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FAKTOR-FAKTOR YANG MEMPENGARUHI CONTRACEPTIVE PREVALENCE RATE (CPR) DI INDONESIA DENGAN PENDEKATAN REGRESI NONPARAMETRIK SPLINE Cristie, Diana; Budiantara, I Nyoman
Jurnal Sains dan Seni ITS Vol 4, No 1 (2015)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM), ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j23373520.v4i1.9234

Abstract

Salah satu permasalahan krusial yang terkait dengan kependudukan berkaitan dengan target MDGs 2015 adalah semakin meningkatnya jumlah penduduk dan tingginya laju pertumbuhan penduduk. Program Keluarga Berencana (KB) merupakan salah satu usaha pemerintah dalam mengendalikan jumlah penduduk. Ukuran yang digunakan untuk mengevaluasi keberhasilan program KB adalah angka prevalensi pemakaian kontrasepsi (CPR). Penelitian ini bertujuan untuk mengetahui faktor-faktor yang mempengaruhi CPR. Pendekatan regresi nonparametrik spline digunakan karena dapat mengestimasi data yang tidak memiliki pola tertentu. Regresi spline yang dipilih adalah yang memiliki titik knot optimum dengan nilai GCV minimum, yaitu tiga titik knot. Berdasarkan hasil pengujian parameter diketahui faktor-faktor yang berpengaruh terhadap CPR adalah persentase penduduk miskin, persentase wanita berumur 15 tahun ke atas dengan pendidikan tertinggi kurang atau sama dengan SLTP, persentase wanita berumur 10 tahun ke atas dengan usia perkawinan pertama 18 tahun ke bawah, persentase wanita berumur 10 tahun ke atas  yang pernah kawin dengan anak lahir hidup kurang atau sama dengan dua, dan persentase wanita berumur 15 tahun ke atas yang bekerja. Model regresi nonparametrik spline ini mempunyai koefisien determinasi ( sebesar 95,59 persen.
Development of Technology Parameter Towards Shipbuilding Productivity Predictor Using Cubic Spline Approach Suwasono, Bagiyo; Widjaja, Sjarief; Zubaydi, Achmad; Yuliadi, Zaed; Budiantara, I Nyoman
Makara Journal of Technology Vol 14, No 2 (2010)
Publisher : Directorate of Research and Community Services, Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (261.068 KB) | DOI: 10.7454/mst.v14i2.188

Abstract

Ability of production processes associated with state-of-the-art technology, which allows the shipbuilding, is customized with modern equipment. It will give impact to level of productivity and competitiveness. This study proposes a nonparametric regression cubic spline approach with 1 knot, 2 knots, and 3 knots. The application programs Tibco Spotfire S+ showed that a cubic spline with 2 knots (4.25 and 4.50) gave the best result with the value of GCV = 56.21556, and R2 = 94.03%.Estimation result of cubic spline with 2 knots for the PT. Batamec shipyard = 35.61 MH/CGT, PT. Dok & Perkapalan Surabaya = 27.49 MH/CGT, PT. Karimun Sembawang Shipyard = 27.49 MH/CGT, and PT. PAL Indonesia = 19.89 MH/CGT.
ANALISIS REGRESI SEMIPARAMETRIK PADA KASUS HILANGNYA RESPON Yahya, Irma; Budiantara, I Nyoman; Fitriasari, Kartika
MATEMATIKA Vol 9, No 1 (2006): JURNAL MATEMATIKA
Publisher : MATEMATIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (97.892 KB)

Abstract

In the specific cases of experiment, not all data (response) may be available, which is called missing response cases. It?s appear for various reasons.  For the existing problem, inference statistics cannot be applied directly.  The aim of this research is to consider about certain method to impute the missing response which is related to semiparametric regression, as a goodness of fit measurement of the used method, suppose an estimator  which is compared to the mean of complete response, then consider asymptotic distribution, consistency and efficiency of parametrics component estimator. By using Kernel approximation, the resulted of nonparametrics estimator and by least square method, the resulted parametric component .The application to minimum temperature?s data in 56 cities at USA, estimator value of  for several confidence interval tend to be similar to the mean value of complete response.  
MODEL REGRESI NONPARAMETRIK ADITIF DENGAN FUNGSI LINK Nasfirah, Siti; Budiantara, I Nyoman; Fitriasari, Kartika
MATEMATIKA Vol 9, No 2 (2006): JURNAL MATEMATIKA
Publisher : MATEMATIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (200.262 KB)

