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All Journal Media Statistika Statistika BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika Jurnal Varian Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Jurnal Abdi Insani ESTIMASI: Journal of Statistics and Its Application International Journal Of Science, Technology & Management (IJSTM) JAMBURA JOURNAL OF PROBABILITY AND STATISTICS Jurnal Siger Matematika Journal of Research Applications in Community Services Eksponensial STATISTIKA Journal of Education Technology and Information System
Sifriyani, Sifriyani
Statistics Study Program, Department Of Mathematics, Faculty Of Mathematics And Natural Sciences, Mulawarman University, Samarinda
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APPLICATION OF NONPARAMETRIC GEOGRAPHICALLY WEIGHTED REGRESSION METHOD ON OPEN UNEMPLOYMENT RATE DATA IN INDONESIA Saputri, Marisa Nanda; Sifriyani, Sifriyani; Wasono, Wasono
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 4 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss4pp2071-2080

Abstract

Nonparametric Geographically Weighted Regression (NGWR) model is a development of nonparametric regression with geographic weights for spatial data where parameter estimators are local to each observation location. NGWR is used to obtain the best model for the Open Unemployment Rate (OUR) data in Indonesia. Unemployment is still a significant social and economic problem in Indonesia. This study aims to obtain the NGWR model on the OUR data in Indonesia and to determine the factors that significantly affect OUR. The method used is the NGWR model with bisquare kernel function weighting and gaussian kernel function. The best model is obtained by NGWR with bisquare kernel function weighting at order 1 and knot point 1, with R2 is 83.45 percent which explains that the predictor variables affect the OUR by that number. The factors that have a significant effect on OUR are the percentage of population density, minimum wage, average years of schooling, GRDP, and the percentage of poor people.
MODELING OPEN UNEMPLOYMENT RATE IN KALIMANTAN ISLAND USING NONPARAMETRIC REGRESSION WITH FOURIER SERIES ESTIMATOR Rahmania, Rahmania; Sifriyani, Sifriyani; Dani, Andrea Tri Rian
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0245-0254

Abstract

Nonparametric regression is a regression approach that is used to determine the relationship between the response variable and the predictor variable if the shape of the regression curve is unknown. One of the popular estimators used in nonparametric regression is the Fourier series estimator. Fourier series nonparametric regression is generally used when the pattern of the investigated data is unknown and there is a tendency for the pattern to repeat. The purpose of this study is to estimate nonparametric regression using the Fourier series approach and to find out the factors that influence the open unemployment rate on the island of Borneo in 2021. The criteria for the goodness of the model used Generalized Cross Validation (GCV) and the coefficient of determination ( ). Based on the results, it was found that the best nonparametric regression model for the Fourier series was the model with 5 oscillations which indicated a minimum GCV of 10.47 and an of 74.22%. Furthermore, based on the results of parameter significance testing either simultaneously or partially, it shows that all predictor variables have a significant effect on the open unemployment rate. The predictor variables include the labor force participation rate, the average length of schooling, the percentage of poor people, economic growth rate, and total population.
ESTIMATION OF GEOGRAPHICALLY WEIGHTED PANEL REGRESSION MODEL WITH BISQUARE KERNEL WEIGHTING FUNCTION ON PERCENTAGE OF STUNTING TODDLERS IN INDONESIA Asnita, Asnita; Sifriyani, Sifriyani; Fauziyah, Meirinda
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0383-0394

Abstract

Stunting is a condition of failure to thrive in children under five years old due to chronic malnutrition. Efforts that can be made to reduce the incidence of stunting in Indonesia are to identify factors that are thought to affect the incidence of stunting in Indonesia. The analysis methods used in this study are the global Fixed Effect Model (FEM) and the local Geographically Weighted Panel Regression (GWPR) model. FEM is a global regression model that assumes that each individual's model has a different intercept value. While GWPR is a local regression model from FEM that considers aspects of geographic location, by repeating data at each observation location, different times, and using spatial data. The weighting function used in this study is fixed bisquare and adaptive bisquare. This study aims to obtain a GWPR model on the percentage of stunting toddlers in Indonesia in 2019 until 2022 with independent variables, namely the percentage of children receiving exclusive breastfeeding , the percentage of households that have access to proper sanitation , the average per capita health expenditure of the population for a month , the average length of schooling for women , and the number of poor people . The variables are obtained from Statistics Indonesia (BPS) and Study of Indonesia’s Nutritional Status (SSGI). The results showed that the best weighting function, namely adaptive bisquare with a CV value of 264.80.
MODELING STUNTING PREVALENCE IN INDONESIA USING SPLINE TRUNCATED SEMIPARAMETRIC REGRESSION Fadlirhohim, Rizki Dwi; Sifriyani, Sifriyani; Dani, Andrea Tri Rian
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss3pp2015-2028

