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INDONESIA
JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI
Published by Universitas Hasanuddin
ISSN : 18581382     EISSN : 26148811     DOI : -
Core Subject : Education,
Mathematics
Jurnal ini mempublikasikan paper-paper original hasil-hasil penelitian dibidang Matematika, Statistika dan Komputasi Matematika.
Arjuna Subject : -
Articles 421 Documents
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Some new properties of g-frame in Hilbert C*-modules Mohamed Rossafi; Hatim Labrigui
Jurnal Matematika, Statistika dan Komputasi Vol. 18 No. 3 (2022): MAY, 2022
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v18i3.20288

Abstract

The theory of frames which appeared in the last half of the century, has been generalized rapidly and various generalizations of frames in Hilbert spaces and Hilbert $C^{\ast}$-modules. In this paper, we will give some new properties of modular Riesz basis and modular $g$-Riesz basis that present a generalization of the results established in a Hilbert space.
Some properties of K-Operator Frame in Hilbert $C^{\ast}$-modules Roumaissae Eljazzar; Mohamed Rossafi; Mohammed Klilou
Jurnal Matematika, Statistika dan Komputasi Vol. 18 No. 3 (2022): MAY, 2022
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v18i3.20459

Abstract

In this paper, we present some properties of K-operator Frame in Hilbert $C^{\ast}$-modules.Topics that will be discussed include: K-operator Frame and Dual K-operator frame in Hilbert $C^{\ast}$-modules.We will also study K-operator Frame in two Hilbert $C^{\ast}$-modules with different $C^{\ast}$-algebras.
Fixed point theorem for Nonlinear $\theta-\phi-$contraction via $w-$distance Mohamed Rossafi; Abdelkarim Kari
Jurnal Matematika, Statistika dan Komputasi Vol. 19 No. 1 (2022): SEPTEMBER, 2022
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v19i1.20866

Abstract

This paper is aimed to the notion of $\theta-\phi-$contraction defined on a metric space with $w-$distance. Moreover, fixed point theorems are given in this framework. Some illustrative examples are provided to advocate the usability of our results.As an application, we prove the existence and uniqueness of a solution for the nonlinear Fredholm integral equations.
Clustering Regencies/Cities in Kalimantan Island Based on Poverty Indicators using Agglomerative Hierarchical Clustering (AHC) Ludia Ni'matuzzahroh; Andrea Tri Rian Dani; Narita Yuri Adrianingsih
Jurnal Matematika, Statistika dan Komputasi Vol. 19 No. 1 (2022): SEPTEMBER, 2022
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Cluster analysis is a statistical analysis that can group objects of observation into several groups/clusters based on their similarity of characteristics. The grouping into several clusters is based on the information contained in the object under study. A cluster can be said to be good if it has high internal homogeneity and high external heterogeneity. The clustering method used in this study is the agglomerate hierarchical clustering (AHC) method, where the cluster formation algorithm used in this AHC method is average linkage, single linkage, complete linkage, and ward. Cluster analysis using the AHC method will be applied to poverty indicator data for Regencies/Cities in Kalimantan Island, which consists of several variables. This study aims to obtain the optimal results of grouping Regencies/Cities in Kalimantan Island, with the number of clusters that have been determined at the beginning, namely as many as 3 clusters. Based on the results of the analysis using the AHC method, the ward algorithm produces an agglomerate coefficient value of 0.89, where this value is close to 1, which means that the ward algorithm is the best in clustering Regencies/Cities in Kalimantan Island.
The GRDP Per Capita Gap between Provinces in Indonesia and Modeling with Spatial Regression Desy Wasani; Setyorini Indah Purwanti
Jurnal Matematika, Statistika dan Komputasi Vol. 19 No. 1 (2022): SEPTEMBER, 2022
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v19i1.20997

Abstract

Gross Regional Domestic Product (GRDP) is one of the key indicators to determine the economic conditions in an area within a certain period, both based on current prices and constant prices. The GRDP per capita shows the value of GRDP divided by the mid-year population. According to data from Statistic Indonesia (BPS), the distribution of GRDP is concentrated in Java. About 59 percent of Indonesia's economy in 2021 was contributed by Java. The contribution of other islands is not more than 10 percent, except for Sumatra at 21 percent. One of the government's policies to equalize the economy announced in 2019 was the relocation of the nation's capital city from DKI Jakarta to East Kalimantan. This policy has generated polemics in various circles of society regarding priorities, urgency, procedures, and risks. The economic inequality between regions in Indonesia involves various regions or provinces with different characteristics. Spatial regression is a model that accommodates spatial effects because the observation unit is a location. The aim of this study is to determine the level of economic disparities between provinces in Indonesia, resulting in the decision to relocate the nation's capital city. In addition, the aim is to determine the significance of several factors that affect GRDP per capita as a measure of regional prosperity, namely population density, number of workers, and the Human Development Index.
PENERAPAN METODE K – HARMONIC MEANS DALAM PENGELOMPOKAN KABUPATEN/KOTA (Studi Kasus: Kemiskinan di Pulau Kalimantan Tahun 2020) Dwi Indra Yunistya; Rito Goejantoro; Fidia Deny Tisna Amijaya
Jurnal Matematika, Statistika dan Komputasi Vol. 19 No. 1 (2022): SEPTEMBER, 2022
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Poverty is one of the problems that faced by every country in the world, especially in developing countries, one of them is Indonesia. Poverty alleviation that is currently planned is no longer uniform, but it is necessary to pay attention to the condition of each dimension causing poverty in an area, so it is necessary to group districts/cities on the Kalimantan Island based on poverty. Cluster analysis is classifying the data (objects) only based on the information discovered in the data that describes the objects and the relations between them. The method used in this research is K-Harmonic Means method. K-Harmonic Means is a non-hierarchical clustering algorithm that uses the average harmonic distance from each data point to the cluster center. This study aims to classify the District/City in Kalimantan Island based on poverty indicators and obtain the silhouette coefficient value from the optimal cluster analysis. Based on the results of the analysis of the K-Harmonic Means method, the optimal number of clusters is 2 clusters with parameter (p) of 4. Cluster 1 consists of 11 Districts/Cities and Cluster 2 consists of 45 Districts/Cities. Silhouette coefficient value for data validation of District/City clustering results on Kalimantan Island using the K-Harmonic Means method, namely 2 clusters with parameter (p) of 4 is 0.323 which states that the resulting cluster structure in this grouping is a weak structure.  
Shifted Liu-Type Estimator in The Linear Regression Funda Erdugan
Jurnal Matematika, Statistika dan Komputasi Vol. 19 No. 1 (2022): SEPTEMBER, 2022
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v19i1.21136