Abstract

??If  given a couple of data  and  the  relationship  between  variable x and y is revealed as nonparametric regression model with is an error which is random variable with zero mean and variance  and is unknown function. If is an additive function with x  Rd, d ? 2. By using link function F, this research is focused on the way to get the estimator in additive component with link function by using two-stage methode and completed with simulation using logit link function. The result shows that two-stage estimator is eisier be used to model nonparametric regression by link function rather than Nadaraya-Watson and local linear. From the simulation, it?s obtained that of the mean and R2 value of the estimator is similar. 
APLIKASI SPLINE ESTIMATOR TERBOBOT Budiantara, I Nyoman
Jurnal Teknik Industri Vol 3, No 2 (2001): DESEMBER 2001
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (28.297 KB) | DOI: 10.9744/jti.3.2.pp. 57-62

Abstract

We considered the nonparametric regression model : Zj = X(tj) + ej, j = 1,2,…,n, where X(tj) is the regression curve. The random error ej are independently distributed normal with a zero mean and a variance s2/bj, bj > 0. The estimation of X obtained by minimizing a Weighted Least Square. The solution of this optimation is a Weighted Spline Polynomial. Further, we give an application of weigted spline estimator in nonparametric regression. Abstract in Bahasa Indonesia : Diberikan model regresi nonparametrik : Zj = X(tj) + ej, j = 1,2,…,n, dengan X (tj) kurva regresi dan ej sesatan random yang diasumsikan berdistribusi normal dengan mean nol dan variansi s2/bj, bj > 0. Estimasi kurva regresi X yang meminimumkan suatu Penalized Least Square Terbobot, merupakan estimator Polinomial Spline Natural Terbobot. Selanjutnya diberikan suatu aplikasi estimator spline terbobot dalam regresi nonparametrik. Kata kunci: Spline terbobot, Regresi nonparametrik, Penalized Least Square.
PEMODELAN B-SPLINE DAN MARS PADA NILAI UJIAN MASUK TERHADAP IPK MAHASISWA JURUSAN DISAIN KOMUNIKASI VISUAL UK. PETRA SURABAYA Budiantara, I Nyoman; Suryadi, Fredi; Otok, Bambang Widjanarko; Guritno, Suryo
Jurnal Teknik Industri Vol 8, No 1 (2006): JUNE 2006
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (128.513 KB) | DOI: 10.9744/jti.8.1.pp. 1-13

Abstract

Regression analysis is constructed for capturing the influences of independent variables to dependent ones. It can be done by looking at the relationship between those variables. This task of approximating the mean function can be done essentially in two ways. The quiet often use parametric approach is to assume that the mean curve has some prespecified functional forms. Alternatively, nonparametric approach, .i.e., without reference to a specific form, is used when there is no information of the regression function form (Haerdle, 1990). Therefore nonparametric approach has more flexibilities than the parametric one. The aim of this research is to find the best fit model that captures relationship between admission test score to the GPA. This particular data was taken from the Department of Design Communication and Visual, Petra Christian University, Surabaya for year 1999. Those two approaches were used here. In the parametric approach, we use simple linear, quadric cubic regression, and in the nonparametric ones, we use B-Spline and Multivariate Adaptive Regression Splines (MARS). Overall, the best model was chosen based on the maximum determinant coefficient. However, for MARS, the best model was chosen based on the GCV, minimum MSE, maximum determinant coefficient. Abstract in Bahasa Indonesia : Analisa regresi digunakan untuk melihat pengaruh variabel independen terhadap variabel dependent dengan terlebih dulu melihat pola hubungan variabel tersebut. Hal ini dapat dilakukan dengan melalui dua pendekatan. Pendekatan yang paling umum dan seringkali digunakan adalah pendekatan parametrik. Pendekatan parametrik mengasumsikan bentuk model sudah ditentukan. Apabila tidak ada informasi apapun tentang bentuk dari fungsi regresi, maka pendekatan yang digunakan adalah pendekatan nonparametrik. (Haerdle, 1990). Karena pendekatan tidak tergantung pada asumsi bentuk kurva tertentu, sehingga memberikan fleksibelitas yang lebih besar. Tujuan penelitian ini adalah mendapatkan model terbaik mengenai nilai ujian masuk terhadap nilai IPK (Indek Prestasi Kumulatif) mahasiswa jurusan Disain Komunikasi Visual tahun 1999 di Universitas Kristen Petra Surabaya dengan analisis regresi, baik parametrik maupun nonparametrik. Pendekatan regresi parametrik menggunakan regresi linear sederhana, kuadratik dan kubik, sedangkan regresi nonparametrik digunakan B-Spline dan Multivariate Adaptive Regression Splines (MARS). Secara keseluruhan, model terbaik dipilih berdasarkan koefisien determinasi terbesar. Namun demikian untuk MARS, model terbaik dipilih berdasarkan pada GCV, minimum MSA dan koefisien determinasi terbesar. Kata kunci: regresi nonparametrik, B-Spline, MARS, koefisien determinasi.
Spline Nonparametric Regression Analysis of Stress-Strain Curve of Confined Concrete Tavio, Tavio; Budiantara, I Nyoman; Kusuma, Benny
Civil Engineering Dimension Vol 10, No 1 (2008): MARCH 2008
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (662.826 KB) | DOI: 10.9744/ced.10.1.pp. 14-27

