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Electronic Journal of Education, Social Economics and Technology
Published by SAINTIS Publishing
ISSN : -     EISSN : 27236250     DOI : https://doi.org/10.33122/ejeset
Core Subject : Religion, Economy, Humanities, Health, Science, Education, Art, Social, Engineering,
Religion Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Nursing Public Health Social Sciences Veterinary Other
Electronic Journal of Education, Social Economics and Technology (eJESET) with ISSN 2723-6250 (online) is a open-access, peer-reviewed multidisciplinary international journal. The journal aims to provide an international platform for researchers, professionals and scientists for solve of problems with multidisciplinary approaches on all topics related to educational, social science, economics and technology to exchange, sharing and disseminate theoretical of current research results as widely as possible. Electronic Journal of Education, Social Economics and Technology (eJESET) publishes the latest research results in multidisciplinary approaches on all topics related to educational, social science, economics and technology. First published in 2020. The Journal is published biannually and is available in open access electronic version.
Arjuna Subject : Ilmu Sosial - Ilmu Sosial (Lain-Lain)
Articles 613 Documents
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Waste Analysis Using Value Stream Mapping (VSM) And FMEA In The Implementation Of Lean Manufacturing On The Woven Bag Production Line At PT. Kerta Rajasa Raya Dewi Mardiana Aditya; Joumil Aidil Saifuddin; Yekti Condro Winursito
Electronic Journal of Education, Social Economics and Technology Vol 7, No 1 (2026)
Publisher : SAINTIS Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33122/ejeset.v7i1.666

Abstract

This research aims to analyze and reduce waste in the woven bag production line at PT Kerta Rajasa Raya through the Lean Manufacturing approach using the Value Stream Mapping (VSM) and Failure Mode and Effect Analysis (FMEA) methods. The company experienced various types of waste such as waiting time, unnecessary movement, over-processing, and product defects, which led to a decrease in production efficiency. The analysis results showed that the highest types of waste were unnecessary motion, defects, and excess processing. Using VSM, it was found that of the total production time of 51,997 seconds, only 51.1% were value-added activities. Through the application of FMEA, the main causes of waste were identified based on the highest Risk Priority Number (RPN) value, and a number of improvement recommendations were designed such as improving work layout, implementing quality gate, and strengthening preventive maintenance. After the improvement, production time was reduced to 42,139 seconds and process cycle efficiency increased to 63.1%. These results show that the application of Lean Manufacturing through VSM and FMEA is effective in improving production efficiency and reducing waste in the company.
Application of the Decision Tree Algorithm for Early Detection of Heart Disease Based on IoT Rosa Englina Silaban; Ridho Maulana Siregar; Natasya Aulia Angkat; Mhd. Raihan M. Manurung; Achmad Ridwan
Electronic Journal of Education, Social Economics and Technology Vol 7, No 1 (2026)
Publisher : SAINTIS Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33122/ejeset.v7i1.1048

Abstract

Heart disease is one of the leading causes of death worldwide, accounting for 32% of all global deaths. Technological developments, particularly in the Internet of Things (IoT), enable real-time monitoring of heart health and early warning alerts. This study aims to implement a Decision Tree algorithm to classify patient conditions based on vital parameters, including BPM, SpO₂, systolic and diastolic blood pressure, and body temperature. The model was trained using a vital parameter dataset and evaluated using a confusion matrix, ROC curve, and feature importance. Test results show that the Decision Tree model achieves an accuracy of 85% with a macro-AUC value of 0.448. These results prove that the Decision Tree algorithm can be used for patient condition classification with reasonably good performance, although the model still tends to make prediction errors in some minority classes.
The Influence of Cognitive Factor and Technology Dimension on Behavioral Intention and Its Impact on Actual Behavior in Digital Banking: Evidence from Bank Jatim Kediri City Angga Rizka Lidiawan; Nur Laely; Ana Komari; Djunaedi Djunaedi
Electronic Journal of Education, Social Economics and Technology Vol 7, No 1 (2026)
Publisher : SAINTIS Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33122/ejeset.v7i1.1408

