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muhammad Muhajir
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INDONESIA
Enthusiastic : International Journal of Applied Statistics and Data Science
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 73 Documents
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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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (291.859 KB) | DOI: 10.20885/enthusiastic.vol1.iss2.art1

Abstract

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.
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (286.828 KB) | DOI: 10.20885/enthusiastic.vol1.iss1.art2

Abstract

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
Application of K-Medoids Clustering to Increase the 2020 Family Planning Program in Sleman Regency Febriyanti, Syintya; Nugraha, Jaka
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 2 Issue 1, April 2022
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol2.iss1.art2

Abstract

Indonesia is a country with a large population. Based on the results of the 2020 census, Indonesia's population ranks fourth in the world. The Indonesian government has made a policy to reduce population growth, namely the Family Planning Program or Keluarga Berencana (KB). One of the areas that did not escape the target was the DIY. Based on BKKBN DIY data, there is a significant difference between the number of active KB participants and the number of couples of childbearing ages, the number of KB equipment and the number of KB health facilities that exist between sub-districts in Sleman Regency. Then the sub-district classification is carried out based on the 2020 KB data in Sleman Regency using the K-Medoids Clustering method. This study aims to see the sub-district grouping used as a reference by the government in increasing active KB participants in the community to overcome the population in Yogyakarta, primarily focusing on Sleman. The categories in each cluster, namely Cluster 1, which consists of 6 sub-districts, have a high level of KB active participants, couples of reproductive ages, KB equipment, and KB health facilities. Then Cluster 2, which consists of 6 sub-districts, has a medium level of KB active participants, couples of reproductive ages, KB equipment, and KB health facilities. While Cluster 3 consists of 5 sub-districts, where KB active participants, teams of reproductive age, KB equipment, and KB health facilities are low level.
Implementation K-Means Algorithm to Group Provinces By Factors Influenced Criminal Act in Indonesia in 2019 Wahidah, Zumrotul; Utari, Dina Tri
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 2 Issue 1, April 2022
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol2.iss1.art5

Abstract

A criminal act is an act that is prohibited by a criminal law accompanied by a sanction in the form of a particular crime for whoever violates the prohibition. Criminal action as a social phenomenon is more influenced by various aspects of life in society, including poverty and unemployment factors. Grouping the factors that influence a crime is necessary to find the most recent information that was not previously known. This research uses the K-Means method, a non-hierarchical cluster analysis that seeks to partition data with the same characteristics into one cluster. The results showed that 3 clusters formed, with cluster 1 covering 17 provinces are areas with the characteristics of the lowest percentage of poverty and the highest average unemployment, the cluster group 2 includes 12 provinces which are areas with the characteristics of the percentage of moderate poverty and the lowest average unemployment, the cluster group 3 includes five provinces which are areas with the characteristics of the highest percentage of poverty and moderate unemployment.
Geographically Weighted Regression with The Best Kernel Function on Open Unemployment Rate Data in East Java Province Putra, Robiansyah; Wahyuning Tyas , Sischa; Fadhlurrahman, Muhammad Ghani
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 2 Issue 1, April 2022
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol2.iss1.art4

Abstract

Unemployment is one of the problems that hinders employment development programs. Based on East Java BPS data, the Open Unemployment Rate in East Java in 2019 is about 3.92 percent. In 2020, unemployment increased by 466.02 thousand people and OUR increased by 2.02 percent to 5.84 percent in August 2020. In addition to the indicators that affect OUR, each observation location has different characteristics, so multiple linear regression modeling is not appropriate. Geographically Weighted Regression is one of the spatial analysis developed from multiple linear regression for data containing spatial heterogeneity effects. The weighting functions used for this GWR model are Kernel Fixed and Adaptive functions (Gaussian, Bi-Square, Tricube, and Exponential). The analytical step carried out in estimating the parameters is to use WLS. In the test, the best weighting was obtained, namely the Adaptive Tricube. Based on the results of the study, the GWR model with Adaptive Tricube weighted resulted in the value of R-Squared = 84.88%. However, the best model is obtained from the GWR model with exponential weighting with the smallest Akaike Information Criterion (AIC) value compared to the others, namely AIC = 86.01264 with R-Squared = 91.67.
Coronary Heart Disease Risk Prediction Using Binary Logistic Regression Based on Principal Component Analysis Azhari, M Fauzan; Fitriani, Farah Ayu
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 2 Issue 1, April 2022
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol2.iss1.art6

