Regresi Spline Kubik Tersegmen dengan Metode Penalized Least Square ]]>
<![CDATA[Anna Islamiyati]]>;
<![CDATA[Anisa Anisa]]>;
<![CDATA[Raupong Raupong]]>;
<![CDATA[Jusmawati Massalesse]]>;
<![CDATA[Nasrah Sirajang]]>;
<![CDATA[Sitti Sahriman]]>;
<![CDATA[Alfiana Wahyuni]]>
<![CDATA[ Al-Khwarizmi : Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam Vol 10, No 2 (2022): Al-Khwarizmi : Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam had Accre ]]>
Publisher : <![CDATA[Prodi Pendidikan Matematika FTIK IAIN Palopo ]]>
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DOI: 10.24256/jpmipa.v10i2.3197
<![CDATA[Abstract:Nonparametric regression is used for data whose data pattern is non-parametric. One of the estimators that can be developed is a segmented cubic spline which is able to show several segmentation changes in the data. This article examines the estimation of segmented cubic spline nonparametric regression models using the Penalized Least Square estimation criteria. The method involves knot points and smoothing parameters simultaneously. In addition, the model is used to analyze data on BPJS claims based on patient age. The results show that the optimal model is at two-knot points, namely 26 and 52 with a smoothing parameter of 0.89. There are three segmentation changes from the cubic data, which consist of young people up to 26 years old, 26-52 years old, and 52 years and over. Abstrak:Regresi nonparametrik digunakan untuk data yang pola datanya bentuk non parametrik. Salah satu estimator yang dapat dikembangkan adalah spline kubik tersegmen yang mampu menunjukkan beberapa segmentasi perubahan pada data. Artikel ini mengkaji estimasi model regresi nonparametrik spline kubik tersegmen melalui kriteria estimasi menggunakan Penalized Least Square. Metode tersebut melibatkan titik knot dan parameter penghalus secara bersamaan. Selain itu, model digunakan untuk menganalisis data klaim BPJS berdasarkan usia pasien. Hasil menunjukkan bahwa model optimal pada dua titik knot yaitu 26 dan 52 dengan parameter penghalus sebesar 0,89. Terdapat tiga segmentasi perubahan data secara kubik, yaitu usia muda hingga 26 tahun, usia 26-52 tahun, dan usia 52 tahun ke atas. ]]>
<![CDATA[Model Regresi Kuantil Spline Orde Dua Dalam Menganalisis Perubahan Trombosit Pasien Demam Berdarah ]]>
<![CDATA[Anisa Anisa]]>;
<![CDATA[Anna Islamiyati]]>;
<![CDATA[Sitti Sahriman]]>;
<![CDATA[Jusmawati Massalesse]]>;
<![CDATA[Bunga Aprilia]]>
<![CDATA[ Jambura Journal of Mathematics Vol 5, No 1: February 2023 ]]>
Publisher : <![CDATA[Department of Mathematics, Universitas Negeri Gorontalo ]]>
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DOI: 10.34312/jjom.v5i1.16086
<![CDATA[Quantile regression can be used to analyze data containing outliers including DHF data. The spline is able to identify several patterns of change in the regression model, so this study uses a second-order quantile spline regression model in analyzing DHF data that occurred in Makassar City. In this article, the authors analyze the pattern of changes that occur in platelets based on changes in the hematocrit content of DHF patients. The selected quantiles are quartiles 0.25; 0.50; and 0.75 with 3-knot points. Based on the results of the analysis, the minimum GCV value obtained at the use of knot points is 30.30; 44.80; 47.10 for the 0.25 quartile; 0.50; and 0.75. This shows that in each quartile, there are four patterns of quadratic changes that occur in the platelet count of DHF patients. The parabolic curve formed in each pattern segmentation shows that there are times when platelets are increasing and there are times when platelets are decreasing. However, the average platelets decreased drastically, especially when the hematocrit reached 47.10%.]]>
<![CDATA[Pemodelan Regresi Logistik Ordinal dengan Dispersi Efek Lokasi ]]>
<![CDATA[Ainun Utari Budistiharah]]>;
<![CDATA[Anna Islamiyati]]>;
<![CDATA[Sri Astuti Thamrin]]>
<![CDATA[ ESTIMASI: Journal of Statistics and Its Application Vol. 4, No. 2, Juli, 2023 : Estimasi ]]>
Publisher : <![CDATA[Hasanuddin University ]]>
