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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Jurnal Statistika Universitas Muhammadiyah Semarang BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Zeta - Math Journal J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika Jambura Journal of Mathematics M A T H L I N E : Jurnal Matematika dan Pendidikan Matematika Jurnal Varian Mandalika Mathematics and Educations Journal Inferensi JAMBURA JOURNAL OF PROBABILITY AND STATISTICS Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Jurnal Litbang Edusaintech ARRUS Journal of Mathematics and Applied Science Mathematics and Applications (MAp) Journal Jurnal Siger Matematika Journal of Research Applications in Community Services Eksponensial Asian Journal of Science, Technology, Engineering, and Art Mikailalsys Journal of Mathematics and Statistics
Dani, Andrea Tri Rian
Universitas Mulawarman
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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. 
ANALISIS KLASIFIKASI ARTIST MUSIC MENGGUNAKAN MODEL REGRESI LOGISTIK BINER DAN ANALISIS DISKRIMINAN DANI, ANDREA TRI RIAN; RATNASARI, VITA; NI'MATUZZAHROH, LUDIA; AVIANTHOLIB, IGAR CALVERIA; NOVIDIANTO, RADITYA; ADRIANINGSIH, NARITA YURI
Jambura Journal of Probability and Statistics Vol 3, No 1 (2022): Jambura Journal Of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjps.v3i1.13708

Abstract

Characteristics of a song are an important aspect that must be kept authentic by a singer. Using the Spotify API feature, we can extract the characteristics or elements of a song sung by a singer.  There are eight (8) elements that we can get from the extraction of a song, namely: Danceability, Energy, Loudness, Speechiness, Acousticness, Liveness, Valence, and Tempo. Based on the extraction results, we can label the music artist using the classification analysis method. In this study, the labels are music artists, namely Ariana Grande and Taylor Swift. This study aims to obtain the classification of music artist labels using binary logistic regression methods and discriminant analysis. The response variable used in this study is Artist Music (Y) which is categorized into two categories, namely Ariana Grande (Y=0) and Taylor Swift (Y=1). The data will be divided into training and testing data with the proportion of data 90:10 and 80:20. Based on the results of the analysis, the binary regression model that was built, with the proportion of training testing data that is 90:10 has a classification accuracy for data testing of 90.00%.
ESTIMASI MODEL REGRESI SEMIPARAMETRIK SPLINE TRUNCATED MENGGUNAKAN METODE MAXIMUM LIKELIHOOD ESTIMATION (MLE) ADRIANINGSIH, NARITA YURI; DANI, ANDREA TRI RIAN
Jambura Journal of Probability and Statistics Vol 2, No 2 (2021): Jambura Journal Of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjps.v2i2.10255

Abstract

Regression modeling with a semiparametric approach is a combination of two approaches, namely the parametric regression approach and the nonparametric regression approach. The semiparametric regression model can be used if the response variable has a known relationship pattern with one or more of the predictor variables used, but with the other predictor variables the relationship pattern cannot be known with certainty. The purpose of this research is to examine the estimation form of the semiparametric spline truncated regression model. Suppose that random error is assumed to be independent, identical, and normally distributed with zero mean and variance , then using this assumption, we can estimate the semiparametric spline truncated regression model using the Maximum Likelihood Estimation (MLE) method.  Based on the results, the estimation results of the semiparametric spline truncated regression model were obtained  p=(inv(M'M)) M'y 
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 .
Daily Rainfall Forecasting with ARIMA Exogenous Variables and Support Vector Regression Permata, Regita Putri; Ni'mah, Rifdatun; Dani, Andrea Tri Rian
Jurnal Varian Vol. 7 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v7i2.3202

