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Enthusiastic : International Journal of Applied Statistics and Data Science

ENTHUSIASTIC
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Published by Universitas Islam Indonesia

ISSN : 2798253X EISSN : 27983153 DOI : 10.20885

Core Subject : Economy, Science, Education, Engineering,

Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Mathematics

ENTHUSIASTIC is an international journal published by the Statistics Department, Faculty of Mathematics and Natural Sciences, Universitas Islam Indonesia. ENTHUSIASTIC publishes original research articles or review articles on all aspects of the statistics and data science field which should be written in English. ENTHUSIASTIC has the vision to become a reputable journal and publish good quality papers. We aim to provide lecturers, researchers both academic and industry, and students worldwide with unlimited access to be published in our journal. Specifically, these scopes of the ENTHUSIASTIC journal are: 1. Statistical Disaster Management 2. Actuarial Science 3. Data Science 4. Statistics of Social and Business 5. Statistics of Industry

Arjuna Subject : Matematika - Statistik dan Probabilitas

Articles 6 Documents

Search results for , issue "Volume 1 Issue 1, April 2021" : 6 Documents clear

Risk Analysis on the Growth Rate of Covid-19 Cases in Indonesia Using Statistical Distribution Model Utari, Dina Tri; Hendradewa, Andrie Pasca
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 1 Issue 1, April 2021
Publisher : Universitas Islam Indonesia

Coronavirus or Covid-19 outbreak has been declared as a pandemic and many countries were not ready to deal with such an eventuality. The highly rapid rate of transmission is one reason for the need to take mitigation measures, since healthcare system has limited capacity. Indonesia is one of the countries that has lost medical resources to the pandemic. In order to provide more comprehensive information about the characteristics of Covid-19 in Indonesia, risk analysis of the occurrence of new cases was needed. This study proposes a related overview about risk occurrence of new Covid-19 cases per daily basis by performing distribution fitting technique to form a statistical distribution model. Among the available alternative models, Geometric distribution is the most suitable to describe the growth of new cases in Indonesia. Received February 12, 2021Revised March 25, 2021Accepted April 15, 2021

Generalized Linear Mixed Model and Lasso Regularization for Statistical Downscaling Hayati, Ma'rufah -; Muslim, Agus
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 1 Issue 1, April 2021
Publisher : Universitas Islam Indonesia

Rainfall is one of the climatic elements in the tropics which is very influential in agriculture, especially in determining the growing season. Thus, proper rainfall modeling is needed to help determine the best time to start cultivating the soil. Rainfall modeling can be done using the Statistical Downscaling (SDS) method. SDS is a statistical model in the field of climatology to analyze the relationship between large-scale and small-scale climate data. This study uses response variables as a small-scale climate data in the form of rainfall and explanatory variables as a large-scale climate data of the General Circulation Model (GCM) output in the form of precipitation. However, the application of SDS modeling is known to cause several problems, including correlated and not stationary response variables, multi-dimensional explanatory variables, multicollinearity, and spatial correlation between grids. Modeling with some of these problems will cause violations of the assumptions of independence and multicollinearity. This research aims to model the rainfall in Indramayu Regency, West Java Province using a combined regression model between the Generalized linear mixed model (GLMM) and Least Absolute Selection and Shrinkage Operator (LASSO) regulation (L1). GLMM was used to deal with the problem of independence and Lasso Regulation (L1) was used to deal with multicollinearity problems or the number of explanatory variables that is greater than the response variable. Several models were formed to find the best model for modeling rainfall. This research used the GLMM-Lasso model with Normal spread compared to the GLMM model with Gamma response (Gamma-GLMM). The results showed that the RMSE and R-square GLMM-Lasso models were smaller than the Gamma-GLMM models. Thus, it can be concluded that GLMM-Lasso model can be used to model statistical downscaling and solve the previously mentioned constraints. Received February 10, 2021Revised March 29, 2021Accepted March 29, 2021

Aplication of ARIMA Model for Forecasting Additional Positive Cases of Covid-19 in Jember Regency Hariadi, Wigid; Sulantari, Sulantari
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 1 Issue 1, April 2021
Publisher : Universitas Islam Indonesia

The autoregressive integrated moving average (ARIMA) model is a popular method for forecasting univariate time series dataset. This method consists of four major stages, namely: identification, parameter assessment, diagnostic examination, and forecasting using the ARIMA model (p, d, q). ARIMA model can be applied in various fields, one of which is medical field. Currently, there had been a daily increase in the number of patients infected with Corona virus. Jember is one of the regencies in East Java with a high number of confirmed patients. On February 5, 2021, it was recorded that 5,872 patients were confirmed positive for Corona, 5,241 patients had been declared cured, and 352 patients were declared dead. Given the high number of confirmed cases of Covid-19 in Jember, the authors would like to conduct a prediction research on the increasing number of confirmed cases of Covid-19 in Jember Regency for the upcoming period using the ARIMA model (p,d,q). The research was conducted in the Jember Regency, East Java. The data were collected from March 28, 2020 to January 30, 2021. The study showed that the ARIMA model (1,2,3) was the best model for predicting the additional positive cases of Covid-19 per week in Jember, with the sum squared resid of 7.9496. The data forecast for the additional positive cases of Covid-19 for the next 6 periods is: 224,56 patients, 247,84 patients, 273,53 patients, 301,89 patients, 333,18 patients, and 367,72 patients. Received February 10, 2021Revised April 8, 2021Accepted April 22, 2021

