ESTIMASI: Journal of Statistics and Its Application
ESTIMASI: Journal of Statistics and Its Application, is a journal published by the Department of Statistics, Faculty of Mathematics and Natural Sciences, Hasanuddin University. ESTIMASI is a peer – reviewed journal with the online submission system for the dissemination of statistics and its application. The material can be sourced from the results of research, theoretical, computational development and all fields of science development that are in one group.
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107 Documents
<![CDATA[Implementasi Algoritma Centroid Linkage dan K-Medoids dalam Mengelompokkan Kabupaten/Kota di Sulawesi Selatan Berdasarkan Indikator Pendidikan ]]>
<![CDATA[Raja, Nur Alfianingsih]]>;
<![CDATA[Tinungki, Georgina Maria]]>;
<![CDATA[Sirajang, Nasrah]]>
<![CDATA[ ESTIMASI: Journal of Statistics and Its Application Vol. 5, No. 1, Januari, 2024 : Estimasi ]]>
Publisher : <![CDATA[Hasanuddin University ]]>
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DOI: 10.20956/ejsa.v5i1.13605
<![CDATA[Cluster analysis is a multivariate analysis technique that aims to cluster the observational data or variables into clusters in such a way that each cluster is homogeneous according to the factors used for clustering. This study used the Centroid linkage algorithm that was useful for forming groups based on the distance between centroids and the K-Medoids algorithm that was based on the use of the most centered object (medoid) to group districts/cities and obtained comparison results based on the education indicator data in South Sulawesi. The implementation of the Centroid Linkage Algorithm and K-Medoids on the education indicator data in South Sulawesi in 2018, showed that the grouping of districts/cities in South Sulawesi produced 2 clusters with cluster 1 of 21 districts/cities, and cluster 2 of 3. To determine the best method, it was seen from the value of the Standard Deviation ratio in the cluster 〖(S〗_W) and Standard Deviation between Clusters 〖(S〗_B) showed the same standard deviation ratio (S) in the Centroid Linkage algorithm and K-Medoids that was equal to 104,967.]]>
<![CDATA[Estimasi Parameter Model Regresi Logistik Biner dengan Conditional Maximum Likelihood Estimation pada Data Panel ]]>
<![CDATA[Fitri, Fitri]]>;
<![CDATA[Islamiyati, Anna]]>;
<![CDATA[Kalondeng, Anisa]]>
<![CDATA[ ESTIMASI: Journal of Statistics and Its Application Vol. 5, No. 2, Juli, 2024 : Estimasi ]]>
Publisher : <![CDATA[Hasanuddin University ]]>
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DOI: 10.20956/ejsa.v5i2.13998
<![CDATA[Binary logistic regression models can be used on panel data with categorical responses that experience repeated measurements based on time. This study aims to determine the factors that influence the Human Development Index in South Sulawesi Province in 2015-2019. Data were analyzed through binary logistic regression with fixed effect model approach through Conditional Maximum Likelihood Estimation (CMLE) for panel data. The results of this study indicate that the variables that have a significant effect are life expectancy (X1), school length expectancy (X2) and the average length of schooling (X3). Obtained the probability value of districts/cities that have a medium low and medium high human development index with a classification accuracy of 56.25%.]]>
<![CDATA[Mengatasi Overdispersi Menggunakan Regresi Binomial Negatif dengan Penaksir Maksimum Likelihood pada Kasus Demam Berdarah di Kota Makassar ]]>
<![CDATA[Fadil, Muhammad]]>;
<![CDATA[Raupong, Raupong]]>;
<![CDATA[Ilyas, Nirwan]]>
<![CDATA[ ESTIMASI: Journal of Statistics and Its Application Vol. 5, No. 1, Januari, 2024 : Estimasi ]]>
Publisher : <![CDATA[Hasanuddin University ]]>
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DOI: 10.20956/ejsa.v5i1.14552
<![CDATA[The basic assumption in Poisson regression is that the mean value is the same as the variance value, which is called equidispersion. However, in some cases, this assumption is not met. A variance value that is greater than the average is called overdispersion and is called underdispersion if the variance value is smaller than the average value. So the Poisson regression model is no longer suitable for modeling this type of data because it will produce biased parameter estimates, therefore a negative binomial regression model is used. The research results show that estimating the parameters of the negative binomial regression model uses the maximum likelihood estimation method and then continues with the Newton-Raphson iteration method. The results obtained show that the negative binomial regression model overcomes the overdispersion that occurs in data on the number of dengue fever cases in Makassar City with the model and an AIC value of 236.06647. The negative binomial regression model produces many models and then the best model with the smallest AIC criteria is selected.]]>
