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<![CDATA[IDENTIFIKASI FAKTOR-FAKTOR YANG MEMPENGARUHI INDEKS PEMBANGUNAN MANUSIA DI KALIMANTAN MENGGUNAKAN REGRESI PANEL ]]>
<![CDATA[Zarkasi, Rifka Nurfaiza]]>;
<![CDATA[Sifriyani, Sifriyani]]>;
<![CDATA[Prangga, Surya]]>
<![CDATA[ BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 15 No 2 (2021): BAREKENG: Jurnal Ilmu Matematika dan Terapan ]]>
Publisher : <![CDATA[PATTIMURA UNIVERSITY ]]>
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DOI: 10.30598/barekengvol15iss2pp277-282
<![CDATA[Pembangunan merupakan salah satu cara untuk meningkatkan kualitas kehidupan demi terciptanya masyarakat yang sejahtera. Pemerintah terus melakukan pembangunan di segala aspek seperti aspek pendidikan, kesehatan, dan kehidupan yang layak. Untuk mengukur keberhasilan pembangunan salah satu indikator yang bisa digunakan adalah Indeks Pembangunan Manusia (IPM). Dalam perhitungan IPM, telah melibatkan komponen ekonomi maupun non ekonomi. Penelitian ini bertujuan untuk meneliti faktor-faktor yang mempengaruhi IPM Kalimantan pada tahun 2014-2017. Karena data yang digunakan merupakan data panel yaitu gabungan antara data cross-section dan data time-series, maka IPM dimodelkan dengan regresi panel. Untuk mengestimasi model digunakan pendekatan Fixed Effect Model (FEM). Pemodelan IPM menghasilkan nilai sebesar 99,54 persen. Hasil penelitian menunjukkan bahwa untuk meningkatkan IPM dapat dilakukan dengan cara meningkatakan angka harapan hidup, rata-rata lama sekolah, harapan lama sekolah, dan pengeluaran per kapita.]]>
<![CDATA[PEMODELAN ANGKA HARAPAN HIDUP DAN ANGKA KEMATIAN BAYI DI KALIMANTAN DENGAN REGRESI NONPARAMETRIK SPLINE BIRESPON ]]>
<![CDATA[Padatuan, Aprianti Boma]]>;
<![CDATA[Sifriyani, Sifriyani]]>;
<![CDATA[Prangga, Surya]]>
<![CDATA[ BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 15 No 2 (2021): BAREKENG: Jurnal Ilmu Matematika dan Terapan ]]>
Publisher : <![CDATA[PATTIMURA UNIVERSITY ]]>
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DOI: 10.30598/barekengvol15iss2pp283-296
<![CDATA[Penelitian ini menggunakan model regresi nonparametrik birespon dengan pendekatan spline truncated. Model tersebut digunakan untuk menyelesaikan permasalahan analisis regresi yang bentuk kurvanya tidak diketahui. Pendekatan spline truncated memiliki fungsi polinomial tersegmen yang memberikan sifat fleksibilitas. Data yang digunakan dalam penelitian ini terdiri dari dua variabel respon yaitu Angka Harapan Hidup (AHH) dan Angka Kematian Bayi (AKB) di Pulau Kalimantan. Tujuan penelitian adalah untuk menentukan model regresi nonparametrik spline truncated birespon pada data AHH dan AKB dan mengetahui faktor-faktor yang mempengaruhi AHH dan AKB. Hasil penelitian diperoleh model terbaik yaitu model regresi nonparametrik spline linier birespon dengan nilai R2 sebesar 80,51 persen dan model spline tiga titik knot dengan nilai Generalized Cross Validation (GCV) minimum 7,1454. Faktor-faktor yang mempengaruhi AHH dan AKB adalah persentase keluarga menerapkan Perilaku Hidup Bersih dan Sehat (PHBS), persentase bayi diberi Air Susu Ibu (ASI) usia 0-6 bulan, laju pertumbuhan ekonomi, persentase persalinan yang dibantu oleh tenaga medis dan persentase penduduk miskin.]]>
<![CDATA[NONPARAMETRIK REGRESSION MODEL ESTIMATION WITH THE FOURIER SERIES THE FOURIER SERIES APPROACH AND ITS APPLICATION TO THE ACCUMULATIVE COVID-19 DATA IN INDONESIA ]]>
<![CDATA[Pasarella, Muhammad Danil]]>;
<![CDATA[Sifriyani, Sifriyani]]>;
<![CDATA[Amijaya, Fidia Deny Tisna]]>
<![CDATA[ BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 4 (2022): BAREKENG: Journal of Mathematics and Its Applications ]]>
Publisher : <![CDATA[PATTIMURA UNIVERSITY ]]>
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DOI: 10.30598/barekengvol16iss4pp1167-1174
