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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 7 Documents
 1 
Search results for , issue "Volume 4 Issue 1, April 2024" : 7 Documents clear
Performance of Three-Parameters Dirichlet Universal Portfolio During COVID-19 Pandemic Yeok Qin, Goh; Sook Theng, Pang
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 4 Issue 1, April 2024
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol4.iss1.art2

Abstract

Stock returns are often the primary objectives for investors, financial analysts as well as the politicians with the intention to make a right investment decision. In this paper, we study the performance of the three-parameters Dirichlet universal portfolio on selected stocks during the COVID-19 pandemic. Some empirical results are obtained based on some selected data sets from the local stock exchange. The period of trading of the stocks are selected from 2nd January 2020 to 18th August 2021 consisting of 400 trading days. The empirical results seem to indicate the three-parameters Dirichlet universal portfolio performs well during the COVID-19 pandemic by a proper choice of parameters. Also, this study provides evidence that the capital achievement at the end of the 400th trading days is influenced by the arrangement of the stocks within each selected data set. Besides, the performance of the homogeneous datasets, particularly, main data set from healthcare sector, is better than heterogeneous datasets during the COVID-19 pandemic.
Characterization of Student’s Performance in Massive Open Online Courses (MOOC) Ching Joe, Tan
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 4 Issue 1, April 2024
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol4.iss1.art3

Abstract

Massive Open Online Courses (MOOC) allow students to learn online at any time and from any location. Unfortunately, poor completion rates and a large student group make it difficult for teachers to keep track of their student’s progress. Due to a lack of adequate counselling, students who perform poorly are more likely to give up. The goal of this study was to predict student’s certification by analyzing data on student’s learning behavior. The initial data on learning behavior was obtained from edX, a well-known MOOC platform. Based on this data, three statistical models such as logistic regression, graph convolutional network, and cluster analysis were utilized to predict student’s performance. The proposed model’s usefulness was demonstrated by using a testing set of data from the actual courses. Our findings showed that tracking student activity in terms of number of unique days active, watching videos, participating in forum discussions, and exploring more courseware content might help predict student’s performance in MOOC and enhance completion rates.
Modelling Exchange Rates and PMS Prices Impact on Inflation in Nigeria (1985-2020): A Regression Analysis Gwani, Alhaji Abdullahi; Farouk, Abbas Umar; Mukhtar; Sek, Siok Kun
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 4 Issue 1, April 2024
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol4.iss1.art6

Abstract

On the rise in Premium Motor Spirit (PMS) prices and cash rates, a conventional least squares analysis was used to determine the relationship between the respondent variable, inflation, and the explanatory variables, PMS price and exchange rate. According to the results, as evidenced by the conventional least squares regression, the PMS price and money rate were significant drivers of inflation, accounting for about 88% of the fluctuation in inflation. Additionally, the Breusch-agnostic test revealed that the residuals of the direct regression model were not heteroscedastic, and the ACF and PACF tests revealed that the error terms did not have autocorrelation. The Jarque-Bera ordinariness test was used to express the perceived background noise as normal. As demonstrated by the findings, the increase in the price of PMS and the decline in the value of the Naira influenced Nigerian inflation. Finally, based on the research econometric outcomes and interpretations, the study discussed the policy implications of these findings and offered recommendations. For future work, research should be conducted on energy transition and efficiency.
Analyzing Tourist Satisfaction Using Factor Analysis and Text Mining: An Ecotourism Study in Girpasang Village Kariyam; Tasya Apriliana; Nur Aulia Maknunah; Hafis Muhammad Nizam; Rizky Mardhatillah; Nova, Rahma Fatwa
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 4 Issue 1, April 2024
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol4.iss1.art4

