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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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Utilizing Machine Learning to Reassess the Predictability of Bank Stocks Hera Antonopoulou; Leonidas Theodorakopoulos; Constantinos Halkiopoulos; Vicky Mamalougkou
Emerging Science Journal Vol 7, No 3 (2023): June
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-03-04

Abstract

Objectives: Accurate prediction of stock market returns is a very challenging task due to the volatile and non-linear nature of the financial stock markets. In this work, we consider conventional time series analysis techniques with additional information from the Google Trend website to predict stock price returns. We further utilize a machine learning algorithm, namely Random Forest, to predict the next day closing price of four Greek systemic banks. Methods/Analysis: The financial data considered in this work comprise Open, Close prices of stocks and Trading Volume. In the context of our analysis, these data are further used to create new variables that serve as additional inputs to the proposed machine learning based model. Specifically, we consider variables for each of the banks in the dataset, such as 7 DAYS MA,14 DAYS MA, 21 DAYS MA, 7 DAYS STD DEV and Volume. One step ahead out of sample prediction following the rolling window approach has been applied. Performance evaluation of the proposed model has been done using standard strategic indicators: RMSE and MAPE. Findings: Our results depict that the proposed models effectively predict the stock market prices, providing insight about the applicability of the proposed methodology scheme to various stock market price predictions. Novelty /Improvement: The originality of this study is that Machine Learning Methods highlighted by the Random Forest Technique were used to forecast the closing price of each stock in the Banking Sector for the following trading session. Doi: 10.28991/ESJ-2023-07-03-04 Full Text: PDF
STEM Talent: A Game Changer in Organizational Digital Transformation Piyawat Jriyasetapong; Supaporn Kiattisin; Smitti Darakorn Na Ayuthaya
Emerging Science Journal Vol 7, No 3 (2023): June
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-03-020

Abstract

Although organizational digital transformation (ODT) is implemented globally, Thailand and the Lao People's Democratic Republic do not possess the right factors for success under the tech-no-socio-economic paradigm. Organizations must modernize their capital resources, particularly their talent, in order to become agile, competitive, and resilient in the digital era. In this research, we identify and validate by proposing talent success factors and a framework for enabling and promoting ODT in Thailand and the Lao People's Democratic Republic. The statistical population consisted of 410 individuals who were observed in their digital businesses. Confirmatory factor analysis (CFA) shows that a four-factor model fits. The most influential factor for ODT was found to be transdisciplinary ontology talent (TOT), followed by mental model talent (MMT), enterprise architecture talent (EAT), and strategic agile talent (SAT). The findings demystified the four factors, entitled "STEM talent," in a comprehensive framework and its artifacts while explaining their respective influences. The article proposes a STEM talent and its framework for ODT with high potential, including but not limited to Thailand and the Lao People's Democratic Republic. Doi: 10.28991/ESJ-2023-07-03-020 Full Text: PDF
The Phenomena of Learning Loss Experienced by Elementary School Students during the Covid-19 Post Pandemic I Wayan Kertih; I Wayan Widiana; I Gede Wahyu Suwela Antara
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-014