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DOI: 10.20956/ejsa.v4i2.12355
<![CDATA[Logistic regression ordinal is a regression model that can explain the relationship between predictor variables in the form of categorical data or continuous data with response variable is more than two categories with a scale of measurement that is level or sequence. In ordinal logistic regression, the frequency of occurrence in each response category is often very different, so it will affect the model's accuracy. Therefore, this study will model ordinal logistic regression with a dispersion of location effects, then applied to the nutritional status data of toddler in 2019 at the Pekkae Puskesmas, Barru Regency. The results obtained show that the ordinal logistic regression model with the dispersion of location effects is better than the usual ordinal logistic regression model for predicting the nutritional status data for toddlers in 2019 at Pekkae Puskesmas, Barru Regency based on deviance values. The factors that influence the nutritional status of toddler based on TB/U are gender, age, and height.]]>
<![CDATA[Pendugaan Koefisien Regresi Logistik Biner Menggunakan Algoritma Least Angle Regression ]]>
<![CDATA[Utami, Mamik]]>;
<![CDATA[Islamiyati, Anna]]>;
<![CDATA[Thamrin, Sri Astuti]]>
<![CDATA[ ESTIMASI: Journal of Statistics and Its Application Vol. 5, No. 1, Januari, 2024 : Estimasi ]]>
Publisher : <![CDATA[Hasanuddin University ]]>
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DOI: 10.20956/ejsa.v5i1.12489
<![CDATA[Binary logistic regression is a statistical analysis method that aims to determine the relationship between variable which has two categories with the predictor variable that have categorical or continuous scale. The method that used to estimate logistic regression parameters is Maximum Likelihood Estimation (MLE) method. In estimating parameters, Least Angle Regression (LAR) algorithm is used to select the significant variables in order to get the best model from the estimation results of binary logistic regression coefficients. This LAR algorithm is applied to the risko of stunting data in two-year-old-babies at Buntu Batu Health Center working area, Enrekang Regency, South Sulawesi in 2019. This results obtained in the estimation of binary logistic regression prediction model using LAR algorithm, the standard error value is 0.018 smaller than the standard error value of binary logistic regression, which is 0.025. This shows that the binary logistic regression model using LAR algorithm is better than the usual binary logistic regression model on the risk of stunting data. Based on the results obtained, the variables that significantly affect the risk of stunting in two-year-old-babies on 2019 are father’s height, body length of birth, exclusive breastfeeding, history of infectious diseases, and history of immunization.]]>
<![CDATA[Estimasi Parameter Model Regresi Logistik Biner dengan Conditional Maximum Likelihood Estimation pada Data Panel ]]>
<![CDATA[Fitri, Fitri]]>;
<![CDATA[Islamiyati, Anna]]>;
<![CDATA[Kalondeng, Anisa]]>
<![CDATA[ ESTIMASI: Journal of Statistics and Its Application Vol. 5, No. 2, Juli, 2024 : Estimasi ]]>
Publisher : <![CDATA[Hasanuddin University ]]>
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DOI: 10.20956/ejsa.v5i2.13998
<![CDATA[Binary logistic regression models can be used on panel data with categorical responses that experience repeated measurements based on time. This study aims to determine the factors that influence the Human Development Index in South Sulawesi Province in 2015-2019. Data were analyzed through binary logistic regression with fixed effect model approach through Conditional Maximum Likelihood Estimation (CMLE) for panel data. The results of this study indicate that the variables that have a significant effect are life expectancy (X1), school length expectancy (X2) and the average length of schooling (X3). Obtained the probability value of districts/cities that have a medium low and medium high human development index with a classification accuracy of 56.25%.]]>