Abstract

In the second half of 2022, the tourism industry started recovering from the vast impacts of the COVID-19 pandemic. Tourism is one of the most feasible sources of income for the small, rural village of Girpasang, situated at the heights of Mount Merapi. Tourist satisfaction has been attributed to the success of tourist destinations and is, therefore, a benchmark for their development. This study aimed to explain the factors that affected tourist satisfaction and other underlying aspects that call for improvement, using confirmatory factor analysis and text mining. The data used was collected from a total of 102 respondents at Girpasang Village within two days. The results showed that there were five common factors affecting tourist satisfaction: staff attitude, reliability of tourist facilities, comfort of tourist facilities, comprehensiveness of facilities provided, and tangible condition of the environment. Based on text mining results of tourist critics, it was found that access roads were the most profound complaint.
K-Means Clustering Application of Open ‎Unemployment in 2020 Caused by COVID-19 in West Java Province Ardiansyah, M. Ficky Haris; Amany, Nurfatimah; Anugrah, Cahya Ireno; Syafitri, Utami Dyah
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 4 Issue 1, April 2024
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol4.iss1.art1

Abstract

West Java was the province with the highest unemployed rate during the COVID-19 pandemic. Significant increase of open ‎unemployment rate in West Java negatively impacts the national income. This study aims to apply the ‎clustering method using the k-means algorithm to determine priority clusters in West Java ‎Province by looking at the number of clusters in West Java’s city and the main characteristic of ‎each cluster. The clustering was conducted utilizing a k-means clustering algorithm which is grouping data based on similar ‎characteristics. The clustering results were evaluated using silhouette method. The results indicated that ‎two clusters were optimal. The clustering process using the k-means method showed that there were three clusters distinguishing the open unemployment rate during the pandemic in West Java Province in 2020. Cluster 1 ‎had a fairly low open unemployment rate due to the stalled service sector and low minimum city wage. ‎Cluster 2 had a high open unemployment rate due to the service sector and high minimum city wage. ‎Cluster 3 had medium open unemployment rate due to the service sector and also medium minimum city ‎wage. It suggests that cluster 2 is a priority cluster in dealing with the open unemployment rate.‎
Hybrid MODWT-ARMA Model for Indonesia Stock Exchange LQ45 Index Forecasting Hermansah
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 4 Issue 1, April 2024
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol4.iss1.art5

Abstract

This research discussed a hybrid Maximal Overlap Discrete Wavelet Transform (MODWT)-Autoregressive Moving Average (ARMA) model by combining the MODWT and the ARMA models to deal with the nonstationary and long-range dependence (LRD) time series. Theoretically, the details series obtained by MODWT are stationary and short-range dependent (SRD). Then, the general form of the MODWT-ARMA model was derived. In the experimental study, the daily Indonesia stock exchange LQ45 index time series was used to assess the performance of the hybrid model. Finally, the Mean Squared Error (MSE) and Mean Absolute Percent Error (MAPE) comparison with DWT-ARMA, ARIMA, and exponential smoothing models indicates that this combined model effectively improves forecasting accuracy. Based on the result of the analysis, the score of MSE of the MODWT-ARMA model was 51.42533, the score of the DWT-ARMA model was 180.1799, the score of the ARIMA model was 168.7863, and the score of the exponential smoothing model was 168.7824. At the same time, the score of MAPE in the MODWT-ARMA model was 0.00580797, the score of the DWT-ARMA model was 0.01106721, the score of the ARIMA model was 0.01070074, and the score of the exponential smoothing model was 0.01069591.
Analyzing the Impact of the Pandemic on Indonesia’s Economic Growth Using Dynamic Time Warping Primandari, Arum Handini; Kusuma Arum, Widya
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 4 Issue 1, April 2024
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol4.iss1.art7

Abstract

Asia’s GDP experienced the most drastic decline during the COVID-19 compared to other economic crises. This study collected data on economic indicators for each province/city to observe economic growth in Indonesia, such as Gross Regional Domestic Product (GRDP), unemployment rate, and economic growth. The clustering method on time series data found several provinces/cities with similar economic growth patterns to observe the pandemic's impact on their economies. Knowing the pattern of economic growth will help the regulation holder support provinces with the right policy. For this purpose, we utilized the Dynamic Time Warping (DTW) distance with the k-medoids procedure. The DTW is an algorithm for measuring the similarity between two temporal sequences. The clustering of the three economic indicators had three clusters with the most optimal validation index. Each cluster had almost the same pattern since the trend tended to increase from before the pandemic and then decrease during the pandemic. The decrease in GRDP was less significant than the minimal data on GRDP that happened before the pandemic. Most provinces had negative economic growth during the pandemic, which skyrocketed even for the first quarter of 2023, almost the same as before the pandemic.

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