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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Epsilon: Jurnal Matematika Murni dan Terapan Zero : Jurnal Sains, Matematika, dan Terapan Jambura Journal of Mathematics ESTIMASI: Journal of Statistics and Its Application RAGAM: Journal of Statistics and Its Application
Arifin, Samsul
Department of Statistics, Lambung Mangkurat University, Banjarbaru
Agriculture, Biological Sciences & Forestry Humanities Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Physics Public Health Transportation

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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.
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.
MODEL REGRESI COX PROPORTIONAL HAZARD DENGAN PENDEKATAN DISTRIBUSI POISSON PADA LAJU SURVIVAL PASIEN HIV/AIDS Amaludin, Ahmad; Hakim, Raihan Nuur; Arifin, Samsul
RAGAM: Journal of Statistics & Its Application Vol 5, No 1 (2026): RAGAM: Journal of Statistics & Its Application
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/ragam.v5i1.18041

Abstract

Human Immunodeficiency Virus (HIV) and Acquired Immune Deficiency Syndrome (AIDS) represent a global health crisis requiring long-term management. Understanding the factors influencing patient survival duration is crucial for effective clinical management. This study aims to model the survival rate of HIV/AIDS patients and determine dominant risk factors using Cox Proportional Hazard regression. This study utilized secondary data from the ACTG 175 clinical trial involving 2,139 patients. Parameter estimation was performed using the Maximum Partial Likelihood method, validated by the Schoenfeld residuals test. The results indicated that the Proportional Hazard assumption was met for all research variables. Simultaneously, the predictor variables significantly influenced the model. Partial testing identified Didanosine combination therapy (HR=0.715) and a history of drug use (HR=0.720) as protective factors. Conversely, clinical symptoms (HR=1.773), a history of Zidovudine use (HR=1.605), and low Karnofsky scores (HR=1.403) were identified as primary risk factors. Treatment factors and clinical conditions proved to have a more dominant influence on survival rates compared to demographic factors.
MODEL SURVIVAL HIDUP PASIEN KANKER MATA MENGGUNAKAN METODE KAPLAN-MEIER DAN REGRESI COX PROPORTIONAL HAZARD Rahmawan, Deyana Maulidya; Haliza, Delia Nur; Arifin, Samsul; Asianingrum, Al Hujjah
EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN Vol 20, No 1 (2026)
Publisher : Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/epsilon.v20i1.18032

Abstract

Eye cancer is a relatively rare disease, but it can cause permanent vision impairment and even death if not treated properly. Differences in cancer type and patients’ clinical conditions are suspected to play a role in determining survival, so survival analysis is needed to describe survival patterns and identify factors affecting the risk of death. This study aims to analyze the survival of eye cancer patients and identify clinical factors influencing mortality risk using the Kaplan–Meier method and Cox Proportional Hazard regression. The research data were obtained from the Eye Cancer Patient Records dataset on the Kaggle platform, consisting of 5,000 medical records of eye cancer patients. Of these, 350 observations were used for Kaplan–Meier curve visualization so that survival patterns between groups could be more easily interpreted, while the Cox regression analysis was conducted using the prepared research data according to the modeling requirements. The results showed that cancer type was the only factor that significantly affected the risk of death. Patients with intraocular lymphoma had a hazard ratio of 1.54, meaning they had a 1.54 times higher risk of death compared to patients with retinoblastoma. Meanwhile, other variables such as gender, stage at first diagnosis, treatment type, surgery status, radiation therapy, and chemotherapy did not show a significant effect. Overall, these findings indicate that cancer type is the most prominent factor distinguishing survival between the two eye cancer groups analyzed.
Co-Authors Al Hujjah Asianingrum Amaludin, Ahmad Anggraini, Dewi Anna Islamiyati Dewi Anggraini Dewi Sri Susanti Hakim, Raihan Nuur Haliza, Delia Nur Herdiani, Erna Tri Nur Salam Rahmawan, Deyana Maulidya Raupong, Raupong