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Variansi : Journal of Statistics and Its Application on Teaching and Research
Published by Universitas Negeri Makassar
ISSN : -     EISSN : 26847590     DOI : https://doi.org/10.35580/variansiunm
Core Subject : Economy, Health, Science, Education,
Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Mathematics Public Health
VARIANSI: Journal of Statistics and Its application on Teaching and Research memuat tulisan hasil penelitian dan kajian pustaka (reviews) dalam bidang ilmu dasar ataupun terapan dan pembelajaran dari bidang Statistika dan Aplikasinya dalam pembelajaran dan riset berupa hasil penelitian dan kajian pustaka.
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 6 Documents
 1 
Search results for , issue "Vol 3, No 3 (2021)" : 6 Documents clear
PERBANDINGAN METODE MOMEN, MAXIMUM LIKELIHOOD DAN BAYES DALAM MENDUGA PARAMETER DISTRIBUSI PARETO A. Nurul Amalia; Muhammad Arif Tiro; Aswi Aswi
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol 3, No 3 (2021)
Publisher : Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm26374

Abstract

This study examines the estimation of Pareto distribution parameters using three different methods, namely the Moment, Maximum Likelihood, and Bayesian methods. The Pareto distribution is a continuous distribution with parameters k > 0 and α > 0. These two parameters are estimated by using three distinct parameter estimation methods. The goodness of fit measure used in choosing the best estimation method is the Mean Square Error (MSE) value. The smallest MSE is the best method. A simulation study is carried out as well as the case study of the data on the number of Gross National Income (GNI) per capita in Southeast Asian countries in 2019. The estimation and simulation results indicate that the best estimation method in estimating the parameters of the Pareto distribution is the Maximum Likelihood in terms of MSE value.Keywords: Pareto distribution, Moment Method, Maximum Likelihood IMethod, Bayesian Method
Regresi Logistik Backward Elimination pada Risiko Penyebaran Covid-19 di Jawa Timur Wara Pramesti; Windi Utami; Fenny Fitriani
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol 3, No 3 (2021)
Publisher : Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm26132

Abstract

AbstractCorona Virus Disease 2019 or commonly called Covid-19 is a type of virus that can infect the human lungs and can cause fatal diseases such as Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS). East Java Province is one of the provinces in Indonesia that has been exposed to Covid-19 with a high ranking, which is at number 3 in Indonesia (Kompas.com, September 2020), so based on this, of course there are factors that affect the level of risk of spreading the virus. , so we need a model that can be used to determine the factors that are thought to have an effect. The risk of spreading the virus is high, medium and low. The Ordinal Logistics Regression method is one method that can be used to model the factors that are thought to affect the level of risk of the spread of the corona virus in East Java, because ordinal logistic regression has an ordinal-scale response variable according to the level of spread that occurs. The results of the model fit test analysis showed that the logit model was feasible to use. Simultaneous testing of parameter estimates with a value of G2 = 25.64 means that the logit model is simultaneously significant to the response variable. The selection of the backward elimination model shows that the number of Covid-19 deaths and the average household member have a significant effect on the risk of spreading Covid-19 in East Java. The odds ratio for the number of Covid-19 deaths is 1.044. This shows that for every unit increase in the number of Covid-19 deaths, an area with a low or moderate risk status of 1.044 times will become a medium and high risk. The odds ratio value for the average number of households is 0.079, indicating that for every one-unit increase in the average number of households, an area with a low or moderate risk status of 0.079 times will be at medium and high risk. Keywords : Covid-19, Ordinal Regresion Logistic Analysis, Backward Elimination, Odds Ratio
PENGENDALIAN KUALITAS KINERJA LEVEL SIX SIGMA PADA PT INDOFOOD CBP SUKSES MAKMUR TBK MAKASSAR Faradiba Ahmad; Muhammad Arif Tiro; Muhammad Kasim Aidid
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol 3, No 3 (2021)
Publisher : Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm25169

