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The Eastasouth Journal of Information System and Computer Science
Published by Eastasouth Institute
ISSN : 30266041     EISSN : 3025566X     DOI : https://doi.org/10.58812/esiscs
Core Subject : Science,
Computer Science & IT
ESISCS - The Eastasouth Journal of Information System and Computer Science is a peer-reviewed journal and open access three times a year (April, August, December) published by Eastasouth Institute. ESISCS aims to publish articles in the field of Enterprise systems and applications, Database management systems, Decision support systems, Knowledge management systems, E-commerce and e-business systems, Business intelligence and analytics, Information system security and privacy, Human-computer interaction, Algorithms and data structures, Artificial intelligence and machine learning, Computer vision and image processing, Computer networks and communications, Distributed and parallel computing, Software engineering and development, Information retrieval and web mining, Cloud computing and big data. ESISCS accepts manuscripts of both quantitative and qualitative research. ESISCS publishes papers: 1) review papers, 2) basic research papers, and 3) case study papers. ESISCS has been indexed in, Crossref, and others indexing. All submissions should be formatted in accordance with ESISCS template and through Open Journal System (OJS) only.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 10 Documents
 1 
Search results for , issue "Vol. 1 No. 03 (2024): The Eastasouth Journal of Information System and Computer Science (ESISCS)" : 10 Documents clear
Ethics of Artificial Intelligence: Dialectics of Artificial Intelligence Policy for Humanity Syafuddin, Khairul
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 03 (2024): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v1i03.178

Abstract

Artificial Intelligence is now widely used by humans. The use of this technology is based on the view that Artificial Intelligence can make their lives easier. Many sectors have utilized this technology, including government, private, social, health, to education. Even though Artificial Intelligence is felt to have many benefits, there are perceived threats so that appropriate policies are needed. Thus, the aim of this research is to find out policies that can be recommended for the use of Artificial Intelligence that focus on humanitarian aspects. This research uses a qualitative approach to deepen the literature review that has been carried out. The results of this research show that the presence of Artificial Intelligence provides quite large benefits, especially as a technology for predicting the future. However, to regulate the use of this technology, appropriate policies are needed to avoid increasingly widespread digital crimes. In formulating Artificial Intelligence policies, humanitarian aspects need to be considered to provide appropriate protection. Especially for vulnerable groups who have low access to the use of Artificial Intelligence.
The Influence of Internet of Things (IoT) on Operational Efficiency and Competitive Advantage in the Information Technology Industry in Indonesia Judijanto, Loso; Triwiyatno, Aris; Sofyan, Sofyan
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 03 (2024): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v1i03.240

Abstract

This study investigates the impact of Internet of Things (IoT) adoption on operational efficiency and competitive advantage within the Information Technology (IT) industry in Indonesia. A quantitative research approach was employed, utilizing a cross-sectional survey design to collect primary data from 170 IT companies operating in Indonesia. Structural Equation Modeling (SEM) with Partial Least Squares (PLS) algorithm was utilized to analyze the data and test the research hypotheses. The findings reveal that IoT adoption positively influences both operational efficiency and competitive advantage within the Indonesian IT industry. These results underscore the transformative potential of IoT technologies in enhancing organizational performance and strategic positioning in the digital era. The study contributes to the existing literature by providing empirical evidence on the benefits of IoT adoption in the context of the Indonesian IT industry, offering insights for policymakers, practitioners, and researchers seeking to harness the potential of IoT technologies for sustainable growth and innovation.
Bibliometric Insights into the Evolution of Health Information Systems and Telemedicine Research Judijanto, Loso
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 03 (2024): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v1i03.241

