cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota makassar,
Sulawesi selatan
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 496 Documents
 35   36   37   38   39 
Pemodelan Pemodelan Regresi Nonparametrik Berdasarkan Estimator Spline Truncated pada Data Simulasi Ghony N Nurhuda; Wasono Wasono; Darnah Andi Nohe
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.v19i1.21534

Abstract

Regression analysis is one of the statistical analysis used to estimate the pattern of the relationship between predictor variables and response variables . In general, the approach to estimating the regression function is the parametric regression, the nonparametric regression and the semiparametric regression. The approach with parametric regression is used if the shape of the regression curve is assumed to follow a certain pattern such as linear, quadratic, cubic and so on, but in fact there is an unknown pattern of relationship between predictor variables and response variables, so nonparametric regression is used. Then the combination of parametric and nonparametric regression is semiparametric regression. One of the well-known nonparametric regression estimators is the spline truncated. This study was conducted by simulating the relationship pattern of the response variable and the predictor variable that not have specific pattern by following a trigonometric function that formed a regression curve with a standard deviation of 0,05 and 0,25 which formed a different distribution of data, then will be approached with parametric regression (linear, quadratic, cubic) and nonparametric regression (spline truncated linear). Based on the coefficient of determination of each standard deviation, it will shows that the nonparametric regression approach has high flexibility so that it is able to adjust the form of regression curve estimation by itself
Some new Mathematical Properties for Kumaraswamy Fréchet distribution Bassant Waheed; Salah M. Mohamed
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.v19i1.21547

Abstract

In this research, some mathematical properties for Kumaraswamy Fréchet distribution was presented, include entropy, the Shannon entropy, probability weighted moments, moments of residual life and mean of residual life. the properties were concluded for the Kumaraswamy Fréchet distribution using the probability density function (pdf) and cumulative distribution function according to linear representations.
Peramalan Jumlah Kunjungan Wisatawan Mancanegara ke Indonesia Menggunakan Analisis Intervensi Fungsi Step Adelia Ramadhani; Sri Wahyuningsih; Meiliyani Siringoringo
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.v19i1.21607

Abstract

   Intervention analysis is a method for processing time series data that can be used to explain the effect of an intervention that is influenced by external and internal factors. One application of this method is the data on the number of foreign tourist visits. Since the emergence of COVID-19 in Indonesia, especially in March 2020, Indonesia has begun to implement a lockdown policy and restrict foreign tourists from entering Indonesia. Lockdown policy caused the number of foreign tourist arrivals to decreased drastically. The purpose of this study was obtained a model and forecast results for the number of foreign tourist arrivals for the period November 2021 to November 2022 used a step function intervention analysis. The results of the analysis was shown that the ARIMA intervention model (0,1,1) with a step function with an intervention orde of b=0, s=0, and r=0 was the best model. The results of forecasting the number of foreign tourist visits to Indonesia will increase slowly from November 2021 to November 2022 with a MAPE value 9.91%.
Identification of Factors that Influence Stunting Cases in South Sulawesi using Geographically Weighted Regression Modeling Siswanto Siswanto; Mirna Mirna; Muhammad Yusran; Ummul Auliyah Syam; Alya Safira Irtiqa Miolo
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.v19i1.21617

Abstract

In Indonesia, nearly seven million children under five are stunted and throughout the world, Indonesia is the country with the fifth-highest stunting prevalence. South Sulawesi ranks fourth with a high stunting potential in Indonesia. Stunting is caused by multi-dimensional factors and not only due to malnutrition experienced by pregnant women and children under five. In more detail, several factors that cause stunting are the effects of poor care, the lack of household/family access to nutritious food, and the lack of access to clean water and sanitation. In addition to maternal characteristics and parenting, the problem of stunting is also influenced by environmental factors and geographical conditions (population density, climatic conditions, and inadequate sanitation) so the spatial analysis is important to do in overcoming this problem. In spatial data, often observations at a location (space) depend on observations at other locations that are nearby (neighboring). By using Geographically Weighted Regression (GWR) obtained variables that affect the prevalence of stunting in South Sulawesi Province, including the percentage of babies receiving vitamin A intake, the percentage of babies receiving exclusive breastfeeding, the percentage of babies receiving health care, the percentage of malnourished children under five, the percentage short toddlers, the percentage of infants receiving DPT-HB-Hib, Measles and BCG immunizations.  for the GWR model is 81.32% and based on variables that are significant to the prevalence of stunting in South Sulawesi Province, three clusters are formed.
Pengukuran dan Profiling Kerentanan Sosial Terhadap Bencana Alam di Indonesia Tahun 2019 Yuliagnis Transver Wijaya; Ian Tryaldi Halim
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.v19i1.21686