Abstract

There is a seasonal element every year, with the dry season often lasting from May to October and the rainy season lasting from November to April. However, climate change causes the changing of the rainy and dry seasons to be erratic, so it is necessary to anticipate weather conditions. Prediction of rainfall is used to see natural conditions in the future with time series modeling. The rainfall modeling method at the six Surabaya observation posts used is the Autoregressive Integrated Moving Average with exogenous variables (ARIMAX) and Support Vector Regression. The exogenous variable used is the captured seasonal pattern of rainfall. The SVR model uses input lags from the ARIMAX model and parameter tuning uses the Kernel Radial Based Function. Selection of the best model uses the minimum RMSE value. The results showed that the average occurrence of rain at the six rainfall observation posts occurred in January, February, March, April, November and December. The ARIMAX method in this study is well used to predict rainfall in Gubeng and rainfall in Wonorejo. The SVR input lag ARIMAX method is good for predicting rainfall for Keputih, Kedung Cowek, Wonokromo and Gunung Sari. Nonparametric methods are better used to forecast rainfall data because they are able to capture data patterns with greater volatility than parametric methods, one of which is the SVR method.
Enhancing the Reliability of the Flood Early Warning System in Samarinda City Through the Hybrid SARIMA-RBFNN Model Rahmah, Syifa Mutia; Dani, Andrea Tri Rian; Wahyuningsih, Sri
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 13 Issue 3 December 2025
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v13i3.34268

Abstract

Rainfall data in Samarinda City exhibit seasonal patterns that play a crucial role in increasing flood risk during certain periods. To enhance the effectiveness of the early warning system, this study developed a hybrid SARIMA-RBFNN model. The Seasonal Autoregressive Integrated Moving Average (SARIMA) model was applied to capture linear seasonal patterns, while the Radial Basis Function Neural Network (RBFNN) was employed to model nonlinear residuals from SARIMA. Model performance was assessed using the Symmetric Mean Absolute Percentage Error (SMAPE) and Root Mean Squared Error Prediction (RMSEP). Compared to the single SARIMA model (SMAPE = 34.699%, RMSEP = 82.255), the hybrid SARIMA–RBFNN achieved lower in-sample errors (SMAPE = 34.175%, RMSEP = 78.577) and demonstrated more stable performance for out-of-sample data. This indicates that the hybrid model provides a more balanced and reliable prediction by capturing nonlinear rainfall fluctuations that SARIMA alone could not model effectively. Forecasts for 2024 revealed a consistent seasonal trend, peaking mid-year. These findings indicate that the hybrid model can improve the reliability of the flood early warning system in Samarinda by providing more accurate rainfall predictions.
Co-Authors A'yun, Qonita Qurrota AINURROCHMAH, ALIFTA Alifta Ainurrochmah Alifta Ainurrochmah Anisar, Anggi Putri AVIANTHOLIB, IGAR CALVERIA Avrilia, Khairunnisa budi Cahyono Budi, Ennesya Estya Candra, Yossy Chandra, Yossy Dandito Laa Ull Darnah Darnah, Darnah Dimas Nugroho Dwi Seputro Fachrian Bimantoro Putra Fadlirhohim, Rizki Dwi Fauziyah, Meirinda Goenjatoro, Rito Hadi Koirudin Hardina Sandariria Hinadang, Elen A. I Gusti Bagus Ngurah Diksa I Nyoman Budiantara I Nyoman Budiantara Ibaad, Muhammad Irsadul Koirudin, Hadi Kosasih, Raditya Arya Krisna Rendi Awalludin Ludia Ni'matuzzahroh Ludia Ni’matuzzahroh M. Fathurahman Mar’ah, Zakiyah Meirinda Fauziyah Melisa Arumsari Memi Nor Hayati Mislan Muawanah, Chusnul Muhammad Aldani Zen Nanda Arista Rizki NARITA YURI ADRIANINGSIH Ni'matuzzahroh, Ludia Nilam Novita Sari Novidianto, Raditya Oroh, Chiko Zet Puspitasari, Melda Putra, Fachrian Bimantoro Qonita Qurrota A'yun Raditya Arya Kosasih Raditya Novidianto Rahayu, Joana K. Rahmah, Syifa M. Rahmah, Syifa Mutia Rahmania Rahmania Ramadhani, Bagus D. Ratna Kusuma, Ratna Regita Putri Permata Rifdatun Ni’mah Riry Sriningsih Rito Goejantoro, Rito Samsurianto Samsurianto Sifriyani, Sifriyani Siringoringo, Meiliyani Siti Mahmuda Sitinjak, Jesselin Paskalis Sri Wahyuningsih Sri Wahyuningsih Sri Wigantono Surya Prangga Suyitno Suyitno Tanur, Erwin Tutik Handayani, Tutik Vita Ratnasari Watika, Noor Hikmah Zen, Muhammad Aldani