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Isran K. Hasan
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Department of Statistics, 3rd Floor Faculty of Mathematics and Natural Sciences, Universitas Negeri Gorontalo Jl. Prof. Dr. Ing. B.J Habibie, Tilongkabila Kabupaten Bone Bolango, 96119
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JAMBURA JOURNAL OF PROBABILITY AND STATISTICS
Published by Universitas Negeri Gorontalo
ISSN : -     EISSN : 27227189     DOI : https://doi.org/10.37905/jjps
Core Subject : Science, Social,
Computer Science & IT Decision Sciences, Operations Research & Management Environmental Science Social Sciences
Probability Theory Mathematical Statistics Computational Statistics Stochastic Processes Financial Statistics Bayesian Analysis Survival Analysis Time Series Analysis Neural Network Another field which is related to statistics and the applications Another field which is related to Probability and the application
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 54 Documents
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Model Markov Switching Autoregressive pada Data Covid-19 di Indonesia Setyo Wira Rizki; Shantika Martha; Bartolomius Bartolomius; Rita Apriliyanti
Jambura Journal of Probability and Statistics Vol 5, No 1 (2024): 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.v5i1.19429

Abstract

The Covid-19 pandemic has had a very influential impact on socio-economic conditions in Indonesia. Forecasting the number of Covid-19 cases is needed to support taking preventive action. The method that can be used to determine the number of Covid-19 cases is a forecasting method using the Markov Switching Autoregressive (MSAR) time series data model as an alternative for analyzing structural change data. This research uses Covid-19 confirmation data in Indonesia for the period March 2020-June 2021, with the aim of designing an MSAR model and calculating the magnitude of the transition opportunity in each state in the Covid-19 confirmation data in Indonesia. The MSAR model begins by describing the data and checking the stationarity of the data. After that, Box-Jenkins modeling was carried out to test heteroskedasticity and structural changes. Next, the MSAR model parameters were estimated and the transition matrix was formed. This research shows that the best MSAR model formed is the MS (2)-AR (5) model, with a static transition probability value in state 1 of 0.981330. However, it appears that there is a chance of 0.018670 for the Covid-19 confirmation condition to move to state 2. Testing in the case of state 2 produces a transition chance of 0.980991 in state 2, with a transition chance of Covid-19 confirmation changing to state 1 of 0.019009.
Pemodelan Kadar Hemoglobin pada Pasien Demam Berdarah di Kota Samarinda Menggunakan Regresi Semiparametrik Spline Truncated Andrea Tri Rian Dani; Fachrian Bimantoro Putra; Muhammad Aldani Zen; Sifriyani Sifriyani; Meirinda Fauziyah; Vita Ratnasari; Narita Yuri Adrianingsih
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 .
Penerapan Principal Component Analysis untuk Reduksi Variabel pada Algoritma K-Means Clustering Istina Alya Rosyada; Dina Tri Utari
Jambura Journal of Probability and Statistics Vol 5, No 1 (2024): 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.v5i1.18733

Abstract

K-Means clustering is a widely used clustering algorithm. However, it has the disadvantage that the performance of clustering data decreases if the variables of the processed data are immense. The complex variables problem in K-Means can be overcome by combining the Principal Component Analysis (PCA) variable reduction method. This study uses seven indicator variables for the welfare of the people of West Java Province in 2021 to measure the welfare level of districts/cities. The results of the analysis obtained two principal components based on eigenvalues. Clustering from cluster analysis with the K-Means with variable reduction using PCA formed the three best clusters where the number of members of each cluster consisted of 12, 8, and 7 districts/cities.
Bayes Estimator of Exponential Distribution Parameters of Type II Censored Data with Linear Exponential Loss Function Method Based on Jeffrey Priors Anggara Teguh Previan; Ardi Kurniawan; M. Fariz Fadillah Mardianto; Sediono Sediono
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.v4i2.22549

Abstract

Survival analysis is often used in the application of analyzing the survival of an object such as living things or objects. This analysis is identical to data censoring which is divided into three, namely: type I, II, and III censored data. Type II censored data is data censoring done by determining the number of objects to be analyzed  from the total number of observation objects . Type II censored data is used when the analysis is intended to maximize the results of the analysis. Bayesian Linear Exponential (LINEX) loss function is one method that can be used to estimate parameters in survival analysis by minimizing the expected value of LINEX. The purpose of this study is to determine the Bayesian LINEX loss function parameter estimation on type II censored data using exponential distribution. This method uses the concept of posterior distribution and prior distribution. The prior distribution used is the Jeffrey prior distribution which has objective properties and is based on Fisher information theory The application of the parameter estimation results is carried out on the survival data of lung cancer patients obtained from the North Central Cancer Treatment Group. Based on the results of parameter estimation, it is concluded that the greater the value of the controller  (a) can produce a smaller value of parameter estimation results (θ^) . The results of this study can be used as a reference in conducting survival tests using type II censored exponential distribution data using the LINEX loss function method based on Jeffrey priors.

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