Abstract

Nowadays, natural hazards are often seen from the nature perspective only. However, it is necessary to know not only about the hazards, but also the community resilience to prepare for, respond to, and recover from disasters based on the social characteristics which are called social vulnerability. This study provides the identification of social vulnerability to natural hazards condition and characterization of the dominant factors at the district level in Indonesia using secondary data. The principal component analysis (PCA) is used to reduce 13 district-level variables into 4 components that represents the driving factors of social vulnerability. The results of PCA are used to quantify the social vulnerability level of the districts in Indonesia using social vulnerability index (SoVI), followed by the deeper exploration of social vulnerability problem using K-Means Clustering. The SoVI and cluster results were mapped by using QGIS to identify the social vulnerability at districts level. The research shows that most districts in Indonesia are at a low-level vulnerability. The districts with low vulnerability are spread in the Sumatera and Kalimantan area. However, there are 43 Districts in Eastern Indonesia are in a high-level vulnerability. These districts also suffer many problems, such low sosioeconomic status. The results of this study support not only the previous social vulnerability studies but also the government as the policymakers by setting priority regions and allocating the policies according to main social vulnerability problem of each district, especially in the most vulnerable regions.
Bahasa Indonesia Bahasa Inggris: Bahasa Indonesia Elok Pratiwi; Henny Pramoedyo; Suci Astutik; Fahimah Fauwziyah
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.v19i1.21757

Abstract

Discrete data on the response variable can be analyzed using poisson regression. The assumption of equidispersion in poisson regression must be fulfilled, but in practice there are many problems of overdispersion. The negative binomial regression model is used to overcome the problem of overdispersion, but this model is global while in some cases each location has different characteristics. Therefore, a method that considers the effects of spatial heterogeneity is needed. If the response variable is discrete data that is overdispersed and includes spatial effects, a model called Geographically Weighted Negative Binomial Regression (GWNBR) is developed. The GWNBR method can be applied in the health sector, such as in stunting. The prevalence of stunting in Malang Regency is still quite high, there is 25.7%. By conducting the GWNBR test, 385 models were obtained, one of them is Tulungrejo Village with factors influencing the incidence of stunting, namely access to permanent healthy latrines, access to posyandu, exclusive breastfeeding, population density and community empowerment. From three weights used, namely the Adaptive Gaussian Kernel, Adaptive Bisquare Kernel and Adaptive Tricube Kernel, the best model was obtained from the Adaptive Bisquare Kernel weighting with the smallest AIC is -211.3763.
Woven generalized fusion frame in Hilbert $C^{\ast}-$module Fakhr-dine Nhari; Mohamed Rossafi
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.v19i1.21791

Abstract

The notion of weaving was recently proposed to simulate a question in distributed signal processing and wireless sensor networks.In this paper we introduced the notion of a woven $g-$fusion frame in Hilbert $C^{\ast}-$modules, also we gives some properties. Finallly we study perturbation of weaving $g-$fusion frames.
An analog of Hardy’s theorem for the second Hankel-Clifford transformation Mohamed El H amma; Radouan Daher; Hasnaa Lahmadi
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.v19i1.21845

Abstract

In this paper, we generalize theorem of Hardy for the second Hankel-Clifford transform .
Forecasting Inflation In Indonesia Using The Modified Fuzzy Time Series Cheng Indi Ria Al Kadry; Jusmawati Massalesse; Muh. Nur
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.v19i1.21868

Abstract

Inflation is one of the most important indicators to analyze a country’s economy. Therefore, it is necessary to forecast the inflation rate. Forecasting can be done by various methods, one of which is Fuzzy Time Series Cheng. In this study, several modifications were made to the method used. The purpose of this study is to forecast using the Modified Fuzzy Time Series (FTS) Cheng method and determine the accuracy of the forecasting results obtained. The results of this study indicate that the Modified FTS Cheng method can be used in forecasting, either by determining the interval average-based or using the Sturges equation. Based on the results of the calculation of forecasting accuracy using Mean Absolute Percentage Error (MAPE), the accuracy for Modified FTS Cheng by determining the average-based interval for forecasting based on the current state and next state is 11.58% and 5.78%, respectively. Furthermore, the Modified FTS Cheng by determining the interval using the Sturges equation resulted in a MAPE value of 9.61% and a FTS Cheng of 7.54%. The MAPE value of each method is less than 10%, which means that the method has a very good performance, except for Modified FTS Cheng by determining the average-based interval for forecasting based on current state has good performance with MAPE values ​​between 10 % and 20%.  
Geographically Weighted Poisson Regression Model with Adaptive Bisquare Weighting Function (Case study: data on number of leprosy cases in Indonesia 2020) Ineu Sintia; Suyitno Suyitno; Memi Nor Hayati
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.v19i1.21879

