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ESTIMASI: Journal of Statistics and Its Application
Published by Universitas Hasanuddin
ISSN : 2721379X     EISSN : 27213803     DOI : http://dx.doi.org/10.20956/ejsa
Core Subject : Education,
Mathematics
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.
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 107 Documents
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Implementasi Algoritma Hierarchical Clustering dan Non-Hierarchical Clustering untuk Pengelompokkan Pengguna Media Sosial Zulkifli Alamtaha; Ismail Djakaria; Nisky Imansyah Yahya
ESTIMASI: Journal of Statistics and Its Application Vol. 4, No. 1, Januari, 2023 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.vi.24830

Abstract

Social media is a means to interact with other people through sentences, pictures and videos online. Excessive use of social media has a negative impact on mental health. The grouping process in this study was carried out to see the level of social media use in Bone Bolango Regency. Before grouping, data pre-processing is carried out and the optimal number of clusters is determined using the Silhoutte index. The optimal cluster results obtained are two clusters for all methods. After that, grouping is done using Hierarchical Clustering and Non-Hierarchical Clustering Algorithms. The Hierarchical Clustering algorithm consists of two methods, namely the single linkage method and the complete linkage method. The Non-Hierarchical Clustering Algorithm consists of two methods, namely the K-Means and K-Medoids methods. The next step is to determine the best method using the Davies-Bouldin Index (DBI). The smaller the DBI value, the better the method used. The smallest DBI value is obtained in the complete linkage method. The grouping results for cluster 1 consisted of 70 respondents and cluster 2 consisted of 80 respondents.
Pengelompokkan Kabupaten/Kota Provinsi Jawa Timur Berdasarkan Indikator Kesejahteraan Rakyat Menggunakan Metode Elbow dan Algoritma K-Prototype Mohamad Rivaldi Koni; Ismail Djakaria; Nisky Imansyah Yahya
ESTIMASI: Journal of Statistics and Its Application Vol. 4, No. 1, Januari, 2023 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.vi.24837

Abstract

People's welfare is the goal of the State of Indonesia which is contained in the official state document, namely the opening of the 1945 Constitution paragraph IV, this can be interpreted to enjoy an affluent life, free from poverty and is a human right for every citizen in Indonesia. The grouping process is carried out to see the level of people's welfare for each Regency/City in East Java. In this study, before grouping, the number of clusters was selected using the Elbow method. After that do the grouping with the K-Prototype Algorithm. Furthermore, using Kruskal Wallis and Chi-Square, the test was carried out to determine the variables that influence grouping. The results of the study obtained the 3 best clusters using the Elbow method, grouping with the K-Prototype Algorithm where cluster 1 consisted of 4 Regencies/Cities, Cluster 2 consisted of 18 Regencies/Cities and cluster 3 consisted of 16 Regencies/Cities. Furthermore, the results of Kruskal Wallis and Chi-Square get 4 influential variables in the grouping, the 4 variables are the Number of Poor Population, Expenditures Per Capita, Open Unemployment Rate and Sources of Water for Drinking.
Pemodelan Topik pada Judul Berita Online Detikcom Menggunakan Latent Dirichlet Allocation Yayang Matira; Junaidi; Iman Setiawan
ESTIMASI: Journal of Statistics and Its Application Vol. 4, No. 1, Januari, 2023 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.vi.24843

Abstract

Detikcom is a very popular news portal today. The news on the portal continues to grow time to time, causing the existing news data to pile up. As a result, this is necessary to utilize this large amount of data. One of the ways that can be used is to extract topics from news text data through topic modeling using the Latent dirichlet allocation (LDA) method. This method is very popular because it can perform analysis on very large documents. This research aims to find certain patterns in a document by generating several different topics so that it does not specifically divide documents into a particular topic. This research has three topics obtained, with a coherence score is 0,7586. The first topic discusses conflicts and crises within a country, the second topic discusses issues related to humanitarian, and the third topic discusses the issues of corruption committed by state officials.
Pemodelan Tindak Pidana Kriminalitas di Kota Tangerang Menggunakan Metode Regresi Lasso Diah Restu Ningsih; Putroue Keumala Intan; Dian Yuliati
ESTIMASI: Journal of Statistics and Its Application Vol. 4, No. 1, Januari, 2023 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.vi.24853