Abstract

Semiparametric regression combines parametric and nonparametric regression approaches. It is employed when the relationship pattern of the response variable is known with some predictors, while for other predictors, the relationship pattern is uncertain. The parametric regression component in this study is linear regression, while the nonparametric component utilizes a spline truncated estimator, resulting in a semiparametric spline truncated regression model. The case study focuses on the prevalence of stunting across 34 provinces in Indonesia in 2022, revealing a relatively high prevalence of 21.60%. The research aims to determine the optimal number of knots, the best model, and factors influencing stunting prevalence in Indonesia. The findings indicate that the optimal three-knot model with a GCV of 9.30 yields an RMSE of 1.70 and R2 of 92.71%. Significance tests for simultaneous and partial parameters reveal that all predictor variables significantly influence stunting prevalence.
PENERAPAN SPATIAL DURBIN MODEL PADA DATA PENYAKIT MALARIA DI INDONESIA Nabilla, Maghrisa Ayu; Hayati, Memi Nor; Sifriyani, Sifriyani
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 19 No. 2 (2025): Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Inform
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47111/jti.v19i2.20334

Abstract

The Spatial Durbin Model (SDM) is a special case of the Spatial Autoregressive (SAR) model, involving the addition of spatial lag effects of both the dependent and independent variables. The parameter estimation used in this study is the maximum likelihood estimator. Parameter estimation for the SDM is performed at each observation location using spatial weighting. The spatial weights are calculated based on queen contiguity and customized contiguity weighting methods. This study aims to obtain the SDM and identify the factors influencing the number of malaria cases in Indonesia in 2023. The Lagrange Multiplier (LM) test indicates that there is a spatial lag in the dependent variable, with the parameter ρ being significant at a significance level of α = 0.1. Based on the results of the SDM analysis, it was found that the factors directly influencing the number of malaria cases in Indonesia in 2023 are the percentage of poor population, number of medical personnel and the percentage of households with access to adequate drinking water services. Meanwhile, the factors that have an indirect or spatial lag effect are the open unemployment rate and the percentage of poor population.
Implementation of Data Mining and Spatial Mapping in Determining National Food Security Clusterization Sifriyani, Sifriyani; Budiantara, I Nyoman; Mardianto, M. Fariz Fadillah; Febriyani, Eka Riche; Chairunnisa, Nurul Rizky; Putri, Asyifa Charmadya
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 3 (2024): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i3.19912

Abstract

This study proposes a cluster analysis of provinces based on national food security data. The research objective is to determine provincial clusters based on food indicators which include rice harvest area, distribution of rice stocks, percentage of trade margin and transportation of rice distribution, percentage of average per capita expenditure, and total per capita consumption of rice. The source of observation data for the Rice Harvested Area by Province variable is the Ministry of Agriculture, Central Bureau of Statistics and Agriculture Services throughout Indonesia. This study uses data mining techniques in data processing with the K-Medoids algorithm. The K-Medoids method is a clustering method that functions to break down data sets into several groups. The advantage of this method is that it can overcome the weakness of the K-Means method which is sensitive to outliers. Another advantage of this algorithm is that the results of the clustering process do not depend on the order in which the dataset is entered. The k-medoids clustering method can be applied to food security data by province. From grouping the data obtained three clusters, with silhouette coefficient values for cluster 1, cluster 2, and cluster 3 respectively 0.33; 0.32; and 0.44. With the largest silhouette coefficient value obtained in cluster 3 and the cluster has entered into a strong cluster structure. The research results can provide information to the government about food security grouping data in Indonesia which has an impact on the distribution and availability of food in Indonesia.
Geographically Weighted Panel Regression Modelling of Dengue Hemorrhagic Fever Data Using Exponential Kernel Function Raihani, Risti; Sifriyani, Sifriyani; Prangga, Surya
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 7, No 4 (2023): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v7i4.16235

