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INDONESIA
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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Analisis Perubahan Berat Badan Balita dengan Estimator Penalized Spline Kuadratik Muhammad Jayzul Usrah; Anna Islamiyati; Anisa
ESTIMASI: Journal of Statistics and Its Application Vol. 3, No. 2, Juli, 2022 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.vi.11459

Abstract

Nonparametric regression is a regression approach that is used when one of the parametric assumptions are not fulfilled. One of the estimators in nonparametric regression is penalized spline. The growth pattern of toddler that varied each month of observation make the suitable regression approach is nonparametric penalized spline regression because of its high flexibility. This study aims to obtain an estimate of the growth model for toddler in South Sulawesi. The optimal model obtained with a minimum GCV value of 4.87E-05 using two point knots that is 14 and 56 with lamda 100. The estimation results show that there are 3 intervals of change patterns in the growth of toddler in South Sulawesi
Penerapan Metode Stepwise dan Dominance Analysis Pada Regresi Logistik Biner (Studi Kasus: Data Hipertensi Di Indonesia) Muhammad Idman; La Podje Talangko; Sitti Sahriman
ESTIMASI: Journal of Statistics and Its Application Vol. 3, No. 2, Juli, 2022 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.vi.12211

Abstract

Binary logistic regression is a method to describe the relationship between response variable that has two categories and one or more predictor variables. One of methods that can be used to obtain best model in logistic regression is stepwise. Stepwise method is a method that sets  and  as criteria to build model. Dominance analysis is used in this research to determine the importance rank of predictor variables by comparing the coefficient of determination  value before and after the predictor variable entered the model. Binary logistic regression can be used to find the relationship between hypertension and the factor risks. This study aims to obtain best model and to obtain the importace rank each predictor variable of binary logistic regression on data of hypertension in Indonesia. The result of this study shows that best model which is obtained is model with predictor variable of Heart Problems, High Cholesterol, Kidney Disease, Imperfect Vision, Breathlessness, and Nausea/ Vomitting. According to the value of  McFadden, predictor variable of High Cholesterol infests first rank in the importance of predictor variable or gives the greatest contributions in explaining variety of Status of Hypertension than other predictor variables.Binary logistic regression is a method to describe the relationship between response variable that has two categories and one or more predictor variables. One of methods that can be used to obtain best model in logistic regression is stepwise. Stepwise method is a method that sets  and  as criteria to build model. Dominance analysis is used in this research to determine the importance rank of predictor variables by comparing the coefficient of determination  value before and after the predictor variable entered the model. Binary logistic regression can be used to find the relationship between hypertension and the factor risks. This study aims to obtain best model and to obtain the importace rank each predictor variable of binary logistic regression on data of hypertension in Indonesia. The result of this study shows that best model which is obtained is model with predictor variable of Heart Problems, High Cholesterol, Kidney Disease, Imperfect Vision, Breathlessness, and Nausea/ Vomitting. According to the value of  McFadden, predictor variable of High Cholesterol infests first rank in the importance of predictor variable or gives the greatest contributions in explaining variety of Status of Hypertension than other predictor variables.
Eksplorasi Metode Double Exponential Smoothing Pada Peramalan Nilai Tukar USD Terhadap Rupiah Edy Widodo; Bella Destia; Febi Permata Putri; Riski Pratama Ramadhan
ESTIMASI: Journal of Statistics and Its Application Vol. 3, No. 2, Juli, 2022 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.vi.18849