Abstract

Based on data from the World Health Organization (WHO), one type of heart disease namely coronary heart disease is the deadliest disease in the world. In 2016 at least 9,4 million people died caused by coronary heart disease. In Indonesia, deaths caused by heart disease, blood vessel (CVD), and respiratory disorders are the fourth highest in ASEAN (23,1%). Because of the danger of coronary heart disease, we need a system or model that can predict heart disease early, so that it can be treated early and can reduce the death rate caused by heart disease. This study uses principal component analysis (PCA) to make a linear combination of variables that have a high correlation so that the assumption of multicollinearity in the data can be resolved. For the prediction, this study uses binary logistic regression to predict heart disease based on existing factors. The result of the PCA there is 7 component variables with a total variance that can be explained as much as 72,9%. From the Bartlett test of the PCA data, the obtained p-value is 1 which means that there is no multicollinearity in the data. Predictive analysis using binary logistic regression based on PCA’s data was proven to increase the accuracy to 85%.
Analysis of Changes in Atmospheric CO2 Emissions Using Prophet Facebook Primandari, Arum Handini; Thalib, Achmad Kurniansyah; Kesumawati, Ayundyah
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 2 Issue 1, April 2022
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol2.iss1.art1

Abstract

CO2 emissions have been an environmental issue for decades. The trigger for the increasing concentration of CO2 in the atmosphere is the growth of industries related to burning fossil fuels for coal, natural gas, and petroleum. For nearly a century, several attempts have been made to suppress the rapid growth of CO2 . This study uses daily atmospheric  CO2  levels observed in  Mauna Loa laboratories. The method used is a Prophet that can handle seasonality and mark the change points. Almost 20% of data was missing value, which was then imputed using spline interpolation. Based on the analysis results,  CO2 levels have an upward trend throughout the year and seasonality. There is no point of change in the last ten years that shows a decrease in  CO2  levels. Using forward chaining cross-validation evaluation and error measurement, the prophet model can follow the pattern of  CO2  levels well. The average RMSE value is less than 2.0, with an MAPE value bellow 0.5%.
Forecasting International Tourist Arrivals in Indonesia Using SARIMA Model Nurhasanah , Deden; Salsabila , Aurielle Maulidya; Kartikasari, Mujiati Dwi
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 2 Issue 1, April 2022
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol2.iss1.art3

Abstract

Tourism is an important sector that significantly contributes to the economy, so the tourism sector is a priority development program. International tourist arrivals indirectly contribute to the country's economic growth. The government has an important task to increase the number of foreign tourist visits. One way to encourage an increase in foreign tourist arrivals is by forecasting. In general, the time series data for the arrival of foreign tourists has a seasonal pattern. The forecasting method that can model seasonal data is SARIMA. This study aims to predict the arrival of foreign tourists in Indonesia using the SARIMA model. Forecasting results show that the appearance of foreign tourists to Indonesia has increased every period.
Towards Data-Driven Teaching Strategies to Develop Mathematical Thinking Gao, Yaqi; Yuan, Rui; Li, Jiansheng
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 2 Issue 2, October 2022
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol2.iss2.art2

Abstract

Mathematics learning is not only about learning knowledge but also about cultivating mathematical thinking and learning how to think about and solve mathematical problems. Unfortunately, the way teachers evaluate students is still based on the student’s academic achievement. Many students learn by rote, which is unsuitable for developing their thinking ability at different levels. With the advent of the era of big data, teaching has become more accurate and convenient. In this paper, we analyze the student’s academic performance at different levels from the point of view of mathematical thinking. We collected the learning achievement of two classes in a middle school in Beijing. We then analyzed the overall thinking of the two classes, and the mathematical thinking level of students at different levels. Based on the analysis results, we put forward some teaching suggestions on improving mathematical thinking to help improve teaching methods and quality and promote students’ mathematical thinking development.
Estimation of Prospective Benefit Reserve Based on Gross Premium Valuation Method using Indonesian Mortality Table IV and De-Moivre Assumptions Anastasya Prionggo , Echa; Pratama, Mohammad Nabil; NL , Amandaputri; Indrayatna, Fajar
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 2 Issue 2, October 2022
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol2.iss2.art1

Abstract

According to regulations, life insurance companies must meet several requirements related to the company's financial health, one of which is a technical reserve. Technical or premium reserves are funds that the insurance companies must prepare. These funds will cover financial losses experienced by someone who applies for the claim. Thus, the author will use the Gross Premium Valuation (GPV) method in this research. Premium reserves can be determined using two approaches: retrospective and prospective reserves. In this research, the author will determine the prospective reserves with GPV for single decrement insurance and single life n-year continuous term life. The distribution of deaths used in determining the probability of death is the Indonesian life table IV and the de-Moivre assumption with parameter (ɷ=111). Different assumptions for the death probability distribution will result in different premium reserve values, so we can see a difference in premium reserves resulting from the two death probability distributions. This research uses data from male policyholders aged 40 years who followed continuous term life insurance for 20 years. Benefits will be paid right at the time of death, with a discrete method of premium payments made at the beginning of each year for 10 years.   

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