Abstract

Geographically Weighted Panel Regression (GWPR) model is a panel regression model applied to spatial data. This research takes the Fixed Effect Model (FEM) panel regression as the global model and GWPR as the local model for dengue hemorrhagic fever (DHF) in East Kalimantan Province data over the years 2018-2020. DHF is a disease that has the potential to become an extraordinary event which is accompanied by death. In comparison to Indonesia, East Kalimantan Province's DHF Incident Rate (IR) was high in 2020. East Kalimantan's IR is 60.6 per 100,000 population, compared to Indonesia's IR of 40.0 per 100,000 population. This research aims to obtain the GWPR model, as well as to acquire factors that affect DHF in East Kalimantan Province over the years 2018-2020 based GWPR model. The parameter of the GWPR model was estimated on each observation location using the Weighted Least Square (WLS) method, which is an Ordinary Least Square (OLS) with the addition of spatial weighting. The spatial weighting on the GWPR model was determined by the best weighting function between fixed exponential and adaptive exponential. The optimum weighting function with a minimum cross-validation (CV) value of 1.7317×106 is adaptive exponential. Based on GWPR parameter testing, factors that affect DHF are local and diverse in each 10 regencies/municipalities in East Kalimantan Province. These factors are population density, number of health facilities, percentage of proper sanitation use in the household, percentage of household with qualified drinking water sources, and percentage of health services. The coefficient of determination of the GWPR model obtains a higher value than the FEM, which is 95.33%. Based on the measurement of goodness using the coefficient of determination value, it can be concluded that GWPR is the best method to model the DHF data rather than the FEM.
Nonparametric Spline Truncated Regression with Knot Point Selection Method Generalized Cross Validation and Unbiased Risk Handayani, Tutik; Sifriyani, Sifriyani; Dani, Andrea Tri Rian
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 7, No 3 (2023): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v7i3.14034

Abstract

Nonparametric regression approaches are used when the shape of the regression curve between the response variable and the predictor variable is assumed to be unknown. Nonparametric excess regression has high flexibility. A frequently used nonparametric regression approach is a truncated spline that has excellent ability to handle data whose behavior is variable at certain sub-intervals. The aim of this study was to obtain the best model of multivariable nonparametric regression with linear and quadratic truncated spline approaches using Generalized Cross Validation (GCV) and Unbiased Risk (UBR) methods and to find out the factors influencing stunting prevalence in Indonesia in 2021. The data used are the prevalence of stunting as a response variable and the predictor variable used by the percentage of infants receiving Exclusive breastfeeding for 6 months, the percentage of households with proper sanitation, the percentage of toddlers receiving Early Childhood Cultivation (IMD), the percentage of the poor population, and the percentage of pregnant womenIt's a risk. Results show that the best linear and quadratic nonparametric spline truncated regression model in modeling the stunting prevalence is linear truncated spline using the GCV method with three knot points. This model has the minimum GCV value of 7.29 with MSE value of 1.82. Factors influencing the incidence of stunting in Indonesia in 2021 include the percentage variable of infants receiving Exclusive breastfeeding for 6 months, the percentage of households with proper sanitation, the percentage of poor people, and the percentage of pregnant women at risk of KEK. 
Pemodelan Kadar Hemoglobin pada Pasien Demam Berdarah di Kota Samarinda Menggunakan Regresi Semiparametrik Spline Truncated Dani, Andrea Tri Rian; Putra, Fachrian Bimantoro; Zen, Muhammad Aldani; Sifriyani, Sifriyani; Fauziyah, Meirinda; Ratnasari, Vita; Adrianingsih, Narita Yuri
Jambura Journal of Probability and Statistics Vol 4, No 2 (2023): Jambura Journal Of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjps.v4i2.18923

Abstract

This article discusses the innovation of statistical modeling in regression analysis with a semiparametric approach applied to health data. Regression analysis is a method in statistics that takes a lot of roles in statistical modeling. Regression analysis is used to model the relationship between the independent variable (x) and the dependent variable (y). There are three approaches to regression analysis, namely parametric, nonparametric, and a combination of the two, namely semiparametric. Semiparametric regression is used when the dependent variable has a known relationship with some of the independent variables and has an unknown pattern of a relationship with some of the other independent variables. The purpose of this study was to model hemoglobin levels in dengue fever patients, with the independent variables used being the number of hematocrits (x1) and the number of leukocytes (x2). The method used is spline truncated semiparametric regression. The truncated spline estimator was chosen for the nonparametric component because it has many advantages in modeling, one of which is being able to model patterns where the form of the relationship is unknown. The parameter estimation used is the maximum estimation. Selection of the optimal knot point using Generalized Cross-Validation (GCV). Based on the results of the analysis, the truncated spline semiparametric regression model was obtained which was applied to the hemoglobin level data in a model with three knots which have a coefficient of determination of 89.074%. Based on the results of testing the hypothesis simultaneously, it can be concluded that simultaneously the independent variable has a significant effect on the dependent variable. In the partial test, it is concluded that the variables x1 and x2 have a significant influence on the dependent variable y .
Stunting Prevalence Modeling Using Nonparametric Regression of Quadratic Splines Handayani, Tutik; Sifriyani, Sifriyani; Rian Dani, Andrea Tri
Jurnal Varian Vol. 7 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v7i2.2916