Abstract

Criminal acts are one indicator of social welfare for a sense of security. The higher the reporting of criminal cases by the public, it indicates that the level of security in the area is getting worse. Crime acts in Tangerang City can be influenced by several factors, namely the poverty factor, the population factor and the population growth rate factor. If the rate of population growth experiences rapid growth, the population will increase and it is undeniable that poverty will increase in the city of Tangerang. This can trigger criminal acts to meet unsatisfied needs. The purpose of this study is to determine the variables that influence criminal acts in Tangerang City and to overcome the variables that occur multicollinearity. It can be concluded that all variables influence crime and the LASSO (Least Absolute Shrinkage And Selection Operator) regression can simplify the model and indirectly overcome the problem of multicollinearity in this study. So that the government can make more efforts to overcome the population and poverty problems that occur and the police to increase security in the City of Tangerang in order to create even better security and minimize crime.
Model Regresi Robust dengan Metode Estimasi M, Estimasi S dan Estimasi MM untuk Produksi Beras di Nusa Tenggara Timur Katarina K. Gasul; Astri Atti; Maria A. Kleden
ESTIMASI: Journal of Statistics and Its Application Vol. 4, No. 1, Januari, 2023 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.vi.24879

Abstract

In the regression analysis, the amount of rice production that far exceeds the general production can be categorized as outlier data. The existence of outliers causes the use of the least squares method to estimate parameters to be deemed inappropriate. To deal with outlier data, it is necessary to use methods that are robust or resistant to outlier data. Robust is defined as insensitivity or rigidity to outlier data. The purpose of this study is to obtain a robust regression model using the M estimation, S estimation and MM estimation methods and determine the factors that have a significant effect on rice production in East Nusa Tenggara Province. The model using the S estimation method is the best model, namely y = 3,895.023 + 1.870 X1 - 60.926 X5 and the factors that have a significant effect on rice production are harvested area and air temperature.
Pemodelan Regresi Binomial Negatif Bivariat pada Data Jumlah Kematian Ibu dan Bayi di Provinsi Sulawesi Selatan Tahun 2020 Nurhidaya L; Erna Tri Herdiani; Georgina Maria Tinungki
ESTIMASI: Journal of Statistics and Its Application Vol. 4, No. 1, Januari, 2023 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.vi.25082

Abstract

In general, negative binomial regression is used for univariate discrete data that is overdispersive and follows the Poisson distribution. In the real world, a case is often influenced by two discrete variables that are correlated with each other. Therefore, in this paper we will examine the regression that is influenced by two independent variables, has overdispersion properties and follows a bivariate Poisson distribution. This regression is called bivariate negative binomial regression with model parameters estimated using the Maximum Likelihood Estimation (MLE) method and Newton Raphson iterations. The formation of this model is based on the Famoye method, while in general it uses the Cheon method. Furthermore, the results of this study were applied to data on the number of maternal and infant deaths in South Sulawesi Province in 2020. The results obtained were the number of puskesmas that had a significant effect on the number of maternal deaths and the proportion of handling obstetric complications, the proportion of pregnant women implementing the K4 program, the proportion of deliveries in facilities health services, the proportion of postpartum mothers implementing the KF2 program and the number of puskesmas have a significant effect on the number of infant deaths.
Pemodelan Mixed Geographically Weighted Regression yang Mengandung Multikolinearitas dengan Regresi Ridge Suritman; Raupong; Anisa Kalondeng
ESTIMASI: Journal of Statistics and Its Application Vol. 4, No. 1, Januari, 2023 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.vi.25426