Solving Fuzzy Transportation Problem Using ASM Method and Zero Suffix Method Aini, Aurora Nur; Shodiqin, Ali; Wulandari, Dewi
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 1 Issue 1, April 2021
Publisher : Universitas Islam Indonesia

The transportation problem is a special case for linear programming. Sometimes, the amount of demand and supply in transportation problems can change from time to time, and thus it is justified to classify the transportation problem as a fuzzy problem. This article seeks to solve the Fuzzy transportation problem by converting the fuzzy number into crisp number by ranking the fuzzy number. There are many applicable methods to solve linear transportation problems. This article discusses the method to solve transportation problems without requiring an initial feasible solution using the ASM method and the Zero Suffix method. The best solution for Fuzzy transportation problems with triangular sets using the ASM method was IDR 21,356,787.50, while the optimal solution using the Zero Suffix method was IDR 21,501,225.00. Received February 5, 2021Revised April 16, 2021Accepted April 22, 2021

Mardia’s Skewness and Kurtosis for Assessing Normality Assumption in Multivariate Regression Wulandari, Dewi; Sutrisno, Sutrisno; Nirwana, Muhammad Bayu
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 1 Issue 1, April 2021
Publisher : Universitas Islam Indonesia

In Multivariate regression, we need to assess normality assumption simultaneously, not univariately. Univariate normal distribution does not guarantee the occurrence of multivariate normal distribution [1]. So we need to extend the assessment of univariate normal distribution into multivariate methods. One extended method is skewness and kurtosis as proposed by Mardia [2]. In this paper, we introduce the method, present the procedure of this method, and show how to examine normality assumption in multivariate regression study case using this method and expose the use of statistics software to help us in numerical calculation. Received February 20, 2021Revised March 8, 2021Accepted March 10, 2021

Survival Analysis Based on Average Response Time of Maritime Search and Rescue (SAR) Incidents in 2019 Using Kaplan-Meier Method and Log-Rank Test Kurniawan, Muhammad Hasan Sidiq; Mahara, Duhania Oktasya
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 1 Issue 1, April 2021
Publisher : Universitas Islam Indonesia

Indonesia is the largest archipelagic country in the world (based on area and population), which makes it as one of countries with the most significant maritime activities. Therefore, there has been a high rate of maritime accidents in Indonesia. The National Search and Rescue Agency (BASARNAS) as a non-ministerial government agency with the primary task of Search and Rescue (SAR) operation deals with several types of accidents, including maritime accidents. Response time as the time to receive news about the accidents until the SAR unit comes to the rescue is very crucial in this matter. Average response time is stipulated based on BASARNAS’s regulations to estimate information about the survival probability of the victims. This research concerns with the survival analysis using Kaplan-Meier Method and Log-Rank Test. The researchers categorized maritime accidents into three categories: ‘Low’, ‘Medium’, and ‘High’. This classification aims to find out whether the survival function of each category has the same or different function and to investigate whether there are differences from the given responses or not. The survival analysis with Kaplan-Meier method revealed that the three categories had different survival functions. The survival analysis was followed by a Log-Rank Test. The final result shows that there is no difference in the responses given by the three categories when maritime accidents occur. Received February 10, 2021Revised March 29, 2021Accepted March 29, 2021

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muhammad Muhajir

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+6289637608885

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enthusiastic@uii.ac.id

Editorial Address
Jl. Teknika, Krawitan, Umbulmartani, Kec. Ngemplak, Kabupaten Sleman, Daerah Istimewa Yogyakarta 55584

Location
Kab. sleman,

Daerah istimewa yogyakarta

INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name muhammad Muhajir

Contact Email mmuhajir@uii.ac.id

Phone +6289637608885

Journal Mail Official enthusiastic@uii.ac.id

Editorial Address Jl. Teknika, Krawitan, Umbulmartani, Kec. Ngemplak, Kabupaten Sleman, Daerah Istimewa Yogyakarta 55584

Location Kab. sleman, Daerah istimewa yogyakarta INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Contact Name
muhammad Muhajir

Contact Email
mmuhajir@uii.ac.id

Phone
+6289637608885

Journal Mail Official
enthusiastic@uii.ac.id

Editorial Address
Jl. Teknika, Krawitan, Umbulmartani, Kec. Ngemplak, Kabupaten Sleman, Daerah Istimewa Yogyakarta 55584

Location
Kab. sleman,

Daerah istimewa yogyakarta

INDONESIA