Abstract

The COVID-19 pandemic caused most students to experience learning loss after joining online learning for almost 2 years. Learning loss is a student's condition experiencing learning setbacks academically due to the factors of the non-sustainable educational process. The study aimed at analyzing the learning loss phenomenon experienced by elementary school students in the COVID-19 post-pandemic situation. The study used a descriptive qualitative approach. The population of the study was 401,321 distributed students, and the sample was gained through conducting two sampling techniques, such as random cluster sampling and accidental sampling, with a final sample of 1,104 students. The data collection methodologies conducted were interview, observation, and questionnaire. The questionnaire used contained 15 questions that had been validated before. The data analysis of the study was conducted interactively using 3 techniques, such as data reduction, data display, and drawing conclusions. The descriptive analysis was conducted by using the Guttman scale with the percentage statistic technique. The result of the study showed that there were learning loss phenomena in the post-pandemic situation, a condition seen from the researched dimensions in the low and medium categories. Moreover, a high level of learning loss was found in the rural area. This was observed from several things, such as the learning facilities provided, the parent's role, and the learning methodologies conducted. To resolve learning loss, we need to participate all parties in the learning process, which in this context means mending a good relationship with the community and parents to improve the learning quality, which is assisted with innovative learning models. Doi: 10.28991/ESJ-2023-SPER-014 Full Text: PDF
Demystifying Tourists’ Intention to Visit Destination on Travel Vlogs: Findings from PLS-SEM and fsQCA Pantas H. Silaban; Wen-Kuo Chen; Bernard E Silaban; Andri Dayarana K. Silalahi; Ixora Javanisa Eunike; Hanna Meilani Damanik
Emerging Science Journal Vol 7, No 3 (2023): June
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-03-015

Abstract

With the advent of digital technologies (i.e., social media), tourism has evolved its marketing strategies. Even though published literature discusses the importance of tourism content on social media from various consumer perspectives, much more work must be done to examine how consumers make travel decisions based on tourism content. This study proposes a model for analyzing travel intent based on consumer motivations (e.g., novelty, entertainment, and relaxation) to watch social media travel videos. Consumers' travel intentions are influenced by trust and parasocial relationships. Through an online survey, 215 responses were collected and analyzed using a structural equation modeling (SEM) approach using Smart-PLS 3.0 and fuzzy set qualitative comparative analysis (fsQCA). In the study, relaxation ranked most highly among the three motivations for viewers to watch travel videos on YouTube for building parasocial relationships. In contrast, consumers seeking entertainment are more likely to form trust, which will result in consumers' intentions to travel. Based on intermediate solutions generated by the fsQCA, two causal configurations can be used to explain consumer travel decisions influenced by social media tourism content. The study also discusses theoretical and practical guidelines in depth. Doi: 10.28991/ESJ-2023-07-03-015 Full Text: PDF
Eco-Innovation and SME Performance in Time of Covid-19 Pandemic: Moderating Role of Environmental Collaboration Gusti N. Achmad; Rizky Yudaruddin; Puput W. Budiman; Eka Nor Santi; . Suharsono; Adi H. Purnomo; Noor Wahyuningsih
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-018

Abstract

Objectives: All businesses worldwide, especially small and medium-sized organizations, are now concerned about environmental degradation. Eco-innovation and environmental collaboration are expected to be the driving forces for saving the environment and the performance of companies. Therefore, this study aimed to ascertain how eco-innovation and environmental cooperation affect the financial, social, and environmental performance of SMEs. This study also explored environmental collaboration as a moderating variable for the effect of eco-innovation on the performance of SMEs. Methods/Analysis: Data from 300 small and medium-sized enterprises of Creative Home Décor were analyzed using structural equation modeling. Findings: Eco-innovation is necessary to improve the performance of Indonesia's SMEs. Environmental collaboration has a beneficial and substantial effect on the performance of the environment and society. Regarding environmental collaboration as a moderating variable, this study identified a positive and statistically significant coefficient regulating the relationship between financial performance and eco-innovation. Novelty /Improvement. The novelty of this research lies in its focus on the impact of eco-innovation and environmental collaboration on the performance of SMEs, particularly in developing countries such as Indonesia, during the COVID-19 pandemic. The study also contributed to the theoretical and empirical understanding of eco-innovation in developing countries and highlighted the importance of environmental collaboration in enhancing the social and environmental performance of SMEs. Additionally, this paper provided empirical and theoretical contributions on the role of environmental collaboration as a moderating variable that is particularly improving the performance of Indonesia's SMEs in Creative Home Décor during the COVID-19 pandemic.JEL Classifications: M12, L68, L25, L53, Q56 Doi: 10.28991/ESJ-2023-SPER-018 Full Text: PDF
Enhancing Learning Object Analysis through Fuzzy C-Means Clustering and Web Mining Methods Meryem Amane; Karima Aissaoui; Mohammed Berrada
Emerging Science Journal Vol 7, No 3 (2023): June
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-03-010