<![CDATA[The nonparametric regression model is applied to regression curves for which the regression curve is unknown. Fourier series estimation is an approach in nonparametric regression, which has high flexibility and is able to adjust to the local nature of data effectively. The purpose of the research is to obtain an estimate of the nonparametric regression model with the Fourier series approach with optimal oscillation values and the model suitability of the positive case of Covid-19 in Indonesia. Research on modeling positive cases of Covid-19 in Indonesia using nonparametric regression with the best Fourier series approach is found in the third oscillation by having a minimum GCV of 78969281 with the best model criteria R2 = 97.86% with influencing factors are the percentage of active smokers, the number of health workers, the number of health service facilities, population density and the percentage of the poor population.]]>
<![CDATA[IMPLEMENTATION OF THE FUZZY GUSTAFSON-KESSEL METHOD ON GROUPING DISTRICTS/CITIES IN KALIMANTAN ISLAND BASED ON POVERTY ISSUES FACTORS ]]>
<![CDATA[Paradilla, Yunda Sasha]]>;
<![CDATA[Hayati, Memi Nor]]>;
<![CDATA[Sifriyani, Sifriyani]]>
<![CDATA[ BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications ]]>
Publisher : <![CDATA[PATTIMURA UNIVERSITY ]]>
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DOI: 10.30598/barekengvol17iss1pp0125-0134
<![CDATA[Cluster analysis is an analysis that is useful in summarizing data by grouping objects based on certain similarity characteristics. One of the group analysis is Fuzzy Gustafson-Kessel (FGK) which is the development of the Fuzzy C-Means (FCM) method. The FGK method has a good way in adjusting the form of cluster membership function correctly for a data. This study aims to determine the results of the optimal number of groups based on the Partition Coefficient (PC) and Classification Entropy (CE) validity indexes and to find out the results of grouping 56 districts/cities on the island of Kalimantan based on poverty issue factors in 2021. The optimal number of groups using the FGK method based on the validity indexes of PC and CE are two groups. The first group and the second group each consist of 28 districts/cities in Kalimantan Island.]]>
<![CDATA[REGRESSION NONPARAMETRIC SPLINE ESTIMATION ON BLOOD GLUCOSE OF INPATIENTS DIABETES MELLITUS AT SAMARINDA HOSPITAL ]]>
<![CDATA[Sari, Ar Ruum Mia]]>;
<![CDATA[Sifriyani, Sifriyani]]>;
<![CDATA[Huda, Mohammad Nurul]]>
<![CDATA[ BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications ]]>
Publisher : <![CDATA[PATTIMURA UNIVERSITY ]]>
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DOI: 10.30598/barekengvol17iss1pp0147-0154
<![CDATA[This study used a biresponse nonparametric regression method with truncated spline estimation that used two response variables. Nonparametric regression method is used when the regression curve is not known for its shape and pattern.One of the nonparametric regression model approaches that is often used is the spline. The truncated spline approach has a segmented polynomial function that provides flexibility. The data used in the study were blood glucose levels in patients with diabetes mellitus, cholesterol levels, and triglyceride levels in 2020. From the results of the study, the best nonparametric biresponse spline truncated regression model with three knot points has been obtained where the minimum GCV value is and has the an value of .]]>
<![CDATA[GEOGRAPHICALLY WEIGHTED PANEL REGRESSION (GWPR) FOR COVID-19 CASE IN INDONESIA ]]>
<![CDATA[Mar'ah, Zakiyah]]>;
<![CDATA[Sifriyani, Sifriyani]]>
<![CDATA[ BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 2 (2023): BAREKENG: Journal of Mathematics and Its Applications ]]>
Publisher : <![CDATA[PATTIMURA UNIVERSITY ]]>
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DOI: 10.30598/barekengvol17iss2pp0879-0886
<![CDATA[Coronavirus disease 2019 (COVID-19) is a newly emerging infectious disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) which was declared a pandemic by the World Health Organization (WHO) on March 11th, 2020. The response to this ongoing pandemic requires extensive collaboration across the scientific community to contain its impact and limit further transmission. Modeling to see cause-and-effect relationships in an event usually uses the Multiple Linear Regression (Ordinary Least Square) method. But in the case of Covid-19, the spread of the virus occurred from one location to another, so there was an indication that there was a spatial effect on the incident. In this study, we did