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INDONESIA
Emerging Science Journal
Published by Ital Publication
ISSN : 26109182     EISSN : -     DOI : -
Core Subject : Social,
Environmental Science
Emerging Science Journal is not limited to a specific aspect of science and engineering but is instead devoted to a wide range of subfields in the engineering and sciences. While it encourages a broad spectrum of contribution in the engineering and sciences. Articles of interdisciplinary nature are particularly welcome.
Arjuna Subject : -
Articles 874 Documents
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Response of Financial Markets to COVID-19 Pandemic: A Review of Literature on Stock Markets Bayu Arie Fianto; Masagus M. Ridhwan; Syed Alamdar Ali Shah; Muhammad Faris; Rafiatul Adlin Hj Mohd Ruslan
Emerging Science Journal Vol 7 (2023): Special Issue "COVID-19: Emerging Research"
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-SPER-03

Abstract

The objective of this research is to consolidate the literature published on the COVID-19 crisis impact on global stock markets to gain managerial implications from the crisis. It performs a thematic bibliometric review of the literature published in Scopus-ranked journals since the beginning of the pandemic using FCWI, Piecharts, and VOSViewer. It identifies the most under-researched regions and eight emerging sub-themes. The research finds that the benchmark theme is market behavior during the COVID-19 crisis, whereas an emerging benchmark theme is the markets after the COVID-19 crisis. The holistic view of the literature supporting eight sub-themes suggests that the government's role is of utmost importance to handle the impact of the COVID-19 crisis, which should be industry-specific. It identifies that all eight sub-themes of the research are the future research directions in all and specifically in the South American, African, South East Asian, and Oceania regions till the crisis continues. Doi: 10.28991/ESJ-2023-SPER-03 Full Text: PDF
A New Efficiency Improvement of Ensemble Learning for Heart Failure Classification by Least Error Boosting Ployphan Sornsuwit; Phimkarnda Jundahuadong; Siwarit Pongsakornrungsilp
Emerging Science Journal Vol 7, No 1 (2023): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-01-010

Abstract

Heart failure is a very common disease, often a silent threat. It's also costly to treat and detect. There is also a steadily higher incidence rate of the disease at present. Although researchers have developed classification algorithms. Cardiovascular disease data were used by various ensemble learning methods, but the classification efficiency was not high enough due to the cumulative error that can occur from any weak learner effect and the accuracy of the vote-predicted class label. The objective of this research is the development of a new algorithm that improves the efficiency of the classification of patients with heart failure. This paper proposes Least Error Boosting (LEBoosting), a new algorithm that improves adaboost.m1's performance for higher classification accuracy. The learning algorithm finds the lowest error among various weak learners to be used to identify the lowest possible errors to update distribution to create the best final hypothesis in classification. Our trial will use the heart failure clinical records dataset, which contains 13 features of cardiac patients. Performance metrics are measured through precision, recall, f-measure, accuracy, and the ROC curve. Results from the experiment found that the proposed method had high performance compared to naïve bayes, k-NN,and decision tree, and outperformed other ensembles including bagging, logitBoost, LPBoost, and adaboost.m1, with an accuracy of 98.89%, and classified the capabilities of patients who died accurately as well compared to decision tree and bagging, which were completely indistinguishable. The findings of this study found that LEBoosting was able to maximize error reductions in the weak learner's training process from any weak learner to maximize the effectiveness of cardiology classifiers and to provide theoretical guidance to develop a model for analysis and prediction of heart disease. The novelty of this research is to improve original ensemble learning by finding the weak learner with the lowest error in order to update the best distribution to the final hypothesis, which will give LEBoosting the highest classification efficiency. Doi: 10.28991/ESJ-2023-07-01-010 Full Text: PDF
Bank Concentration and Bank Stability during the COVID-19 Pandemic Sukisno Selamet Riadi; Michael Hadjaat; Rizky Yudaruddin
Emerging Science Journal Vol 6 (2022): Special Issue "COVID-19: Emerging Research"
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/esj-2022-SPER-018

Abstract

Objectives: The banking sector has been impacted more negatively by the COVID-19 pandemic. At the same time, bank concentration and capitalization stabilize banking systems during times of crisis. This study evaluated the monthly financial reports of all commercial banks in Indonesia to investigate the joint impact of the COVID-19 pandemic and bank concentration on bank stability. Moreover, this study was conducted to determine whether adequate capitalization could enhance the positive effect of the interaction between COVID-19 and bank concentration during the pandemic. Methods/Analysis: Using 108 commercial banks between March 2020 and May 2021, data were analyzed using the fixed-effects estimator with heteroskedasticity and within-panel serial correlations for robust standard errors. Several robustness checks were performed to ensure that the results were accurate and consistent. Findings: Subsequently, the impact of the pandemic and bank concentration was determined to be significant and adverse, though their interplay was strong enough to promote bank stability. This highlights the importance of adequate capitalization in enhancing the beneficial effects of the interaction between COVID-19 and bank concentration on bank stability. Novelty /Improvement: Hence, these findings contribute to the literature on bank stability and have important policy implications for the banking sector during this pandemic.JEL Classifications: E51, G20, G21. Doi: 10.28991/esj-2022-SPER-018 Full Text: PDF
Optimization of Markov Weighted Fuzzy Time Series Forecasting Using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) Sugiyarto Surono; Khang Wen Goh; Choo Wou Onn; Afif Nurraihan; Nauval Satriani Siregar; A. Borumand Saeid; Tommy Tanu Wijaya
Emerging Science Journal Vol 6, No 6 (2022): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2022-06-06-010