Abstract

The development of learning objects (LO) and e-pedagogical practices has significantly influenced and changed the performance of e-learning systems. This development promotes a genuine sharing of resources and creates new opportunities for learners to explore them easily. Therefore, the need for a system of categorization for these objects becomes mandatory. In this vein, classification theories combined with web mining techniques can highlight the performance of these LOs and make them very useful for learners. This study consists of two main phases. First, we extract metadata from learning objects, using the algorithm of Web exploration techniques such as feature selection techniques, which are mainly implemented to find the best set of features that allow us to build useful models. The key role of feature selection in learning object classification is to identify pertinent features and eliminate redundant features from an excessively dimensional dataset. Second, we identify learning objects according to a particular form of similarity using Multi-Label Classification (MLC) based on Fuzzy C-Means (FCM) algorithms. As a clustering algorithm, Fuzzy C-Means is used to perform classification accuracy according to Euclidean distance metrics as similarity measurement. Finally, to assess the effectiveness of LOs with FCM, a series of experimental studies using a real-world dataset were conducted. The findings of this study indicate that the proposed approach exceeds the traditional approach and leads to viable results. Doi: 10.28991/ESJ-2023-07-03-010 Full Text: PDF
University Students’ Rejection to Learning Statistics: Research from a Latin American Standpoint Carlos Ramos-Galarza; Valentina Ramos; Jorge Cruz-Cárdenas; Mónica Bolaños-Pasquel
Emerging Science Journal Vol 7 (2023): 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-2023-SIED2-07

Abstract

Introduction: Negative beliefs, fear, avoidance behaviors, and superficial attitudes surrounding the learning of statistics create significant problems for university students in Latin America. Objective: To analyze the impact of fearful behavior, superficial work, and avoidance displayed by university students when it comes to statistics. Method: In this article, we give details about a quantitative research project carried out by two independent studies. The first (N = 310) focused on the development of a scale to assess negative beliefs, fears, and avoidance behaviors towards statistics, in which goodness of fit was determined in a 3-factor model. In the second study (N = 250), it was hypothesized that undergraduates perform superficially due to negative beliefs and avoidance behaviors when learning statistics. Findings: The proposed model explained 42% of the variance. In addition, in the analysis of the proposed mediation model, an adequate adjustment was found. In the discussion of this research project, the need to intervene in the negative beliefs, fears, and avoidance behaviors displayed by university students towards statistics is highlighted. Novelty:This research project explains why college students dislike or avoid learning statistics in depth. The findings will allow for a modification in the way statistics is taught so that Latin American professionals achieve better performance in this field. Doi: 10.28991/ESJ-2023-SIED2-07 Full Text: PDF
Anticancer Activity Study of Modified Artocarpin Compound from Pudau Plant (Artocarpus kemando Miq.) Tati Suhartati; Khalimatus Sa’diah; Yandri Yandri; Sutopo Hadi
Emerging Science Journal Vol 7, No 3 (2023): June
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-03-05

