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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 2, October 2021" : 6 Documents clear

Implementation of Markov Chain in Detecting Opportunities for Natural Disasters in Klaten (Case Study: Number of Floods, Landslides, and Hurricanes 2019-2020) Novianti, Afdelia; Utari, Dina Tri
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 1 Issue 2, October 2021
Publisher : Universitas Islam Indonesia

Java Island is one of the areas that is very fertile and densely populated, but on the other hand, Java Island is also one of the areas that is most frequently hit by natural disasters, one of which is Klaten Regency. Natural disaster itself is an event that threatens and disrupts human life caused by nature. Some of the natural disasters that often occur simultaneously in Klaten Regency are floods, landslides, and hurricanes. These three disasters usually occur during the rainy season. This of course makes the government need to take action by seeing the large chance of a disaster occurring in order to optimize disaster management. Then research will be carried out that aims to determine the chances of natural disasters occurring in the next few years. Forecasting will be carried out using the Markov chain method, with this method the probability value of the future period can be estimated using the current period probability value based on the characteristics of the past period. So that the value of the steady state chance of floods and landslides in period 36 (December 2023) and hurricanes in period 15 (March 2022) with the chances of a disaster are 34.21%, 15.38%, and 73.53%, respectively.Received August 31, 2021Revised October 27, 2021Accepted November 11, 2021

Application of the Spatial Autoregressive (SAR) Method in Analyzing Poverty in Indonesia and the Self Organizing Map (SOM) Method in Grouping Provinces Based on Factors Affecting Poverty Islamy, Ulimazzada; Novianti, Afdelia; Hidayat, Freditasari Purwa; Kurniawan, Muhammad Hasan Sidiq
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 1 Issue 2, October 2021
Publisher : Universitas Islam Indonesia

The economy is a benchmark to determine the extent of the development of a country. Indonesia, which is now a developing country, is ranked 5th as the poorest country in Southeast Asia. Of course, the government must pay attention because until now, poverty has become one of Indonesia's main problems. Ending poverty everywhere and in all its forms is goal 01 of the Sustainable Development Goals (SDGs) program. One of the efforts that can be done is by planning as part of the implementation of the target, namely eliminating poverty and appropriate social protection for all levels of society so that the SDGs are achieved. Therefore, it is important to do a spatial analysis by making a model of poverty estimation in Indonesia and grouping to identify areas in Indonesia that have the highest poverty mission. The clustering method used in this grouping is Self Organizing Map (SOM). In this study, Spatial Autoregressive (SAR) analysis was used to create a predictive model. This is because poverty is very likely to have a spatial influence or be influenced by location to other areas in the vicinity. The results of the SAR model that can be formed are . Furthermore, the region with the highest mission is grouped using the Self Organizing Map (SOM) clustering based on variables that significantly affect the amount of poverty in Indonesia. From the results of the analysis obtained four clusters, each of which has its characteristics to classify 34 provinces in Indonesia. The clusters formed include cluster 1 consisting of 17 provinces, cluster 2 consisting of 9 provinces, cluster 3 consisting of 1 province, and cluster 4 consisting of 7 provinces.

Comparison of Simple and Segmented Linear Regression Models on the Effect of Sea Depth toward the Sea Temperature Nirwana, Muhammad Bayu; Wulandari, Dewi
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 1 Issue 2, October 2021
Publisher : Universitas Islam Indonesia

The linear regression model is employed when it is identified a linear relationship between the dependent and independent variables. In some cases, the relationship between the two variables does not generate a linear line, that is, there is a change point at a certain point. Therefore, themaximum likelihood estimator for the linear regression does not produce an accurate model. The objective of this study is to presents the performance of simple linear and segmented linear regression models in which there are breakpoints in the data. The modeling is performed onthe data of depth and sea temperature. The model results display that the segmented linear regression is better in modeling data which contain changing points than the classical one.Received September 1, 2021Revised November 2, 2021Accepted November 11, 2021

Determination of the Shortest Route on the Distribution System using Ant Colony Optimization (ACO) Algorithm (Case Study: Alfamidi Palu Branch – PT. Midi Utama Indonesia) Indria, Nabila Dwi; Junaidi, Junaidi; Utami, Iut Tri
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 1 Issue 2, October 2021
Publisher : Universitas Islam Indonesia

The distribution system of goods is one of the most important parts for every company. The company certainly has many route options to visit, and this is expected to be conducted efficiently in terms of time. In the distribution of goods by Alfamidi company in Palu City which has 51 outlets include into the category of Traveling Salesman Problem (TSP) because of many route options that can be visited. The problem can be solved by employing the Ant Colony Optimization (ACO) method which is one of the algorithms Ant Colony System (ACS). The ACS acquires principles based on the behavior of ant colonies and applies three characteristics to determine the shortest route namely status transition rules, local pheromone renewal and global pheromones. The result showed that the shortest route of the distribution of goods based on the calculation of selected iterations was ant 1 with the shortest total distance obtained 86.98 km.

Grouping of Districts Based on Poverty Factors in Papua Province Uses The K-Medoids Algorithm Novianti, Afdelia; Afnan, Irsyifa Mayzela; Utama, Rafi Ilmi Badri; Widodo, Edy
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 1 Issue 2, October 2021
Publisher : Universitas Islam Indonesia

Poverty is an essential issue for every country, including Indonesia. Poverty can be caused by the scarcity of basic necessities or the difficulty of accessing education and employment. In 2019 Papua Province became the province with the highest poverty percentage at 27.53%. Seeing this, the district groupings formed in describing poverty conditions in Papua Province are based on similar characteristics using the variables Percentage of Poor Population, Gross Regional Domestic Product, Open Unemployment Rate, Life Expectancy, Literacy Rate, and Population Working in the Agricultural Sector using K-medoids clustering algorithm. The results of this study indicate that the optimal number of clusters to describe poverty conditions in Papua Province is 4 clusters with a variance of 0.012, where the first cluster consists of 10 districts, the second cluster consists of 5 districts, the third cluster consists of 12 districts, and the fourth cluster consists of 2 districts.

Forecasting COVID-19 Cases in Indonesia Using Hybrid Double Exponential Smoothing Kartikasari, Mujiati Dwi
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 1 Issue 2, October 2021
Publisher : Universitas Islam Indonesia

The COVID-19 epidemic has spread throughout countries around the world. In Indonesia, this case was detected in early March 2020, and until now, there is still an increase in positive cases of COVID-19. The purpose of this paper is to predict COVID-19 cases in Indonesia using a time series approach. The method used is H-WEMA method because this method can capture trend data patterns following the conditions of COVID-19 cases in Indonesia. Based on the analysis results, H-WEMA can predict COVID-19 cases very well. The forecasted results of the COVID-19 cases in Indonesia still have an upward trend, so it needs the cooperation of all elements of community to reduce the spread of COVID-19.

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muhammad Muhajir

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+6289637608885

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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