not only look at spatial perspective but also considered time series data, so the method used was Geographically Weighted Panel Regression (GWPR). This study modeled the number of positive cases of Covid-19 in 34 provinces in Indonesia that occurred from March 2020 to August 2021 and looked at what factors influenced the number of positive cases of Covid-19 in each province. GWPR was performed with the assumption of a Fixed Effect Model (FEM). The FEM assumption was used by considering that the conditions of each observation unit were different. Based on the results, the best GWPR model obtained was the GWPR model with a Fixed Gaussian Kernel. The predictor variables that influenced the number of positive cases of Covid-19 were different at each location and tent to cluster at certain locations.]]>
<![CDATA[TUBERCULOSIS CASE MODEL USING GCV AND UBR KNOT SELECTION METHODS IN TRUNCATED SPLINE NONPARAMETRIC REGRESSION ]]>
<![CDATA[Anggraeni, Sitti]]>;
<![CDATA[Sifriyani, Sifriyani]]>;
<![CDATA[A'yun, Qonita Qurrota]]>
<![CDATA[ BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications ]]>
Publisher : <![CDATA[PATTIMURA UNIVERSITY ]]>
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DOI: 10.30598/barekengvol17iss3pp1565-1574
<![CDATA[The nonparametric regression approach is used when the shape of the regression curve is not known. The advantage of nonparametric regression is that it has a high degree of flexibility. The truncated spline is a method in the nonparametric regression approach, which can overcome changing data patterns at certain sub-intervals with the help of knot points. The purpose of this research is to obtain the best truncated spline nonparametric regression model estimates based on the GCV and UBR knot point selection methodsThe data used in this study came from the publications of the Indonesian Ministry of Health and BPS Indonesia. The response variable used is the percentage of successful treatment of tuberculosis patients in Indonesia with predictor variables namely the percentage of people who smoke over the age of 15 years, the percentage of households that have access to proper sanitation, the percentage of poor people, the percentage of food processing establishments that meet the standard requirements , national health insurance membership coverage and percentage of accredited hospitals. The results showed that the best model came from the GCV method using three knots. This model produces an MSE value of 3.65 with value of 97.04. The value indicates that the predictor variable used in this study affects the response variable by 97.04% while the other 2.96% is influenced by other variables that are not included in this study.]]>
<![CDATA[APPLICATION OF NONPARAMETRIC REGRESSION SPLINE TRUNCATED FOR MODELING THE HEIGHT OF YEOP CHAGI KICKS OF TAEKWONDO ATHLETES IN SAMARINDA CITY ]]>
<![CDATA[Sitohang, Frans Karta Sayoga]]>;
<![CDATA[Sifriyani, Sifriyani]]>;
<![CDATA[Mahmuda, Siti]]>
<![CDATA[ BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 2 (2024): BAREKENG: Journal of Mathematics and Its Application ]]>
Publisher : <![CDATA[PATTIMURA UNIVERSITY ]]>
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DOI: 10.30598/barekengvol18iss2pp0657-0666
<![CDATA[Nonparametric regression is a model approach method that is used when the shape of the regression curve between the response variable and the predictor variable is assumed to have an unknown shape or pattern. One of the estimators in the nonparametric regression approach is the truncated spline which has the ability to handle data whose behavior changes at certain sub intervals. The purpose of this study was to obtain the estimated value of the parameters of the nonparametric regression model with a truncated spline approach at one knot point, two knot points, and three knot points for kick height data of yeop chagi taekwondo athletes in Samarinda City. The results showed that the truncated spline nonparametric regression model was the best in modeling high kick height data for yeop chagi taekwondo athletes in Samarinda City with three knot points. This model has the minimum Generalized Cross Validation (GCV) value of 7.94 with an R2 value of 94.72% and a Mean Square Error (MSE) value of 2.62. Based on the results of the model parameter significance test, it was concluded that the factors that influence the kick height of the yeop chagi taekwondo athlete in Samarinda City are flexibility, leg power, leg length, and waist circumference.]]>