Abstract

This research is a continuation of the successful isolation of artocarpin from the root of Artocarpus kemando Miq reported in our previous study. In the previous study, the artocarpin was characterized with UV-Vis and FTIR techniques. In this follow-up investigation, the artocarpin was subjected to a transesterification reaction using acetic anhydride and pyridine as catalysts, and the product of the reaction was specified as compound 1. The compound 1 was further characterized with different techniques to gain more complete data and then tested for anticancer activity test against P-388 murine leukemia cells. Characterizations of the compound 1 using 1H-NMR and 13C-NMR techniques suggest that the modification reaction resulted in the conversion of the -OH groups at C2' and 4' at the artocarpin molecule to -OOCH3, and based on the MS analysis, the compound was proposed to have the molecular formula of C30H32O8. Another important feature of compound 1 that should be noted is the significant improvement in stability compared to the unmodified artocarpin. Anticancer activity tests against P-388 murine leukemia cells revealed that compound 1 has an IC50of 2.35 µg/mL, confirming that the compound is categorized as an active anticancer agent and suggesting that the compound has promising potential that deserves further investigations. Doi: 10.28991/ESJ-2023-07-03-05 Full Text: PDF
Concentration of B-CG and sFlt-1 in Rattus Norvegicus Model of Preeclampsia with Swimming Exercise Treatment Oktalia Sabrida; Muslim Akmal; Sri Wahyuni; Khairan Khairan; Gholib Gholib
Emerging Science Journal Vol 7, No 3 (2023): June
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-03-021

Abstract

Preeclampsia (PE) is a life-threatening pregnancy complication for the mother and fetus. High concentrations of human chorionic gonadotrophin (hCG) and soluble fms-like tyrosine kinase-1 (sFLt-1) during pregnancy may have a role in the pathophysiology of PE. Swimming Exercise (SE) is one of the physical activities recommended for pregnant women and carries a minimal risk. This study is aimed at analyzing the interaction between the conditions of rats (normal and PE), the onset of PE (early onset and late onset), and the time of SE (SE 0 minutes; SE 5 minutes; SE 10 minutes) on the concentrations of B-CG and sFlt-1 in the Rattus norvegicus (R. norvegicus) model of PE. 72 R. norvegicus were included in this study and divided into 12 experimental groups (each group n = 6 individuals). R. norvegicus PE was prepared by inducing L-Nitro-Arginine-Methyl Ester (L-NAME) at a 75 mg/kg BW/day dose. The determination of PE was supported by the observation of differences in the values of urine protein (PU), urine glucose (GU), and urine leukocytes (LU) in R. norvegicus before and after injection of L-NAME. The three-factorial statistical test showed a significant interaction between the concentration of B-CG and the condition of R. norvegicus, the onset of PE, and the time of SE, with a p-value <0.001. The three-factorial statistical test also showed a significant interaction between the sFLt-1 concentration and the condition of R. norvegicus, the onset of PE, and the time of SE with p<0.05. The difference in the concentration of B-CG and sFLt-1 R. norvegicus in each treatment group was influenced by the condition of the rats (normal and PE), the onset of PE (early onset and late onset), and the time of SE (SE 0 minutes; SE 5 minutes; SE 10 minutes). Research related to SE on PE still needs to be continued to decide on recommendations on whether SE can be used as a preventive measure in complementary midwifery care for preventing and reducing symptoms of PE in pregnancy. Doi: 10.28991/ESJ-2023-07-03-021 Full Text: PDF
A Proposed Framework of Knowledge Management for COVID-19 Mitigation based on Big Data Analytic Mardhani Riasetiawan; Ahmad Ashari
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-015

Abstract

The COVID-19 pandemic has highlighted the importance of effective knowledge management in mitigating the impact of public health crises. Big data analytics can play a critical role in providing insights and informing decision-making during a pandemic. However, the challenges associated with collecting, analyzing, and managing the data, especially with privacy and security concerns, make it a complex task. This paper proposes a knowledge management framework for COVID-19 mitigation using a big data analytics approach. The framework includes a systematic process for data collection, analysis, and dissemination, as well as a set of best practices for knowledge management. Additionally, the framework complies with data protection and privacy regulations. The proposed framework aims to support public health officials and other stakeholders in effectively managing the COVID-19 pandemic by providing timely and accurate information. It can also be adapted and applied to other public health crises and be a useful tool for addressing the challenges associated with big data analytics in the context of public health. The paper presents the proposed framework in detail and provides components of how the framework can be applied to COVID-19 in Indonesia. Doi: 10.28991/ESJ-2023-SPER-015 Full Text: PDF

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