Abstract

As a leading regional development bank, Bank Jatim continues to strengthen its reputation through digital transformation initiatives, numerous national awards, asset growth reaching IDR 118.15 trillion (16.71% YoY), and support for regional digitalization (P2DD) and inclusive MSME development. Financial performance during 2016–2020 demonstrated significant growth, with assets increasing from IDR 43 trillion to IDR 83 trillion (±94%), third-party funds increasing by approximately 109%, and net profit growing by around 45%, reflecting financial stability and enhanced customer trust. This study aims to analyze the influence of Cognitive Factor and Technology Dimension on Behavioral Intention and its implications for Actual Behavior in digital banking usage among customers of Bank Jatim Kediri City. Using an explanatory quantitative approach with SEM-PLS and 173 respondents, the findings reveal that Cognitive Factor has the most dominant direct effect on Actual Behavior (β = 0.544; p 0.001; f² = 0.603), followed by Technology Dimension (β = 0.381; p 0.001; f² = 0.221). Behavioral Intention does not significantly affect Actual Behavior (β = 0.032; p = 0.645), indicating that it does not function as a mediator. The model demonstrates good fit (SRMR = 0.077; NFI = 0.838) and predictive relevance (Q² = 0.528). Theoretically, this study contributes by demonstrating that in the context of regional banking, trust and perceived risk play a more decisive role in shaping actual behavior than intention as a mediating variable. Practically, the findings recommend strategies focused on strengthening trust, enhancing digital security, providing assisted onboarding, and optimizing user experience to accelerate digital banking adoption
User Engagement Patterns in Viral Social Media Content: A Multinational Comparative Study Based on Interaction Ratios and Data Visualization Robi Rojaya Simbolon; Sarah Fauziah Saefudin; Serani Arta Mauli Silalahi; Akhmad Bakhrun
Electronic Journal of Education, Social Economics and Technology Vol 6, No 2 (2025)
Publisher : SAINTIS Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33122/ejeset.v6i2.704

Abstract

This study explores user engagement patterns in viral social media content through a data visualization dashboard built with Power BI. The dataset comprises 5,000 viral posts across eight countries and four major platforms—Instagram, TikTok, X (Twitter), and YouTube—encompassing ten content hashtags. The analysis covers over 13 billion total views, with an average of 2.56 million views per post and an overall engagement rate of 22.27%. By visualizing metrics such as likes, comments, shares, and views, the dashboard enables multi-dimensional filtering and correlation analysis. The strongest finding is a perfect correlation (CC = 1.00) between views and all engagement types when filtered by content type, highlighting the pivotal role of format (e.g., YouTube Shorts, Photo posts) in driving interactions. High correlations were also found regionally, such as views and comments (CC = 0.92), and views and shares (CC = 0.91), suggesting significant influence of geographic and cultural factors.Further insights show that YouTube leads with 76.29% of total engagements in Brazil, while TikTok and Instagram dominate in the USA. Hashtags also contribute meaningfully, with view-comment correlation reaching 0.88. This dashboard proves valuable not only for tracking metrics but for generating actionable insights to inform content strategy, platform prioritization, and regional targeting. The findings affirm that virality is not incidental but influenced by measurable factors, making data-driven decisions essential for digital success.
Hoaxes and Headlines: Comparing News Framing and Political Narratives in Social Media Fohan Muzakir; Rizkha Maulana; Linda Handayani; Yusri Yusri; Razami Razami
Electronic Journal of Education, Social Economics and Technology Vol 7, No 1 (2026)
Publisher : SAINTIS Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33122/ejeset.v7i1.1117