Abstract

The methods to solve the problem of multicollinearity have an important issue in the linear regression. The Liu-type estimator is one of these methods used to reduce its effect. This estimator is an estimator with two parameters denoted  and . Kurnaz and Akay (2015) [6] introduced a new approach for the Liu-type estimator and called it new Liu-type (NL) estimator. This proposed estimator is based on a continuous function of  rather than two parameters and includes OLS, ridge estimator, Liu estimator, and some estimators with two biasing parameters as special cases. This study aimed to improve the NL estimator by shifting. The performance of the shifted NL estimator is compared to the NL estimator and other estimators depending on the mean squared error (MSE) criterion. The real data example and simulation study reveal that the SNL estimator can be a good selection in the linear regression model.
Pemodelan Penderita Tuberkulosis di Jawa Timur Berdasarkan Pendekatan Geographically Weighted Regression (GWR) Diah Puspita Ningrum; Toha Saifudin; Suliyanto Suliyanto; Nur Chamidah
Jurnal Matematika, Statistika dan Komputasi Vol. 19 No. 1 (2022): SEPTEMBER, 2022
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v19i1.21262

Abstract

Tuberculosis is the 13th trigger of death causes around the world. Even after Covid-19, tuberculosis ranks 2nd as a contagious killer disease. In 2020, Indonesia ranks 2nd out of 8 countries with the highest contributor to tuberculosis sufferers after India. East Java Province is the region with the largest number of tuberculosis cases in order of 8. Tuberculosis cases in East Java in 2020 have decreased, but when viewed from the success rate of treatment of tuberculosis cases per district/city in East Java, it was found that 53% still did not meet the target of 90%. According to (World Health Organization), gender affects the occurrence of tuberculosis disease, where men are more susceptible than women. In finding treatment for all tuberculosis incidents in East Java, the highest patient was male. This study was conducted to model tuberculosis in men in the East Java area. The results of the study prove that the modeling of male tuberculosis in East Java used linear regression and GWR  (Geographically Weighted Regression) obtained the best model was GWR with Fixed Gaussian Kernel weighting, CV value of 5.68, and R2 86.47%. Variables that have a significant effect on male tuberculosis in East Java are BCG immunization for male infants, public places meeting health requirements, youth who smoke tobacco every day, sex ratio, and households with access to proper sanitation facilities.      
Generalized rational $ \alpha_{\ast}- $ contraction in $ C^{\ast}- $algebra-valued b-metric spaces Mohamed Rossafi; Hafida Massit; Abdelkarim Kari
Jurnal Matematika, Statistika dan Komputasi Vol. 19 No. 1 (2022): SEPTEMBER, 2022
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v19i1.21301

Abstract

This present paper extends some common fixed point theorems for generalized rational $ \alpha_{\ast}- $ contraction of multi-valued mappings in the setting of $ C^{\ast}- $algebra-valued b-metric spaces .
Comparison of Variance Covariance and Historical Simulation Methods to Calculate Value At Risk on Banking Stock Portfolio Maria Yus Trinity Irsan; Evelyn Priscilla; Siswanto Siswanto
Jurnal Matematika, Statistika dan Komputasi Vol. 19 No. 1 (2022): SEPTEMBER, 2022
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v19i1.21436

Abstract

In investing, all investors must be faced with risk that must be borne. Therefore, to determine the best strategy in investing, every investor must calculate the risk. One statistical approach that can be used to measure the risk is Value at Risk (VaR). VaR is defined as a tolerable loss with a certain level of confidence. The purpose of this research is to estimate VaR using Variance Covariance and Historical Simulation methods on banking stock portfolio consisting of three stocks for the period 11 September 2020-30 September 2021. Both methods will then be evaluated using backtesting to determine the accuracy of VaR and to obtain the best method. From the research results, if the holding period is 1 day, then the VaR calculation for banking stock portfolio using both methods can be used to estimate the risk at 99% and 95% confidence levels, except for the VaR value using the Variance Covariance method for banking stock portfolio at 95% confidence level. The results show that Variance Covariance method is the best method for 99% confidence level. As for the 95% confidence level, Historical Simulation method is the best method.

Page 36 of 43 | Total Record : 421

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