Abstract

Due to enormous uncertainties in confinement models associated with the maximum compressive strength and ductility of concrete confined by rectilinear ties, the implementation of spline nonparametric regression analysis is proposed herein as an alternative approach. The statistical evaluation is carried out based on 128 large-scale column specimens of either normal-or high-strength concrete tested under uniaxial compression. The main advantage of this kind of analysis is that it can be applied when the trend of relation between predictor and response variables are not obvious. The error in the analysis can, therefore, be minimized so that it does not depend on the assumption of a particular shape of the curve. This provides higher flexibility in the application. The results of the statistical analysis indicates that the stress-strain curves of confined concrete obtained from the spline nonparametric regression analysis proves to be in good agreement with the experimental curves available in literatures
MODEL SPLINE DENGAN ERROR BERKORELASI Nalim, Nalim; Budiantara, I Nyoman
MATEMATIKA Vol 8, No 3 (2005): JURNAL MATEMATIKA
Publisher : MATEMATIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (111.739 KB)

Abstract

Spline smoothing is a popular method for estimating the function in nonparametric regression  model. Its performance depends greatly on the choice of smoothing parameters. Many methods of selecting smoothing parameters such as GCV, GML and UBR are developed under the assumption of independent observations. They fail badly when data are correlated. In nonparametric regression, correlated error could be solved by finding weighted estimator and determine the correlation matrix from the error. Estimation of nonparametric function is obtained by minimizing the penalized weighted least-square (PWLS). In this paper, the extension of the GML method to estimate the smoothing parameters and correlation simulataneously is presented. Simulation was conducted to evaluate and to compare the performance of  the original GML and the extended GML method. The extended GML is recommended since it works well in all simulation scheme. This method is also able to illustrate the data concentration data in a continous chemical process
ESTIMASI SELANG KEPERCAYAAN NILAI UJIAN NASIONAL BERBASIS KOMPETENSI BERDASARKAN MODEL REGRESI SEMIPARAMETRIK MULTIRESPON TRUNCATED SPLINE Hidayati, Lilik; Chamidah, Nur; Budiantara, I Nyoman
MEDIA STATISTIKA Vol 13, No 1 (2020): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.13.1.92-103

Abstract

Confidence interval estimation is important in statistical inference for the parameters of the regression model, but the theory of confidence interval estimation for multi-response semiparametric regression model parameters based on the truncated spline estimator has not been examined. In this study, we estimate the confidence interval of the multi-response semiparametric regression model based on the truncated spline estimator by using pivotal quantity method with the central limit theorem approach. This confidence interval theory is applied to data of competency-based national exam (UNBK) scores in West Nusa Tenggara Province where its UNBK  in the lowest position among other provinces in Indonesia. The method used for estimating parameters is weighted least square. The best model is determined based on the Generalized Cross Validation (GCV) minimum value. Based on the estimated 95% confidence interval of parameters of the multi-response truncated spline semiparametric regression model, the results showed that the insignificant factors affecting the UNBK scores were gender and parental education duration while the report card of scores and USBK scores had a positive effect on the UNBK scores but only the UNBK scores of mathematics that report card of scores factor has a negative effect on it.