Abstract

The Markov Weighted Fuzzy Time Series (MWFTS) is a method for making predictions based on developing a fuzzy time series (FTS) algorithm. The MWTS has overcome certain limitations of FTS, such as repetition of fuzzy logic relationships and weight considerations of fuzzy logic relationships. The main challenge of the MWFTS method is the absence of standardized rules for determining partition intervals. This study compares the MWFTS model to the partition methods Genetic Algorithm-Fuzzy K-Medoids clustering (GA-FKM) and Fuzzy K-Medoids clustering-Particle Swarm Optimization (FKM-PSO) to solve the problem of determining the partition interval and develop an algorithm. Optimal partition optimization. The GA optimization algorithm’s performance on GA-FKM depends on optimizing the clustering of FKM to obtain the most significant partition interval. Implementing the PSO optimization algorithm on FKM-PSO involves maximizing the interval length following the FKM procedure. The proposed method was applied to Anand Vihar, India’s air quality data. The MWFTS method combined with the GA-FKM partitioning method reduced the mean absolute square error (MAPE) from 17.440 to 16.85%. While the results of forecasting using the MWFTS method in conjunction with the FKM-PSO partition method were able to reduce the MAPE percentage from 9.78% to 7.58%, the MAPE percentage was still 9.78%. Initially, the root mean square error (RMSE) score for the GA-FKM partitioning technique was 48,179 to 47,01. After applying the FKM-PSO method, the initial RMSE score of 30,638 was reduced to 24,863. Doi: 10.28991/ESJ-2022-06-06-010 Full Text: PDF
Immobilization and Stabilization of Aspergillus Fumigatus α-Amylase by Adsorption on a Chitin Yandri Yandri; Ezra Rheinsky Tiarsa; Tati Suhartati; Bambang Irawan; Sutopo Hadi
Emerging Science Journal Vol 7, No 1 (2023): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-01-06

Abstract

In this research, immobilization of A. fumigatus α-amylase on chitin was studied with the main purpose to improve the characteristics of the enzyme. A series of experiments were carried out to study stability improvement, thermodynamic parameters, include ki, ΔGi, and t½, and reusability of the immobilized enzyme. The experimental results indicate that significant thermal stability was achieved, as indicates by the ability of the enzyme to retain its relative activity above 39% after 80 min of incubation at 60oC. Thermodynamic parameters, include ki, ΔGi, and t½, indicate that the immobilized enzyme is more rigid, stable, and less flexible in the water, resulting in increased stability up to 1.5 times compared to that of the native enzyme. Furthermore, the immobilized enzyme was able to retain over 46% of its initial activity after six consecutive applications for starch hydrolysis, confirming the potential of chitin for the production of immobilized enzymes on an industrial scale. Doi: 10.28991/ESJ-2023-07-01-06 Full Text: PDF
Flipped Learning and E-Learning as Training Models Focused on the Metaverse Jesús López-Belmonte; Santiago Pozo-Sánchez; Noemí Carmona-Serrano; Antonio-José Moreno-Guerrero
Emerging Science Journal Vol 6 (2022): Special Issue "Current Issues, Trends, and New Ideas in Education"
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2022-SIED-013

Abstract

Virtual Learning Environments (EVA) have acquired special importance in the educational field in recent years. The metaverse has been constructed as a learning space with enormous potential. As such, the immersion possibilities of the metaverse increase when compared to other methodologies that already implement technology, such as flipped learning and e-learning. In these learning environments, students require a set of specific abilities and skills. Therefore, this study aims to understand which training approach (flipped learning or e-learning) helps students acquire better skills through a teaching and learning process in the metaverse. This thesis used a pre-post quasi-experimental design of a group containing 173 Spanish high school students to achieve its aim. The data collection has been carried out by the Teaching and Learning Experiences Questionnaire (ETLQ). Among the obtained results, it is discovered that in all the dimensions analyzed, a significant relationship is observed. The greatest difference in means occurs in the LO dimension, meaning that these educational experiences directly impact the student’s academic results. It is concluded that both training approaches are adequate in preparing students for training processes carried out in the metaverse since they complement each other. Therefore, as preliminary instruction, the sequential use of these approaches is necessary when familiarizing students with a new learning reality such as the metaverse. Doi: 10.28991/ESJ-2022-SIED-013 Full Text: PDF
Ensuring Healthcare Efficiency in the Context of the Medical and Pharmaceutical Staff Regulation Guzal G. Galiakbarova; Yenlik N. Nurgaliyeva; Elmira B. Omarova; Svetlana B. Zharkenova; Muslim K. Khassenov
Emerging Science Journal Vol 6, No 6 (2022): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2022-06-06-05