Abstract

Currency exchange rate is the price of one unit in domestic currency against foreign currencies. Exchange rates change over time depending on the supply and demand for foreign exchange relative to the domestic currency. This study aims to predict changes in the USD exchange rate against the Rupiah in 2021 using the DES method. The use of the DES method in this forecasting takes into account that the data to be used has a trend which is characterized by the tendency of the data to move up and down over a long period of time. The DES method can also determine the trend equation for the second most extensive smoothing data through a smoothing process. This forecasting system captures patterns from past data and then uses it to project future data. The results of forecasting the USD exchange rate against the Rupiah from 2021 to 2022 show that the exchange rate ranges from IDR 14,512 to IDR 14,744 with a MAPE value of 1.93%.
Penerapan Algoritma K-Means dan K-Medoids dalam Pengelompokan Provinsi di Indonesia Berdasarkan Indikator Perumahan Rumah Tangga Tahun 2020 Fahriza Rianda
ESTIMASI: Journal of Statistics and Its Application Vol. 3, No. 2, Juli, 2022 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.vi.19100

Abstract

Statistics Indonesia explained that the percentage of households in Indonesia that had access to decent, safe and affordable housing during the 2019-2020 period was still below 60 percent. Of course, this is a big job for the government to be able to achieve the target in the RPJMN 2020-2024, which is up to 70 percent in providing decent, safe, and affordable housing for the community by 2024. This study aims to group provinces in Indonesia based on indicators. household housing by applying and choosing the best algorithm among k-means and k-medoids. Based on the selection of the best algorithm, k-means is the best algorithm in classifying provinces in Indonesia compared to k-medoids with three clusters. The results of the grouping of provinces in Indonesia are expected to assist the government in dealing with problems related to household housing indicators so that the government's target of increasing the percentage of households occupying decent, safe, and affordable housing can be achieved.
Peramalam Model ARFIMA-GPH dan Intervensi Multi Input pada Indeks Harga Perdagangan Besar Indonesia Vivi Dina Melani; Miftahuddin; Muhammad Subianto
ESTIMASI: Journal of Statistics and Its Application Vol. 3, No. 2, Juli, 2022 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.vi.19683

Abstract

IHPBI is an early indicator in consumer price analysis. When inflation has occurred, Indonesia's economic stability begins to be disturbed, so in order to suppress inflation, the government raises interest rates and when the circulation of money begins to decrease. This study to see IHPBI in the next 3 years through forecasting using the ARFIMA method and multi-input intervention. This is done to find out the movement of the IHPBI over the next 3 years and to compare the two methods. The results obtained show that the selected model is ARFIMA(1,0.1579,0), the January 2009 intervention with ARIMA(1,1,1) of order (b=0, s=1, r=1) and November 2013 intervention with ARIMA(1,1,2) order (b=1, s=1, r=0). The IHPBI forecast for the next 3 years is increasing slowly every month
Penerapan Metode Linearized Ridge Regression pada Data yang Mengandung Multikolinearitas Mukrimin Adam; Sitti Sahriman; Nasrah Sirajang
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.19081

Abstract

One of the assumptions that must be met in the multiple linear regression model is that there is no multicollinearity problem among the independent variables. However, if there is a multicollinearity problem, then parameter estimation can be done using the linearized ridge regression (LRR) method. The LRR method has the advantage of choosing an optimal constant that is easy to determine and also has a minimum PRESS value. In this study, the infant mortality rate in South Sulawesi Province will be modeled using the LRR method based on the variables of the amount of vitamin A given, the number of health services, the number of babies born with low weight, the number of mothers who give birth assisted by medical personnel, and the number of babies who are breastfed. exclusive. One measure to see the goodness of the regression model is the Prediction Error Sum of Squares (PRESS). Based on the t-test at a significance level of 5%, the total coverage of vitamin A administration and the number of babies born with low weight gave a significant effect on infant mortality with a PRESS value of 0.6846.
Pemodelan Regresi Bivariate Poisson Inverse Gaussian pada Kasus Kematian Ibu dan Neonatal di Sulawesi Selatan Nurul Ikhsani; Anisa Kalondeng; Nirwan Ilyas
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.24113