Abstract

This bibliometric study provides a comprehensive analysis of the research landscape in Health Information Systems (HIS) and Telemedicine, elucidating key trends, influential factors, and thematic clusters shaping the field's evolution. Through systematic data collection and analysis of nearly a thousand scholarly articles, this research offers insights into publication output, citation patterns, collaboration networks, and thematic areas within HIS and Telemedicine research. Thematic network and temporal analyses reveal distinct clusters focusing on telehealth, HIS implementation, advanced telemedicine technologies, and contextual factors influencing telemedicine adoption. Highly cited papers and author collaboration networks further underscore seminal contributions and research clusters within the field. Overall, this study contributes to a deeper understanding of the current state of HIS and Telemedicine research, informing strategic initiatives for future advancements in healthcare technology and delivery.
Analyzing the Impact of Blockchain Technology on Transaction Security with a Bibliometric Perspective Judijanto, Loso; Gamaliel, Fritz
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 03 (2024): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v1i03.242

Abstract

This study presents a comprehensive bibliometric analysis aimed at assessing the impact of blockchain technology on transaction security. Spanning the period from 2016 to 2024, our analysis encompasses a total of 980 papers, revealing a profound scholarly interest and a rapidly evolving research landscape in this domain. Through quantitative metrics such as citation analysis, and qualitative insights from thematic, overlay, density, and author network visualizations, we delineate the key trends, challenges, and future directions in blockchain research. The findings indicate a marked emphasis on the decentralization, security, and transparency that blockchain offers, with significant applications across banking, energy trading, the Internet of Things (IoT), healthcare, and education sectors. Despite its promise, the study also identifies persistent challenges related to scalability, interoperability, privacy, and regulatory compliance that need addressing. By mapping the scholarly landscape, this research not only sheds light on the current state of blockchain technology in enhancing transaction security but also outlines potential avenues for future research, underscoring the technology's interdisciplinary impact and its evolving role in digital transactions.
The Influence of Business Analytics and Big Data on Predictive Maintenance and Asset Management Judijanto, Loso; Uhai, Sabalius; Suri, Ihsan
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 03 (2024): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v1i03.243

Abstract

This study investigates the impact of business analytics and big data on predictive maintenance and asset management practices within the energy industry in Indonesia. A quantitative research approach, utilizing a survey methodology, was employed to gather data from stakeholders representing various sectors of the energy industry. The study analyzed the relationships between business analytics, big data, predictive maintenance, and asset management using structural equation modeling (SEM) with Partial Least Squares (PLS) regression. The results indicate significant positive relationships between the utilization of business analytics and big data and various performance metrics, including asset reliability, operational efficiency, and cost savings. Furthermore, organizational factors such as leadership support and data quality were found to play a crucial role in facilitating the adoption and implementation of predictive maintenance strategies. The findings underscore the transformative potential of data-driven maintenance strategies in enhancing operational efficiency, reducing downtime, and improving asset reliability within the Indonesian energy industry.
The Impact of Digital User Experience on Brand Perception and Consumer Loyalty in the E-Commerce Industry in Indonesia Susilawati, Agnes Dwita; Wahyudi, Farid; Putra, Wira Pramana; Supriyanto, Wawan; Limpo, Lita
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 03 (2024): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v1i03.244

Abstract

This research investigates the impact of digital user experience (DUE) on brand perception and consumer loyalty within the burgeoning e-commerce industry in Indonesia. Through a quantitative analysis involving 150 e-commerce users, the study examines the relationships between DUE, brand perception (BP), and consumer loyalty (CL) using structural equation modeling (SEM) with Partial Least Squares (PLS) 3.0. The findings highlight the significant positive associations between DUE and both BP and CL, emphasizing the crucial role of user-centric digital platforms in shaping consumer behavior and fostering brand loyalty. Furthermore, the study elucidates the mediating role of BP in the relationship between DUE and CL, underscoring the importance of cultivating favorable brand perceptions to enhance consumer loyalty. These insights offer valuable implications for e-commerce businesses seeking to optimize their digital strategies and cultivate enduring customer relationships in the dynamic Indonesian market landscape.
Lead Prioritization: A guide to maximizing sales using analytics and AI in Real Estate Goel, Krupa
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 03 (2024): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v1i03.450