Abstract

The nonparametric regression approach is used when the shape of the regression curve between the response variable and the predictor variable is assumed to be of unknown shape. The advantages of nonparametric regression have high flexibility. A nonparametric regression approach that is often used is truncated spline which has an excellent ability to handle data whose behavior changes at certain sub-sub intervals. The purpose of this study is to obtain the best model of multivariable nonparametric regression with linear and quadratic truncated spline approaches using the Generalized Cross Validation (GCV) and Unbiased Risk (UBR) methods and to find out the factors that influence the prevalence of stunting in Indonesia in 2021. The data used were the prevalence of stunting as a response variable and the predictor variable used was the percentage of infants receiving exclusive breastfeeding for 6 months, the percentage of households that have proper sanitation, the percentage of toddlers who get Early Initiation of Breastfeeding (IMD), the percentage of poor people, and the percentage of pregnant women at risk of SEZ. The results showed that the best quadratic truncated spline nonparametric regression model in modeling stunting prevalence was quadraic truncated spline using the GCV method with three knot points. This model has a minimum GCV value of 7.29 with an MSE value of 1.82 and a R2 value of 94.07%.
Co-Authors A'yun, Qonita Qurrota Afif Nurdiansyah, Mochamad Afriani, Nur Alam, Muhammad Zainul Andrea Tri Rian Dani Angeline Seru, Indra Anggraeni, Sitti Anisar, Anggi Putri Asnita, Asnita Astafira, Ilyas Atarezcha Pangruruk, Thesya Aufi, Tresna Restu Aulia, Nabila Bahriah, Ayu Chairunnisa, Nurul Rizky Christian, Diego Clemensius Arles Damayanti, Elok Dani, Andrea Dani, Andrea Tri Rian Darnah Andi Nohe Darnah, Darnah Dedi Rosadi Deni Sunaryo Eka Nur Amaliah Erlyne Nadhilah Widyaningrum Etty Puji Lestari Fadlirhohim, Rizki Dwi Fatia Fatimah Fauziyah, Meirinda Febriana Rinda Sihotang Febriyani, Eka Riche Fidia Deny Tisna Amijaya Gerald Claudio Messakh Hadi Koirudin Hidayanty, Nurul Ilma Hillidatul Ilmi I Nyoman Budiantara Ika Purnamasari Ilmi, Hillidatul Kesuma, Ahmad Rizky Khoiruddin, Ahmad Zulfikar Khotimah, Ariska Khusnul Kosasih, Raditya Arya Lestari, Tri Septi Ayu M. Fariz Fadillah Mardianto Mahmuda, Siti Mar'ah, Zakiyah Mar’ah, Zakiyah Meirinda Fauziyah Memi Nor Hayati Memi Nor Hayati Messakh, Gerald Claudio Mohammad Nurul Huda Muhammad Hunaipi Pratama Mumtaz, Ghina Fadhilla Nabilla, Maghrisa Ayu Nadia Serena NARITA YURI ADRIANINGSIH Nariza Wanti Wulan Sari Nasywa, Syarifah Novalia, Viona Nugraha, Pratama Yuly Nur, Yumi Handayani Nuraini, Ulfa Siti Nurmayanti, Wiwit Pura Padatuan, Aprianti Boma Pangruruk , Thesya Atarezcha Pangruruk Paradilla, Yunda Sasha Pasarella, Muhammad Danil Purnaraga, Tirta Putra, Fachrian Bimantoro Putri, Asyifa Charmadya Rabiatul Adawiyah Rahman, Athaya Azahra Rahmania Rahmania Raihani, Risti Rian Dani, Andrea Tri Rinanda, Farikah Ayu Rito Goejantoro, Rito Salsabila, Adellia Saputri, Marisa Nanda Sari, Ar Ruum Mia Saska, Indria Shalihatunnisa, Shalihatunnisa Siti Mahmuda SITI MAHMUDAH Sitohang, Frans Karta Sayoga Sri Wahyuningsih Sri Wahyuningsih Surya Prangga Suyitno Suyitno Suyitno Suyitno Tamba, Felicia Joy Rotua Tandi Kala, Ezra Alfrianto Tutik Handayani Tutik Handayani, Tutik Vita Ratnasari Wasono, Wasono Wianita Noviani Wiyli Yustanti Yuniarti, Desi Zarkasi, Rifka Nurfaiza Zen, Muhammad Aldani