Abstract

This article aims to substantiate the impact of socio-economic labor protection for healthcare professionals based on developing legislative regulations in some OECD and EAEU countries and identifying their relationship with the efficiency of healthcare systems. The methodology includes general scientific methods (systemic analysis, synthesis, comparison, abstraction, induction, deduction, and modeling) and special research methods (formal logical, structural, and functional). The results of international rankings evaluating healthcare systems were used to determine the list of states for comparative legal analysis. Also, empirical methods were used: meetings, questionnaire surveys, and interviews held in 2021 with medical and pharmaceutical workers in Kazakhstan. The research results showed that states with special labor regulations for medical and pharmaceutical personnel occupy stable leading positions in international rankings regarding healthcare evaluation. On the other hand, based on the example of the EAEU countries with an insufficient level of specialization in labor regulation for these categories of workers, some states occupy weak positions in similar international ratings. This paper is novel because previously, there was no debate in the literature justifying the finding that specifics in the labor regulation of medical and pharmaceutical staff, along with other factors, influence the healthcare system's efficiency and development. Doi: 10.28991/ESJ-2022-06-06-05 Full Text: PDF
A Comparative Study of Collaborative Filtering in Product Recommendation Agori Argyro Patoulia; Athanasios Kiourtis; Argyro Mavrogiorgou; Dimosthenis Kyriazis
Emerging Science Journal Vol 7, No 1 (2023): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-01-01

Abstract

Product recommendation is considered a well-known technique for bringing customers and products together. With applications in music, electronic shops, or almost any platform the user daily deals with, the recommendation system’s sole scope is to help customers and attract new ones to discover new products. Through product recommendation, transaction costs can also be decreased, improving overall decision-making and quality. To perform recommendations, a recommendation system must utilize customer feedback, such as habits, interests, prior transactions as well as information used in customer profiling, and finally deliver suggestions. Hence, data is the key factor in choosing the appropriate recommendation method and drawing specific suggestions. This research investigates the data challenges of recommendation systems, specifying collaborative-based, content-based, and hybrid-based recommendations. In this context, collaborative filtering is being explored, with the Surprise library and LightFM embeddings being analysed and compared on top of foodservice transactional data. The involved algorithms’ metrics are being identified and parameterized, while hyperparameters are being tuned properly on top of this transactional data, concluding that LightFM provides more efficient recommendation results following the evaluation’s precision and recall outcomes. Nevertheless, even though the Surprise library outperforms, it should be used when constructing user-friendly models, requiring low code and low technicalities. Doi: 10.28991/ESJ-2023-07-01-01 Full Text: PDF
The Quality of E-Government Management, Information Security and Quality Aferdita Qekaj-Thaçi; Ledri Thaçi
Emerging Science Journal Vol 7, No 1 (2023): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-01-016

Abstract

The purpose of the presented paper was to present the state of e-government services in the Republic of Kosovo within the management of electronic services and the role of ICT in data security and quality. E-government is an important sphere in Kosovo, which has been shown to have positively influenced the provision of services to all stakeholders, taking care of the security and quality of these services. This paper belongs to the qualitative and quantitative types, where a total of 115 subjects, experts, and users of e-government services participated and gave their opinions against these services. A literature review was used to process qualitative data, while a questionnaire was used to process quantitative data with experts and users of e-government services. From the results of the research, we understood that the level of use of e-government services in the Republic of Kosovo is high and that there is a high level of data security and quality of services, where the role of ICT in the management of e-data-government is essential and very important. It is recommended that, according to the results of this study, direct access to the management of these services should be considered and offered to ensure the sustainability of e-government services in the Republic of Kosovo. Doi: 10.28991/ESJ-2023-07-01-016 Full Text: PDF
Deep Learning in Predicting High School Grades: A Quantum Space of Representation Ricardo Costa-Mendes; Frederico Cruz-Jesus; Tiago Oliveira; Mauro Castelli
Emerging Science Journal Vol 6 (2022): Special Issue "Current Issues, Trends, and New Ideas in Education"
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2022-SIED-012

Abstract

This paper applies deep learning to the prediction of Portuguese high school grades. A deep multilayer perceptron and a multiple linear regression implementation are undertaken. The objective is to demonstrate the adequacy of deep learning as a quantitative explanatory paradigm when compared with the classical econometrics approach. The results encompass point predictions, prediction intervals, variable gradients, and the impact of an increase in the class size on grades. Deep learning’s generalization error is lower in the student grade prediction, and its prediction intervals are more accurate. The deep multilayer perceptron gradient empirical distributions largely align with the regression coefficient estimates, indicating a satisfactory regression fit. Based on gradient discrepancies, a student’s mother being an employer does not seem to be a positive factor. A benign paradigm shift concerning the balance between home and career affairs for both genders should be reinforced. The deep multilayer perceptron broadens the spectrum of possibilities, providing a quantum solution hinged on a universal approximator. In the case of an academic achievement-critical factor such as class size, where the literature is neither unanimous on its importance nor its direction, the multilayer perceptron formed three distinct clusters per the individual gradient signals. Doi: 10.28991/ESJ-2022-SIED-012 Full Text: PDF

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