Abstract

The development of social media has accelerated the spread of hoax news, especially in the context of political narratives. The framing used in hoax news often differs from that used in mainstream journalism, creating polarization of public opinion. This study aims to examine the differences in framing between hoax news and news from mainstream media, and their impact on public political perceptions. By understanding these framing patterns, research can contribute to hoax mitigation strategies and improve media literacy. This study used qualitative methods with framing analysis and critical discourse analysis approaches. The results show contrasting framing differences: hoax news tends to use emotional, sensational, and manipulative framing, with provocative titles, hyperbolic language, and unclear sources to polarize audiences. In contrast, mainstream journalism uses informational and contextual framing, prioritizes facts, data, and credible sources, and strives to maintain the principle of balance. Furthermore, on social media, hoax framing spreads more quickly and widely due to algorithms that encourage engagement, creating echo chambers that strengthen beliefs and complicate factual correction.
Environmental Accounting Education and Entrepreneurial Character in the Green Economy masradin masradin
Electronic Journal of Education, Social Economics and Technology Vol 7, No 1 (2026)
Publisher : SAINTIS Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33122/ejeset.v7i1.986

Abstract

This study focuses on analyzing the impact of environmental accounting education on the green economy, character development, and entrepreneurship among college students, and examines how the green economy and character development function as a bridge. The high carbon emissions caused by business actors due to minimal environmental awareness, so universities have a role in the need for entrepreneurs, while universities have a role in developing entrepreneurs who understand environmentally friendly businesses. The research objects were taken from 366 students. The results showed that environmental accounting education had a positive and significant effect on the green economy and character building, but not significantly on students' entrepreneurial intentions. Understanding the green economy was proven to be able to significantly mediate environmental accounting education and students' entrepreneurial intentions, while character building did not play a mediating effect. The conclusion of this study indicates that universities implementing environmentally focused curricula help raise students' awareness of sustainable entrepreneurship. Based on this research, universities should continue integrating curricula with other disciplines and developing real-life entrepreneurial projects to produce entrepreneurs with character and an understanding of the fundamentals of a green economy.
Visualization of XYZ Sales Data Using Power BI: A Case Study of Superstore in the United States Agnia Amani; Fadila Rahmawati; Naura Nazhifah Suryana; Zahdam Azril Firmansyah; Akhmad Bakhrun
Electronic Journal of Education, Social Economics and Technology Vol 7, No 1 (2026)
Publisher : SAINTIS Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33122/ejeset.v6i1.702

Abstract

The Sample - Superstore dataset is a collection of retail sales transactions from a major retail chain in the United States, comprising 9,994 rows and 21 attributes. This dataset captures detailed information on individual orders, including order and shipping dates, customer names and segments, geographical locations, product categories and sub-categories, as well as financial metrics such as sales, discounts, and profits. The data spans various regions and customer types, providing a comprehensive overview of business operations across different market segments. The aim of this study is to visualize the Superstore sales data using Power BI, an advanced business intelligence and data visualization tool capable of transforming raw and complex data into clear, interactive, and insightful visual representations. The visualization process involves data loading, transformation using Power Query, model structuring, and dashboard creation. The final dashboards feature bar charts, pie charts, maps, and time-series graphs that present key insights such as regional sales distribution, category-wise profit contributions, customer segmentation analysis, and seasonal sales trends. These visuals reveal patterns in product performance, highlight profitable and underperforming segments, and help identify regional disparities in sales performance. Additionally, the time-based charts provide a better understanding of sales trends throughout the year, offering valuable input for strategic planning and forecasting. Overall, the findings suggest that Power BI is highly effective in making data more accessible and actionable, supporting more informed and accurate decision-making processes for businesses aiming to optimize performance and enhance market responsiveness.
Communication Patterns of Risky Wedding Organizer Organizations in Improving Service Quality Gandung Satriyono; Priyo Prasetyo; Muh Lutfi Raid H; Johan Prasetyo
Electronic Journal of Education, Social Economics and Technology Vol 7, No 1 (2026)
Publisher : SAINTIS Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33122/ejeset.v7i1.1144

