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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 2 Issue 2, October 2022" : 7 Documents clear
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.   
Multiple Correspondence Analysis towards the Change of Income and Sociodemographic of Citizens due to COVID-19 Pandemic in Malaysia Ong Wen Xuan; Lim Chui Ting; Nurulain Nabilah Binti Muhammad Aris Fadzilah; Nora Binti Muda
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.art5

Abstract

This paper intends to observe the economic impact and spending patterns of citizens when the spread of coronavirus occurred uncontrollably in Malaysia. The data used in this paper is the result of a first-round Special Survey ‘Effect of COVID-19 on Economy and Individual’ conducted by the Department of Statistics Malaysia. Based on the paired-t test, all the aspects tested are found to have significant differences before and during the pandemic. This is likely because citizens comply with the rules set during Movement Control Order. Next, the Chi-square test between the changes in citizens’ monthly income and sociodemographic factors are significantly associated. Therefore, factors state, gender, ethnicity, and age group of citizens are used in further analysis of multiple correspondence (MCA) to study the relationship between several categorical variables. Hence, citizens of age group 35­–44 years old from Central region and Chinese citizens have no changes in their income before and during the pandemic. Citizens of age group 15–34 years old and Northern region have both increment or reduction income whereas men citizens received lower-income payments. Indian women and citizens from Eastern region tend to not work during the pandemic. This study can be a guide to the government in overcoming social problems. 
Portfolio Analysis Using Malaysia Stock Market Data: Before and During COVID-19 Pandemic Khah May, She; Wei Yeing, Pan
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.art4

Abstract

This study attempts to evaluate Malaysia’s stock performance before and during COVID-19 across all sectors by using the Sharpe ratio and Sortino ratio with risk measured by standard deviation. We develop an algorithm for stock selection to construct the portfolio investment. In our study, we apply the Sharpe ratio, Treynor ratio and Jensen’s alpha to identify the optimal portfolio. The result shows that the portfolio with stock selection based on the Top 20 Sortino ratio from all stocks is superior to stock selection based on the Top 3 Sortino ratio for each sector. The daily adjusted closing stock prices are collected from March 1, 2019 to December 31, 2021. The result of this study indicates that several sectors are not affected during the COVID-19 pandemic, which are Technology, Industrial, Consumer Products and Services and Property. Hence, investors are suggested to form an optimal portfolio investment based on these sectors. Stock analysts are recommended to imply various risk-adjusted measures to evaluate the portfolio performance with a comprehensive perspective to support the outcome analysis.
AI-Based Personalized Virtual Therapist for Alcohol Relapse Win, Myat Noe; Han, Lim Whei; Samson Chandresh Kumar, Eric Kamalesh Kumar; Keat, Tan Yong; Ravana, Sri Devi
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.art3

Abstract

Binge drinking is one type of harmful alcohol use that has a variety of negative health impacts in both the drinker and others, either globally or in Malaysia. According to previous research, one in two current drinkers in Malaysia who are 13 years and older reported having engaged in binge drinking. Therefore, increased attention should be given to understand the drinking pattern of an individual and propose a solution that can help with addiction relapse. Thus, this study identified interventions that could assist alcohol relapse recovery and proposed a new generation of relapse prevention solution based on artificial intelligence (AI). By using a deep learning approach and machine learning based recommendation technique, it predicts the relapse rate of users, providing recovery consultation based on the user’s data and clinical data through a chatbot. This study involved helpful data collection, advanced data modeling, prediction analysis to support the alcohol relapse recovery journey. Hence, the proposed AI solution acted as a personalized virtual therapist to help the addicts stay sober. The objective is to present the design and realization of the AI based solution for sober journey. The proposed solution was tested with pilot study and significant benefits of virtual therapists for alcohol addiction relapse is reported in this paper.
Modeling the Rural Banks’ Survival in Semarang City and Regency during the COVID-19 Pandemic Naufal, Cesario Hanif; Putri, Santi Maudila; Ekaria
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.art7

Abstract

The COVID-19 pandemic has a significant impact on all sectors including the financial sector. Rural banks are one of the financial institutions that has been affected by the pandemic. The pandemic makes debtors unable to repay the loans that can lead to the increase of non-performing loans. This increase will affect the performance of the rural banks which can lead to the liquidation of rural banks. This is reflected by the decrease in the number of rural banks in Indonesia in 2018–2020 periods. On the other hand, it turns out that rural banks in Semarang City and Regency were not affected by the pandemic. Those rural banks are relatively able to survive, which is reflected by the stable level of the bank’s health. Therefore, this study aims to determine factors that affect the survival status of rural banks in Semarang whether they survive or not during the COVID-19 pandemic. To achieve this goal, binary logistic regression was used in this study. The results show that firm size, income growth, and debtors’ quality have a significant effect on the survival status of rural banks in Semarang City and Regency during the COVID-19 pandemic.
Optimal Classification of Emotions from Electroencephalography (EEG) Signals M.P., Mahima; Reddy, Meghana; C., Rachita; Hegde, Rajeshwari
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.art6

Abstract

It is incredibly challenging to build an intelligent algorithm for emotion recognition that can deliver high accuracy because electroencephalography (EEG) signals are not stationary, nonlinear, and noisy. First, decomposing the preprocessed EEG signals of the SEED dataset into five frequency bands: delta, theta, alpha, beta, and gamma, and then calculated their energy and entropy from the extracted features. Then Principal Component Analysis (PCA) method for feature reduction was performed.  It is important to note that different types of wavelets transform (db6, db5, etc.) were tested, and hyperparameter tuning of classification models was done to obtain optimal accuracy. The next step is classifying the emotions into three states:  -1(negative), 0(neutral), and +1(positive), and tested the dataset on two types of classification models, namely Random Forest and Support vector machine (SVM). SVM gives better performance compared to Random Forest with an accuracy of 80.74%.

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