Abstract

Abstract Geographically Weighted Poisson Regression (GWPR) is a Poisson regression model which is applied on spatial data. The parameter estimation of GWPR is done in each observation location through spatial weighting. This study aims to determine the GWPR model of the number of leprosy cases in each province of Indonesia 2020 and to find the influencing factors. The research uses secondary data collected from Indonesian Ministry of Health and Central Statistics Agency. The spatial weighting is calculated by using the adaptive bisquare function, while the optimum bandwidth is determined by using Generalized Cross-Validation criteria (GCV). The parameter estimation of GWPR uses Maximum Likelihood Estimation (MLE) method. The result of research show that the closed form of Maximum Likelihood (ML) estimator can not be found analytically and that the approximation of ML estimator is found by using Newton-Raphson iterative method. Based on the parameter significance test of the GWPR model, the factors that influenced the number of leprosy cases locally are the percentage of households that have access to proper sanitation, population density, the percentage of people who experience health complaints and outpatient, the number of health workers, the percentage of poor people, the percentage of districts/cities that carry out healthy living community movement (GERMAS) and the percentage of habitable houses. While the factors that globally affected the number of leprosy cases are  the percentage of households that have access to proper sanitation, population density, the percentage of people who experience health complaints and outpatient, the number of health workers, the percentage of poor people, the percentage of districts/cities that carry out GERMAS.  

Page 37 of 50 | Total Record : 496

 35   36   37   38   39 

Filter by Year

2005 2025


Reset
Filter By Issues
All Issue Vol. 21 No. 2 (2025): JANUARY 2025 Vol. 21 No. 1 (2024): SEPTEMBER 2024 Vol. 20 No. 3 (2024): May 2024 Vol. 20 No. 2 (2024): JANUARY 2024 Vol. 20 No. 1 (2023): SEPTEMBER, 2023 Vol. 19 No. 3 (2023): MAY, 2023 Vol. 19 No. 2 (2023): JANUARY 2023 Vol. 19 No. 1 (2022): SEPTEMBER, 2022 Vol. 18 No. 3 (2022): MAY, 2022 Vol. 18 No. 2 (2022): JANUARY 2022 Vol. 18 No. 1 (2021): September 2021 Vol. 17 No. 3 (2021): May, 2021 Vol. 17 No. 2 (2021): JANUARY 2021 Vol. 17 No. 1 (2020): JMSK, SEPTEMBER, 2020 Vol. 16 No. 3 (2020): JMSK, MAY, 2020 Vol. 16 No. 2 (2020): JMSK, JANUARY, 2020 Vol. 15 No. 2 (2019): JMSK Vol. 15, No. 2, January 2019 Vol. 16 No. 1 (2019): JMSK, July, 2019 Vol. 15 No. 1 (2018): July 2018 Vol. 14 No. 2 (2018): January 2018 Vol. 14 No. 1 (2017): July 2017 Vol. 13 No. 2 (2017): January 2017 Vol. 13 No. 1: July 2016 Vol. 12 No. 2: January 2016 Vol. 12 No. 1: July 2015 Vol. 11 No. 2: January 2015 Vol. 11 No. 1: July 2014 Vol. 10 No. 2: January 2014 Vol. 10 No. 1: July 2013 Vol. 9 No. 2: January 2013 Vol. 9 No. 1: July 2012 Vol. 8 No. 2: January 2012 Vol. 8 No. 1: July 2011 Vol. 7 No. 2: January 2011 Vol. 7 No. 1: July 2010 Vol. 6 No. 2: January 2010 Vol. 6 No. 1 (2009): July 2009 Vol. 5 No. 2: January 2009 Vol. 5 No. 1: July 2008 Vol. 4 No. 2: January 2008 Vol. 4 No. 1: July 2007 Vol. 3 No. 2: January 2007 Vol. 3 No. 1: July 2006 Vol. 2 No. 2: January 2006 Vol. 2 No. 1: July 2005 More Issue