Abstract

Overdispersion is a state with a variance value greater than the mean value so the Poisson Inverse Gaussian regression model is used. Meanwhile, to model two correlated response variables, the Bivariate Poisson Inverse Gaussian (BPIG) regression model was used. The BPIG model is a mixed- distributed model between the Poisson Bivariate and Gaussian Inverse distributions. The parameters of the BPIG regression model are estimated using Maximum Likelihood Estimation (MLE) with the Fisher Scoring algorithm. This study was applied to data on the number of maternal and neonatal deaths in South Sulawesi in 2019. The results obtained are predictor variables that affect the number of maternal and neonatal deaths in South Sulawesi in 2019, namely K4 services for pregnant women , active birth control participants , handling obstetric complications , handling neonatal complications  and the number of health centers .
Penerapan Metode Median Clustering Untuk Clusterisasi Peternakan di Provinsi Maluku M. Y. Matdoan; A. M. Balami; F. Kondolembang; S. J. Latupeirissa
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.24452

Abstract

Livestock in Maluku Province is one of the sectors that is the main priority in the context of increasing people's welfare. The potential for livestock in Maluku Province is increasing every year. However, there needs to be integrated processing and identification of potential commodities in each region. One method that is a reliable statistical method is to use the median clustering method. Median clustering is a method of grouping based on the median value. The median clustering algorithm selects K cluster centers with the aim of minimizing the sum of the measurement distances between each point cluster and the closest cluster center. The data used in this study came from the Maluku Province Central Bureau of Statistics (BPS) in 2022. The results of this research were that there were 3 clusters formed in livestock clusterization in regencies and cities in Maluku Province. Clus ter 1 consists of Southwest Maluku Regency. Cluster 2 consists of the Regencies of Central Maluku, Buru and West Seram. Furthermore, Cluster 3 consists of the Tanimbar Islands, Southeast Maluku, Aru Islands, Eastern Seram, South Buru, Ambon and Tual City.
Aplikasi Model Autoregressive Conditional Heteroscedastic-Generalized Auto Autoregressive Conditional Heteroscedastic pada Data Return Saham Bank Syariah Indonesia Zulfanita Dien R; Siswanto
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.24799

Abstract

The increase of the financial sector, financial information is used in the economy to model and predict the movement of capital market stocks, so investors can easily understand investment risks. Financial sector data is in the form of time series data. Financial data  is found that does not fit the assumption of heteroscedasticity, so a model is needed that can maintain heteroscedasticity. Model Autoregressive Conditional Heteroscedasticity-Generalized Autoregressive Conditional Heteroscedastic is one of the econometric models used to model heteroscedasticity data in time series. The data in this study is BSI's daily closing price data taken from 4 January 2021 to 31 August 2022 with 406 data. Based on the selection of a time series model on Bank Syariah Indonesia (BSI), the best models are ARMA (11.0) and ARCH models (1). So that the ARMA (11.0)-ARCH (1) model can be the best model for modeling and predicting BSI stock return prices.
Analisis Peluang Steady State Pada Kasus Covid-19 di Indonesia Menggunakan Rantai Markov Ika Pratiwi Haya; Andi Kresna Jaya; Nurtiti Sunusi
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.24802

Abstract

Covid-19 in Indonesia began to be recorded on March 2, 2020 with the number of positive patient cases as many as 2 people with the passage of time Covid-19 cases in Indonesia are always increasing. To see the development of Covid-19 cases in the future period, the opportunity for the number of Covid-19 cases can be used using the Markov chain. The Markov chain method is carried out using a transition probability matrix which is seen from the number of additions to positive Covid-19 patients in a steady state or a situation for a long period of time. Based on the results of the range of additions to the number of positive cases of Covid-19, 6 states were used. Furthermore, the calculation of the Markov Chain in the stationary state of Covid-19 cases in Indonesia after 328 days or 11 months obtained the probability of each state, namely state 1 of 0.0005, state 2 of 0.0069, state 3 of 0.1707, state 4 of 0.1462, state 5 of 0.1884 , and state 6 is 0.4873. Prediction of the addition of positive Covid-19 patients obtained results as many as 2058 patients in state 5 for July 1, 2022 with actual data as many as 2049 patients.

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