<![CDATA[Peta Kendali p Berdasarkan Metode Peningkatan Transformasi Akar Kuadrat ]]>
<![CDATA[Rasyid, Riska]]>;
<![CDATA[Herdiani, Erna Tri]]>;
<![CDATA[Sunusi, Nurtiti]]>
<![CDATA[ ESTIMASI: Journal of Statistics and Its Application Vol. 5, No. 1, Januari, 2024 : Estimasi ]]>
Publisher : <![CDATA[Hasanuddin University ]]>
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DOI: 10.20956/ejsa.v5i1.18487
<![CDATA[When the proportion of nonconformities is small, the effectiveness of the control chart performance becomes inadequate because it has a skewness that causes asymmetryc. Therefore, the Improved Square Root Transformation (ISRT) method is used to construct the attribute control chart to increase the accuracy of the chart control limit which is called the ISRT- control chart. In this study, the effectiveness of the ISRT- control chart perfomance is compared with the control chart after being applied to the data on the number of defects in the newspaper production process at PT. Radar Sulteng Membangun. The results showed that the production process at PT. Radar Sulteng Membangun was not in a statistically controlled and the ARL value obtained on the ISRT- control chart is much smaller than the ARL value for the control chart, so that the ISRT- chart is more effective and sensitive to detecting changes in the production process which produces in a small proportion of nonconformities.]]>
<![CDATA[Text Mining: Absolute Advantage Research at Scopus ]]>
<![CDATA[Mubarak, Fadhlul]]>;
<![CDATA[Aslanargun, Atilla]]>;
<![CDATA[Sundara, Vinny Yuliani]]>;
<![CDATA[Nurniswah, Nurniswah]]>
<![CDATA[ ESTIMASI: Journal of Statistics and Its Application Vol. 5, No. 2, Juli, 2024 : Estimasi ]]>
Publisher : <![CDATA[Hasanuddin University ]]>
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DOI: 10.20956/ejsa.v5i2.21896
<![CDATA[This study aims to collect scopus indexed articles with the keyword absolute advantage in 2020, 2021, and 2022 (until July 15, 2022). In addition, we analyzed the text mining of several abstracts from these articles using the R software. we used 75 articles from top 3 journals that have most publications based on the keyword including the Journal of Cleaner Production, the Journal of Chemical Engineering, and the Journal of Applied Soft Computing. Based on data mining analysis, the word-cloud of each abstract automatically appears based on the frequency of each word that appears in the abstract.]]>
<![CDATA[Penerapan Model Regresi Hurdle Binomial Negatif Menggunakan Algoritma Broyden-Fletcher-Goldfarb-Shanno pada Data Jumlah Kematian Bayi di Kota Makassar Tahun 2017 ]]>
<![CDATA[Yusuf, Anisa Haura Salsa Fatih]]>;
<![CDATA[Jaya, Andi Kresna]]>;
<![CDATA[Sahriman, Sitti]]>
<![CDATA[ ESTIMASI: Journal of Statistics and Its Application Vol. 5, No. 1, Januari, 2024 : Estimasi ]]>
Publisher : <![CDATA[Hasanuddin University ]]>
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DOI: 10.20956/ejsa.v5i1.22749
<![CDATA[Poisson regression is a nonlinear regression method used to analyse the relationship between discrete response variables. Equidispersion is the assumption that must be met in the Poisson regression. Furthermore, there are cases in which the equidispersion assumption is invalidated when using the Poisson regression model to analyze data. One such case is overdispersion, which occurs when there is an excess of zero. As a result, the Negative Hurdle Binomial (HBN) regression is implemented to solve the overdispersion issue. Maximum Likelihood Estimation (MLE) with the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm was applied in this study to perform parameter estimation. In addition, the HBN regression model was used to analyze the data on the number of infant mortality cases in Makassar in 2017 with the variables assumed to be significant with infant mortality. The percentage of infants who were exclusively breastfed was the variable that had a significant impact on the outcome of HBN regression on the data on the number of infant mortality that experienced overdispersion.]]>
<![CDATA[Estimasi Parameter Regresi Ridge Robust pada Data Profil Kesehatan Sulawesi Selatan ]]>
<![CDATA[Waibusi, Hendriete Tiur Marowi]]>;
<![CDATA[Tinungki, Georgina Maria]]>;
<![CDATA[Sahriman, Sitti]]>
<![CDATA[ ESTIMASI: Journal of Statistics and Its Application Vol. 5, No. 2, Juli, 2024 : Estimasi ]]>
Publisher : <![CDATA[Hasanuddin University ]]>
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DOI: 10.20956/ejsa.v5i2.25520
