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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Zero : Jurnal Sains, Matematika, dan Terapan Jambura Journal of Mathematics ESTIMASI: Journal of Statistics and Its Application Indonesian Journal of Statistics and Its Applications
Arifin, Samsul
Department of Statistics, Lambung Mangkurat University, Banjarbaru
Agriculture, Biological Sciences & Forestry Chemistry Computer Science & IT Mathematics Physics Other

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Kemampuan Estimator Spline Linear dalam Analisis Komponen Utama Samsul Arifin; Anna Islamiyati; Raupong Raupong
ESTIMASI: Journal of Statistics and Its Application Vol. 1, No. 1, Januari, 2020 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (305.82 KB) | DOI: 10.20956/ejsa.v1i1.9262

Abstract

In the formation of a regression model there is a possibility of a relationship between one predictor variable with other predictor variables known as multicollinearity. In the parametric approach, multicollinearity can be overcome by the principal component analysis method. Principal component analysis (PCA) is a multivariate analysis that transforms the originating variables that are correlated into new variables that are not correlated by reducing a number of these variables so that they have smaller dimensions but can account for most of the diversity of the original variables. In some research data that do not form parametric patterns also allows the occurrence of multicollinearity on the predictor variables. This study examines the ability of spline estimators in the analysis of the main components. The data contained multicollinearity and was applied to diabetes mellitus data by taking cholesterol type factors as predictors. Based on the estimation results, one main component is obtained to explain the diversity of variables in diabetes data with the best linear spline model at one knot point.
Modeling of COVID-19 Cases in Indonesia with the Method of Geographically Weighted Regression Samsul Arifin; Erna Tri Herdiani
Jurnal Matematika, Statistika dan Komputasi Vol. 19 No. 2 (2023): JANUARY 2023
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v19i2.23481

Abstract

The COVID-19 pandemic has spread to all corners of the world, including Indonesia. Various factors affect the spread of COVID-19 cases in an area so that the government and the community can make prevention and control efforts so that this pandemic does not spread. This study aims to model the number of COVID-19 cases in Indonesia using the Geographically Weighted Regression (GWR) method, which develops a linear regression model. The GWR model uses weights based on the location of each observation so that the model is obtained for that location. Determine the weighting on the bandwidth. Optimum bandwidth selection is obtained by minimizing the value of Cross-Validation (CV). The GWR model using a fixed bisquare kernel weighting function has an optimum bandwidth of 0.999948 with a minimum CV value of 397.076.128 with a coefficient of determination R2   of 85.1 %. The results show that the number of positive cases positively correlates with the number of patients who died from COVID-19. In contrast, the number of recovered patients negatively correlates with the number of patients who died from COVID-19.
KLASIFIKASI FAKTOR-FAKTOR PENYEBAB PENYAKIT DIABETES MELITUS DI RUMAH SAKIT UNHAS MENGGUNAKAN ALGORITMA C4.5 Dewi Rahma Ente; Sri Astuti Thamrin; Samsul Arifin; Hedi Kuswanto; Andreza Andreza
Indonesian Journal of Statistics and Applications Vol 4 No 1 (2020)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v4i1.330

Abstract

Diabetes mellitus (DM) is one of the chronic and deadly diseases that are widely observed in various countries today. This disease continues and is increasing to a very alarming stage. This study aims to identify and see the relationship between factors that influence DM disease. The method used in this research is C4.5 algorithm which is one of the algorithms used to make predictive classifications. Classification is one of the processes in data mining that aims to find patterns in relatively large data that use the representations in the form of decision trees. This method is applied to data from medical records of patients with DM in 2014-2018 taken from the Hasanuddin University Teaching Hospital. The results obtained indicate that there are four factors that influence the prediction of a patient's DM status namely; Fasting Blood Glucose (GDP), LDL Cholesterol, Triglycerides, and Body Weight.
LASSO Quantile Regression for Predictive Modeling of Dengue Hemorrhagic Fever Incidence in Indonesia Arifin, Samsul; Anggraini, Dewi; Susanti, Dewi Sri
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 1 (2025): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v9i1.24877

Abstract

Dengue Hemorrhagic Fever (DHF) is an endemic disease that continues to burden public health in Indonesia, characterized by an uneven pattern of distribution influenced by various environmental, social, and economic factors. This study aims to develop a predictive model for DHF incidence using the LASSO Quantile Regression approach, which can reveal the influence of predictor variables across different quantiles while addressing multicollinearity and overfitting issues. The data used includes nine predictor variables obtained from BPS and BMKG for the year 2025. The estimation results show that the urban/rural Area Size consistently affects all quantiles, while the percentage of population living in poverty and the number of healthcare facilities are significant only at the 0.25 and 0.50 quantiles. Model evaluation indicates that this approach provides good predictive performance, especially at the 0.25 quantile, with a R² pseudo value of 0.2838. These findings suggest that the LASSO Quantile Regression method is effective in identifying the determinants of DHF in Indonesia.
A Comparative Study of Linear and Quadratic Spline Regression Models for Predicting HbA1c Levels in Patients with Diabetes Mellitus Arifin, Samsul; Anggraini, Dewi; Salam, Nur
Jambura Journal of Mathematics Vol 7, No 2: August 2025
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v7i2.33292

Abstract

HbA1c is widely recognized as a key clinical indicator for monitoring and controlling diabetes, as it reflects average blood glucose levels over the preceding 2–3 months and is closely linked to the risk of complications. This study compares linear and quadratic truncated spline regression models for predicting HbA1c levels in patients with diabetes mellitus. The analysis used retrospective medical record data from a hospital in Makassar, Indonesia, collected in 2023. Fasting blood glucose and LDL cholesterol were included as predictors, with HbA1c as the response variable. Truncated spline regression was applied to capture nonlinear associations between predictors and HbA1c, and the comparison focused on linear versus quadratic specifications. The selection of the best model was based on the minimum GCV value. The model selection process indicated that the best specification was the linear truncated spline regression with two knot points. For FBG, the optimal knots were located at 159 mg/dL and 368 mg/dL, yielding the lowest GCV value of 3.5798. For LDL cholesterol, the best fit was achieved with knots at 183 mg/dL and 191 mg/dL, resulting in a GCV value of 4.3325. The predictive performance of this model was further supported by an R² value of 0.3861, indicating that the linear spline with two knots provides a better fit compared with the quadratic spline alternative. The spline approach showed a better fit based on GCV in depicting the changes in the influence of predictors on HbA1c, suggesting its potential as a more accurate predictive model for clinical and epidemiological purposes.
Co-Authors Andreza Andreza Anggraini, Dewi Anna Islamiyati Dewi Anggraini Dewi Rahma Ente Dewi Sri Susanti Hedi Kuswanto Herdiani, Erna Tri Nur Salam Raupong, Raupong Sri Astuti Thamrin