Abstract

In this highly competitive market, selecting the right lead that would bring significant sales conversion is one of the keys to an effective real estate business. In the following paper, we discuss the function of analytics and artificial intelligence in transforming lead management in the real estate industry. Conventional methods of lead prioritizing involve the rote use of the gut feeling of a salesperson, which, although inefficient, leaves much room for potential errors. Incorporating AI and analytics lead management thrust real estate agents into data analysis, where agents can make predictive and behavioral decisions based on stated data. Through lead scoring tools and other intelligent platforms such as AI, an agent can determine high probable lead conversion, directing efforts to more converting leads. Furthermore, this paper examines the use of big data analytics in predicting market trends and buying behavior to improve the position of agents competing in a highly technological marketplace. The paper also discusses changes in buyers' needs, including the interest in customization and environmental sustainability, which are gaining importance in lead selection processes. For this, adopting these technologies not only makes operations more efficient but also assists agents in building better client relations by initiating marketing that would capture the clients' interest and subsequently follow up on them. The lead management of the real estate industry will only improve due to enhanced artificial intelligence and analytics in the future. This paper acts as a reference for real estate agents trying to incorporate current tools in managing their leads to improve their chances of closing deals.
Analyzing Energy Consumption Data to Optimize Efficiency in High-Performance Computing Centers Dahule, Pratik
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 03 (2024): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v1i03.514

Abstract

High-performance computing (HPC) centers are at the forefront of technological innovation, enabling breakthroughs in fields ranging from scientific research to artificial intelligence. However, the immense computational power they deliver comes at a cost: these facilities consume vast amounts of energy, leading to soaring operational expenses and significant environmental footprints. As the demand for HPC capabilities continues to grow, optimizing energy efficiency has become a critical priority not only to cut costs but also to align with global sustainability goals. This article delves into how energy consumption data analysis can serve as a game-changer for HPC centers striving to balance performance with efficiency. By harnessing advanced tools such as real-time energy monitoring, machine learning algorithms, and predictive analytics, these facilities can unlock new opportunities for optimization. Data-driven strategies enable smarter workload distribution, more efficient cooling systems, and better utilization of hardware resources, all while maintaining the high-performance standards required for complex computations. To illustrate the real-world impact of these approaches, the article presents a case study of an HPC center that successfully implemented energy optimization strategies. Through a combination of cutting-edge analytics and strategic adjustments, the center achieved a notable reduction in power consumption without compromising computational performance. This example underscores the transformative potential of data-driven energy management in HPC environments, offering valuable insights for other facilities looking to enhance their sustainability and operational efficiency. By embracing these innovative techniques, HPC centers can not only reduce their energy costs but also contribute to a greener, more sustainable future, proving that high performance and environmental responsibility can go hand in hand.
Automated ETL Pipelines for Modern Data Warehousing: Architectures, Challenges, and Emerging Solutions Chanda, Deepak
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 03 (2024): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v1i03.523

Abstract

The paper addresses the evolution of automated Extract, Transform, Load (ETL) pipelines in contemporary data warehousing environments, highlighting their essential role in enabling timely analytics and business intelligence. Recent architectural approaches like cloud-native ETL, stream processing architectures, and metadata-driven automation are addressed in the context of increasing data volume and variety. The article addresses typical challenges like schema evolution management, data quality assurance, and cross-platform integration in the context of discussing novel solutions based on leveraging artificial intelligence for pipeline optimization. Through a survey of current implementations and future perspectives, this research provides an in-depth view of how automated ETL workflows are transforming data warehouse environments and enabling more agile, scalable business intelligence solutions.
The Evolution of E-Commerce: Data-Driven Insights and Future Trends Annapureddy, Govardhan Reddy
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 03 (2024): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v1i03.539

Abstract

The dynamic nature of change is evident from the fact that it is prompted by such factors as advancements in technology and shift in consumers’ behavior for E-commerce operations. The following paper will look at four developing industry trends that include artificial intelligence as well as artificial intelligence mobile commerce or social commerce as well as sustainability. The integration of artificial intelligence with the mobile commerce makes personalization and logistic improvements while providing a more tailored buying experience with the help of the deployment of the smartphones and AR technologies in Shopping. Social commerce enables the consumers to buy products through social media networks which in turn affect their buying decisions.

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