<![CDATA[ABSTRACT Multicollinearity is one of the assumption violations in regression analysis. The existence of multicollinearity causes the standard error to increase. Ridge regression is one of the regression analysis approaches that can overcome this problem. Besides multicollinearity, another problem that often occurs is outliers. The existence of outliers causes the data not to be normally distributed. Ridge Robust Least Trimmed Square Regression is a method that can be used to overcome multicollinearity and outlier problems in the data simultaneously in the regression analysis model. The purpose of this study was to obtain the estimation results of the least trimmed square ridge robust regression model on the Health Profile data of South Sulawesi in 2017. From the results and discussion it was found that the least trimmed square ridge robust regression method has an Rsquare value or ?2 which is 88% and an MSE value 1.96, thus indicating that the ridge robust least trimmed square model fits better in dealing with data containing multicollinearity and outliers. Keywords: Robust Ridge Regression, Least Trimmed Square, Multicollinearity, Outlier, Infant Mortality Rate.]]>
<![CDATA[Penerapan Metode Kano, Customer Satisfaction Index dan Quality Function Deployment dalam Menganalisis Kepuasan Mahasiswa Terhadap Penerapan MB-KM ]]>
<![CDATA[Usman, Sri Adiningsi B.]]>;
<![CDATA[Isa, Dewi Rahmawaty]]>;
<![CDATA[Nuha, Agusyarif Rezka]]>
<![CDATA[ ESTIMASI: Journal of Statistics and Its Application Vol. 5, No. 1, Januari, 2024 : Estimasi ]]>
Publisher : <![CDATA[Hasanuddin University ]]>
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DOI: 10.20956/ejsa.v5i1.26740
<![CDATA[The implementation of the MB-KM program in Higher Education has a variety of obstacles based on points of view. Internship is a form of implementing the MB-KM program which involves students as executors of the program. This study aims to analyze student satisfaction with the implementation of MB-KM using the Kano method, Customer Satisfaction Index (CSI) and Quality Function Deployment (QFD). The Kano method aims to categorize variable attributes, the CSI method to calculate the overall level of satisfaction and the QFD to determine priority attribute improvements. Kano's results show that 5 attributes are in quadrant A, 30 attributes are in quadrant O, 5 attributes are in quadrant I and 13 attributes are in quadrant M. The results of the analysis of the Customer Satisfaction Index are 87.57% meaning students are "very satisfied". The final result of the HOQ attribute that is the priority is attribute .]]>
<![CDATA[Modeling the Effects of Climate Change and Socio-Ecomonic Variables on Agricultural Production ]]>
<![CDATA[Taiwo, Abass Ishola]]>;
<![CDATA[Ayo, Femi Emmanual]]>;
<![CDATA[Ogundele, Lukman Adebayo]]>
<![CDATA[ ESTIMASI: Journal of Statistics and Its Application Vol. 5, No. 1, Januari, 2024 : Estimasi ]]>
Publisher : <![CDATA[Hasanuddin University ]]>
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DOI: 10.20956/ejsa.v5i1.26843
<![CDATA[Climate change has serious effects on human life and existence in various forms. This study used Principal Component Analysis (PCA) and Mutiple Regression model (MRM) to determine the effects of meteorological factors and socio-economic factors on agricultural production. PCA showed 95.6% aggregated variation within the variables and the correlation matrix of the principal components was used to reduce the variables to six. MRM was employed for determining linear association within agricultural productions and the reduced factors showed that climate change and socio-economic factors influenced Nigerian agriculture production. The model obtained was validated with respect to coefficient of determination, adjusted coefficient of determination and Durbin Watson statistics values. Overall, this study indicated that climate change and socio-economic factors influenced the level of agriculture productions in Nigeria.]]>
<![CDATA[Perbandingan Metode Naïve Bayes Classifier dengan Metode Random Forest pada Prediksi Rating Review Drama Korea ]]>
<![CDATA[Meisty, Ferisa Dwi Alfia]]>;
<![CDATA[Anggraeni, Dian]]>;
<![CDATA[Fatekurohman, Mohamat]]>
<![CDATA[ ESTIMASI: Journal of Statistics and Its Application Vol. 5, No. 1, Januari, 2024 : Estimasi ]]>
Publisher : <![CDATA[Hasanuddin University ]]>
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DOI: 10.20956/ejsa.v5i1.26942
<![CDATA[Korean dramas have very many fans and are spread in various countries. This study aims to determine whether the korean drama is classified as Bagus, Tidak Bagus, or Cukup Bagus and compares two methods, namely the naïve bayes classifier method and the random forest method in predicting korean drama review ratings. This study shows that the naïve bayes classifier and random forest methods are capable of predicting korean drama review ratings. In the prediction review, the random forest method obtained an accuracy value of 89%, while the naïve bayes classifier method obtained an accuracy value of 86%. In rating predictions, the random forest method obtains an accuracy value of 41%, while the naïve bayes classifier method obtains an accuracy value of 40%. The conclusion of this study is that the random forest method is superior and accurate in predicting Korean drama review ratings.]]>