Abstract

In the Mixed Geographically Weighted Regression (MGWR) model, some variables are local and some are global. In MGWR modeling, it is often found that the data have multicollinearity. To overcome this problem, MGWR models with ridge regression are used. The MGWR model can be applied to poverty cases because it can experience spatial heterogeneity due to differences in geographical, cultural, and economic policies that vary in each region. In this study, the estimation of MGWR model parameters with ridge regression is then applied to data on the poor population of South Sulawesi in 2016. Data on the poor population of South Sulawesi experience multicollinearity, so it is solved using the MGWR model with ridge regression. Variables that have a significant effect globally are x3 and x6. while the variables that have a significant local effect are x2, x4, x5, x7, x8, x9 and x10. The AIC value of the MGWR model with ridge regression of 63.64473 is smaller than the MGWR model, meaning that the addition of ridge regression to the MGWR model makes the model better at overcoming multicollinearity problems.
Pemodelan Geographically Weighted Logistic Regression dengan Metode Ridge Reski Amalah; Andi Kresna Jaya; Nasrah Sirajang
ESTIMASI: Journal of Statistics and Its Application Vol. 4, No. 2, Juli, 2023 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v4i2.12250

Abstract

One of the goals of national development is to reduce poverty. Poverty is included in the phenomenon of spatial heterogeneity because it can be shown by the varying economic conditions in each region. The statistical modeling method developed for data analysis takes into account regional factors namely Geographical Weighted Logistic Regression (GWLR). The parameter estimator of the GWLR semiparametric model used in this study was obtained using the Maximum Likelihood Estimation method. In GWLR, the assumption that must be fulfilled is the absence of multicollinearity. One method for dealing with multicollinearity is ridge regression involving the addition of a constant bias . The results obtained were the MSE value of the parameter estimator with the ridge method (707.77) smaller than the GWLR model before using the ridge (715.88). This shows that the ridge method is more effective if there are multicollinearity problems.
Pemodelan Regresi Logistik Ordinal dengan Dispersi Efek Lokasi Ainun Utari Budistiharah; Anna Islamiyati; Sri Astuti Thamrin
ESTIMASI: Journal of Statistics and Its Application Vol. 4, No. 2, Juli, 2023 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v4i2.12355

Abstract

Logistic regression ordinal is a regression model that can explain the relationship between predictor variables in the form of categorical data or continuous data with response variable is more than two categories with a scale of measurement that is level or sequence. In ordinal logistic regression, the frequency of occurrence in each response category is often very different, so it will affect the model's accuracy. Therefore, this study will model ordinal logistic regression with a dispersion of location effects, then applied to the nutritional status data of toddler in 2019 at the Pekkae Puskesmas, Barru Regency. The results obtained show that the ordinal logistic regression model with the dispersion of location effects is better than the usual ordinal logistic regression model for predicting the nutritional status data for toddlers in 2019 at Pekkae Puskesmas, Barru Regency based on deviance values. The factors that influence the nutritional status of toddler based on TB/U are gender, age, and height.
Performa Model Statistical Downscaling dengan Peubah Dummy Berdasarkan K-Means dan Average Linkage Fitri Annisa; Raupong Raupong; Sitti Sahriman
ESTIMASI: Journal of Statistics and Its Application Vol. 4, No. 2, Juli, 2023 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v4i2.12658

Abstract

Climate change that occurs is often used to predict future climate conditions. For future climate predictions it is only possible to use climate models. One of the climate models used to predict climate conditions is the Global Circulation Models (GCM). GCM represents global climatic conditions but not on a regional or local scale. The approach that has been widely used to bridge the difference in scale is statistical downscaling. Large-scale GCM data allows for multicollinearity. estimation liu regression and principal component regression is used to solve the multicollinearity problem. In addition, dummy variables based on k-means and average linkage are used in the model to overcome the heterogeneous variance of residue. There are 4 dummy variables in the cluster technique. In this paper, Liu k-means regression model parameter estimation method is the best model.

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