Abstract

This study aims to analyze the organizational communication patterns applied by Wedding Organizer (WO) Risky in its efforts to improve the quality of service to clients. The background of this study is based on the importance of organizational communication as an instrument for coordination, task allocation, and effective decision-making, especially in the service industry that demands speed, accuracy, and professionalism. This study uses a qualitative descriptive method with data collection techniques through semi-structured interviews, observation, and documentation of five key informants consisting of the owner, event coordinator, head of the decoration division, head of the documentation division, and field staff. The results show that WO Risky applies a combinational communication pattern, including vertical, horizontal, and diagonal communication, with a combination of formal and informal communication. The use of digital media such as WhatsApp Groups facilitates coordination and accelerates the delivery of information between divisions. Open, participatory, and two-way communication has been proven to increase work effectiveness, prevent miscommunication, and strengthen client satisfaction. Despite obstacles such as delays in information and lack of documentation, a communication strategy based on reconfirmation, regular briefings, and post-event evaluations successfully maintained effective coordination. Thus, effective organizational communication is the main foundation in improving the quality of service and professional reputation of WO Risky. 
Investment Decisions: Comparative Analysis of the Performance of Gold, Stocks and Bonds Friska Fauziah Umardhi
Electronic Journal of Education, Social Economics and Technology Vol 7, No 1 (2026)
Publisher : SAINTIS Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33122/ejeset.v%vi%i.1342

Abstract

Investors currently face significant difficulties in making optimal investment decisions that align with targeted profits due to limited information and challenges in analyzing performance, returns, and risks across different investment instruments. Existing studies tend to focus on single assets or partial indicators, resulting in a research gap concerning comprehensive comparative analysis of major investment instruments. This study aims to analyze and compare investment decisions based on the performance of gold, stocks, and bonds by examining differences in returns, risks, and overall investment performance. This research employs a quantitative comparative approach using secondary time-series data. The data consist of monthly gold closing prices, LQ45 stock index data, and Indonesian government bonds with a ten-year tenor from 2018 to 2023. Hypothesis testing was conducted to examine differences in return, risk, and performance measured using the Sharpe ratio. The results show that there are statistically significant differences in returns among gold, stocks, and bonds, as indicated by significance values below 0.05. Similarly, significant differences were found in risk levels and investment performance as measured by the Sharpe ratio across the three instruments. These findings indicate that each investment instrument exhibits distinct characteristics in terms of return, risk, and performance. In conclusion, investment decisions should be based on a comprehensive evaluation of performance indicators rather than relying on a single metric. This study contributes theoretically by enriching comparative investment literature and practically by providing investors with empirical insights to support more rational and informed investment decisions. Future research is recommended to incorporate additional instruments and longer observation periods.
Bike Sales Analysis for Understanding Market Trends in Europe via Power BI Dashboard Indriana Kurnia Cahyati; Fitri Nur Waliyaden; Gloria Theodora Wahi Leo; Mery Adelia; Akhmad Bakhrun
Electronic Journal of Education, Social Economics and Technology Vol 6, No 2 (2025)
Publisher : SAINTIS Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33122/ejeset.v6i2.698

Abstract

In the Industry 4.0 era, transforming raw transactional data into strategic insights is vital for maintaining competitiveness. This study leverages Microsoft Power BI to analyze 113,037 bicycle sales records from 2011 to 2016 across Europe using descriptive quantitative methods and interactive dashboards. The analysis reveals that accessories account for the majority of unit sales (77.94%) due to their affordability and repurchase frequency, while high-end bicycles such as Road-150 Red, 62 and Mountain-200 Black, 38 contribute the highest revenue per unit. The adult age group (35–64 years) emerges as the most profitable segment, generating USD 45 million in revenue and USD 18 million in profit. A strong positive correlation (r = 0.87) between age and product price underscores the purchasing power of older consumers. Geographically, the United States dominates with 34.68% of customers and USD 15.9 million in revenue, followed by Australia and Canada. Meanwhile, Europe shows promising potential for future growth. The gender distribution is nearly balanced, with both male and female customers favoring accessories—highlighting opportunities for inclusive and gender-neutral marketing. Power BI’s visualization tools—bar charts, scatter plots, map views, and forecasting—enable dynamic trend analysis and strategic planning. This study contributes academically by enriching the literature on visual analytics in retail and practically by offering a replicable framework for data-driven decision-making in the bicycle market and other consumer goods industries.

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