Abstract

Six Sigma is a combination of several statistical quality control methods that focus on reducing variations in the process so that it can suppress production in the form of goods or services to approach zero defects. The six sigma systematic stages are called DMAIC (Define, Measure, Analyze, Improve, and Control) which is a continuous process which, if carried out optimally throughout the system, can increase the productivity of a company. There are four kinds of quality characteristics or CTQ in the production of instant noodles at PT Indofood CBP Sukses Makmur Tbk Makassar. The four CTQs are HH, HP, HHPGK, and HHPG. The CTQ was then implemented using the six sigma method and the DPMO values from October-December 2019 were as follows 4,457.90, 5,404.26, and 4,827.45 and if the DPMO value was converted to the six sigma level it would be 4.11σ, 4.04σ, and 4.08σ. To increase the productivity of related companies to approach zero defects, the application of the six sigma method must be carried out optimally so that the sigma level of instant noodle production can increase.Keywords: Regression, resampling, bootstrap, jackknife
Pendekatan persamaan struktural pada model regresi error spasial (Kasus: PDRB Sulawesi Selatan) Muhammad Kasim Aidid; Zulkifli Rais; Muhammad Fahmuddin S
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol 3, No 3 (2021)
Publisher : Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm26380

Abstract

The spatial autocorrelation model studied in the framework of structural equations is the spatial error regression model. The results of this study are applied to South Sulawesi's Gross Regional Domestic Product (GRDP) data. For parameter estimation using open source software Mx. To implement the spatial error model in SEM, two new sets of weighted spatial variables need to be formed, namely W based on the dependent variable (PW) and ηW based on the independent variable (PW) and ξW based on the independent variable (QW). Since in the case of the latent model, the variables P and Q cannot be observed directly, then ηW and ξW are directly defined by the observation variables (indicators) Y yW and Y xW which are related to each other as Yy and Yx to η and ξ. obtained a model that represents the spatial error in SEM. By using South Sulawesi GRDP data where y represents the per capita GRDP in the Regency/City, x1 and x2 respectively represent the value of the Mining sector and the building sector in the Regency/City. XW1 represents first-order contiguity spatially lagged for trade and XW2 represents first-order contiguity spatially lagged for agriculture. yW denotes spatially lagged first-order contiguity for GRDP. (1−λ)γ0 represents the unit variable coefficient. From the model it can be stated that GRDP (y) is influenced by several sectors in the economy such as mining (x1) and building (x2). In addition, there is a location effect (Spatial Effect) that affects the GRDP in South Sulawesi. Based on the final results obtained, it is known that λ = 0,16 which indicates that there is a dependency on the GRDP data in South Sulawesi in 2008 between one district/city and another district/city based on the spatial correction. Areas that are centers of mining and construction in South Sulawesi are mutually dependent, causing dependence on GRDP data, this can be seen in the positive covariance value between mining lagged, and building lagged, and lagged GRDPKeywords: Effect Spatial, Error Spatial, SEM, GRDP
PERBANDINGAN METODE PCA-SVM DAN SVM UNTUK KLASIFIKASI INDEKS KEPUASAN MASYARAKAT TERHADAP LAYANAN PENDIDIKAN DI KABUPATEN JENEPONTO Nur Ikhwana; Muhammad Nusrang; Sudarmin Sudarmin
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol 3, No 3 (2021)
Publisher : Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm22988

Abstract

Support Vector Machine (SVM) is one of the classification methods used to find the best hyperplane by maximizing the distance between classes. SVM aims to build a model that can predict the given test data. The SVM method can be implemented easily and the testing time is short, but it needs to reduce the computation burden. One way that can be done is to perform feature extraction to get the main characteristics of the data. The method that can be used to extract features is Principal Component Analysis (PCA). PCA is used to reduce the dimensions of data which are generally used in numerical scale data. If the data in the study used categorical data, then the PCA used was Nonlinear PCA. The data used in this study is the Community Satisfaction Survey data in Jeneponto Regency. This study compares the PCA-SVM and SVM methods for the classification of the Jeneponto Regency Community Satisfaction Index. The overall PCA-SVM classification results are better than SVM with 100% accuracy.
Analisis Kruskal-Wallis Terhadap Kemampuan Numerik Siswa Andi Quraisy; Wahyuddin Wahyuddin; Nur Hasni
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol 3, No 3 (2021)
Publisher : Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm29957

Abstract

This study aims to determine the differences in the numerical abilities of students from 4 classes, namely classes A, B, C, and D. The population in this study were students of class VII SMP Muhammadiyah 1 Makassar with a total sample of 57 students. The data collection technique used a numerical ability test instrument, then the data obtained were analyzed using descriptive analysis and Kruskal-Wallis nonparametric analysis. The results of the descriptive analysis obtained that the average value of the numerical abilities of students in grades A, B, C, and D was not much different, namely 76.13; 78.4; 76.57; 77.23. Meanwhile, the results of the Kruskal Wallis analysis showed that there was no significant difference in the grades of A, B, C, and D numerical abilitiesKeywords: Mann-Whitney test, Non-parametric test,  Problem Based Learning Model

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