<![CDATA[ESTIMATION OF A BI-RESPONSE TRUNCATED SPLINE NONPARAMETRIC REGRESSION MODEL ON LIFE EXPECTANCY AND PREVALENCE OF UNDERWEIGHT CHILDREN IN INDONESIA ]]>
<![CDATA[Anisar, Anggi Putri]]>;
<![CDATA[Sifriyani, Sifriyani]]>;
<![CDATA[Dani, Andrea Tri Rian]]>
<![CDATA[ BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 4 (2023): BAREKENG: Journal of Mathematics and Its Applications ]]>
Publisher : <![CDATA[PATTIMURA UNIVERSITY ]]>
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DOI: 10.30598/barekengvol17iss4pp2011-2022
<![CDATA[Researchers use the nonparametric regression method because it provides excellent flexibility in the modeling process. Nonparametric regression procedures can be used if the relationship pattern between the predictor and response variables is unknown. The truncated spline method is one of the most frequently used nonparametric regression methods. A truncated spline is a polynomial slice with continuous segmented properties, and the resulting curve is relatively smooth. The advantage of truncated splines is that they can be used on data that experience behavior changes at specific intervals. The nonparametric spline truncated bi-response regression approach is used when one or more predictor variables affect the two response variables with the assumption that there is a correlation between the response variables. This study aimed to obtain the best spline truncated bi-response nonparametric regression model on life expectancy data and the prevalence of underweight children in Indonesia in 2021. The data used comes from the Central Bureau of Statistics and the Indonesian Ministry of Health. The optimal knot point selection method uses the Generalized Cross Validation (GCV) method. The results showed that the best model formed was obtained using three-knot points based on a minimum GCV value of 22.77 and a coefficient of determination of 99.58%.]]>
<![CDATA[MIXED ESTIMATORS OF TRUNCATED SPLINE-EPANECHNIKOV KERNEL ON NONPARAMETRIC REGRESSION AND ITS APPLICATIONS ]]>
<![CDATA[Sifriyani, Sifriyani]]>;
<![CDATA[Dani, Andrea Tri Rian]]>;
<![CDATA[Fauziyah, Meirinda]]>;
<![CDATA[Mar’ah, Zakiyah]]>
<![CDATA[ BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 4 (2023): BAREKENG: Journal of Mathematics and Its Applications ]]>
Publisher : <![CDATA[PATTIMURA UNIVERSITY ]]>
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DOI: 10.30598/barekengvol17iss4pp2023-2032
<![CDATA[Research on innovations in the statistics and statistical computing program systems implemented in the health sector. The development of a mixed estimator model is an innovation of nonparametric regression analysis by combining two approaches in nonparametric regression, namely the truncated spline estimator and the Epanechnikov kernel. The urgency of this study is that there are often cases where there are different data patterns from each predictor variable. In addition, by using only one form of the estimator in estimating a multivariable regression curve, the result is that the estimator obtained will not match the data pattern. The research objective was to find a mixed estimator between the truncated spline and the Epanechnikov kernel and the estimator results were applied to Dengue Hemorrhagic Fever case data. The unit of observation is a province in Indonesia and This study relied on secondary data received from the Central Statistical Agency (BPS) and the Health Office. Based on the analysis results, it was found that the best model of nonparametric regression with a mixed estimator of the truncated spline and Epanechnikov Kernel is a model with 3 knots with a combination of variables. The coefficient of determination (R2) is 98.11%. We can conclude that the mixed estimator tends to follow actual data and represents a nonparametric regression model with a mixed estimator that can predict the number of Dengue Hemorrhagic Fever Cases in Indonesia]]>
