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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 11 Documents
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Search results for , issue "Vol. 1 No. 02 (2023): The Eastasouth Journal of Information System and Computer Science (ESISCS)" : 11 Documents clear
Implementation of Enterprise Resource Planning (ERP) Based Information System Using Odoo Software Mega Putri Utami; Ivana Lucia Kharisma
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 02 (2023): 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.v1i02.161

Abstract

Management and good data management is something that is very important for the continuity of a company. With management in a company, it is expected that all actions or activities carried out will run well and be controlled. The research focused on the use of an Enterprise Resource Planning (ERP) Based Information System using the odoo software used in the Purchasing & Materials department or more specifically known as PPIC. The purpose of this research is to find out how the use of ERP technology can facilitate coordination and communication between users so as to produce fast decision making. The implementation of this information system is expected to be one of the solutions in overcoming the problems that exist at PT Longvin Indonesia, especially the PPIC department.
The Influence of Strategic Planning and Analytical Maturity on Organizational Performance in Implementing Business Intelligence in Indonesia Eri Mardiani; Ivon Arisanti; Diky Wardhani; Ni Desak Made Santi Diwyarthi
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 02 (2023): 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.v1i02.180

Abstract

This research delves into the dynamics of intricate Business Intelligence (BI) deployments in Indonesian startups, emphasizing the interplay of organizational performance, analytical maturity, and strategic planning. Structural Equation Modeling with Partial Least Squares (SEM-PLS) was utilized in the study, which had 187 individuals from several industries, to examine the linkages and extract significant findings. The results showed three strong positive relationships: organizational performance is positively impacted by strategic planning, analytic maturity has a considerable impact on organizational performance, and strategic planning influences analytic maturity. Large impact sizes and statistical robustness characterize these practically meaningful associations. This study adds to our understanding of BI adoption in the particular setting of startups by highlighting the crucial roles that mature analytics and strategic planning play in fostering organizational success.
The Impact of Scalability and Consistency Management on Database Management System Performance in Big Data Environment in Indonesia Eri Mardiani; Yesi Sriyeni; Astrid Napita Sitorus; Hanifah Nurul Muthmainah
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 02 (2023): 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.v1i02.181

Abstract

The swift expansion of technology start-up enterprises in Indonesia demands a deep comprehension of the variables impacting the functionality of Big Data Environments (BDE) and Database Management Systems (DMS). In the context of Indonesian start-ups, this study examines the effects of scalability and consistency management on DMS and BDE. Data from 134 participants were examined using Structural Equation Modeling (SEM-PLS) in a quantitative manner. The findings showed a strong favorable correlation between DMS -> BDE, Scalability -> DMS, and Consistency Management -> DMS. Strong reliability was exhibited by the measurement model, and discriminant validity was verified. While the model fit indices revealed places for improvement, the R Square values indicated an effective explanation of variation. An overview of Indonesian start-up characteristics that is representative was given by the demographic sample study. This research adds knowledge for improving big data and database operations in the dynamic startup environment.
The Influence of Data Quality and Machine Learning Algorithms on AI Prediction Performance in Business Analysis in Indonesia Loso Judijanto; Donny Muda Priyangan; Hanifah Nurul Muthmainah; I Wayan Jata
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 02 (2023): 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.v1i02.182

Abstract

This research investigates the intricate relationships among AI prediction performance, business analysis, data quality, and machine learning algorithms within the manufacturing sector in Indonesia. Through structural equation modeling analysis, the study explores the impact of these variables on one another, shedding light on the dynamics that contribute to successful AI adoption and business decision-making. The findings underscore the pivotal role of data quality in influencing AI prediction performance and machine learning algorithms, ultimately shaping the effectiveness of business analysis. The results provide practical insights for manufacturing companies seeking to optimize their data management practices and harness the potential of advanced technologies for strategic decision-making.
An Interdisciplinary Bibliometric Review of the Symbiotic Relationship between Business Intelligence and Artificial Intelligence Loso Judijanto; I Wayan Karang Utama; Nita Priska Ambarita; Indra Permana
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 02 (2023): 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.v1i02.183

Abstract

This research conducts an interdisciplinary bibliometric review to explore the symbiotic relationship between Business Intelligence (BI) and Artificial Intelligence (AI). Utilizing advanced bibliometric tools, we analyze a comprehensive dataset extracted from reputable databases, encompassing articles that meet predefined inclusion criteria. The study reveals thematic clusters, influential documents, and core keywords shaping the discourse within the BI-AI landscape. Thematic clusters highlight the multidisciplinary nature of research, ranging from the impact of AI on finance to business model innovation and sustainability. The top-ten cited documents provide a snapshot of seminal works guiding academic and practical understanding, while keyword analysis illuminates the central themes and areas of emphasis. The cross-analysis of these elements offers a nuanced view of the evolving landscape of BI-AI integration. The findings not only contribute to academic scholarship but also provide practical insights for organizations navigating the dynamic intersection of BI and AI.
Migrating from Oracle to PostgreSQL: Leveraging Open-Source to Reduce Database Costs and Enhance Flexibility Natti, Murali
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 02 (2023): 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.v1i02.433

Abstract

In today’s competitive landscape, organizations are under increasing pressure to reduce IT costs while maintaining or improving operational efficiency. One of the largest ongoing expenses for businesses with significant database infrastructure is the licensing and support fees associated with proprietary database systems, such as Oracle. While Oracle is renowned for its enterprise-grade capabilities and robust features, the escalating costs associated with scaling Oracle database environments can pose a significant financial burden. In contrast, PostgreSQL, a widely adopted open-source relational database, offers a powerful, cost-effective alternative that can provide comparable, and often superior, performance without the hefty licensing and support expenses. This white paper addresses the growing demand for cost-effective database solutions by exploring the migration process from Oracle to PostgreSQL. It highlights the financial and operational benefits of transitioning to PostgreSQL, including significant reductions in Total Cost of Ownership (TCO), improved performance, and greater flexibility in managing large-scale data environments. In particular, we delve into how PostgreSQL’s open-source nature enables organizations to avoid vendor lock-in, reduce upfront capital expenditure, and achieve scalability without compromising on features or functionality. Ultimately, this paper underscores the significance of transitioning to PostgreSQL, not just as a cost-saving measure, but as a strategic decision that enhances an organization’s ability to scale and innovate. By offering a comprehensive view of the migration process, from initial planning to post-migration performance optimization, this white paper equips IT decision-makers with the knowledge and tools to make informed decisions about database architecture and management. It highlights that PostgreSQL is not merely a viable alternative to Oracle, but a compelling choice for businesses looking to future-proof their data management infrastructure while maintaining high levels of performance and security.
Optimizing System Performance: Load Balancers and High Availability Ramdoss, Vasudevan Senathi
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 02 (2023): 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.v1i02.435

Abstract

Modern computing systems depend on load balancing and high availability to maintain reliability and scalability and achieve optimal performance. Load balancing achieves optimal resource utilization by distributing traffic across several servers to prevent overloading while high availability maintains system operations during maintenance and failure events. The combination of these technologies delivers uninterrupted user experiences and minimizes operational interruptions along with supporting distributed systems expansion. The paper examines fundamental concepts along with practical applications and industry-specific use cases in e-commerce as well as cloud computing and content delivery networks. The study shows how enhanced fault tolerance and system performance improvements come with implementation complexity challenges and increasing security concerns alongside cost considerations.
The Strategic Impact of Project Management and Kanban in Enhancing Data Analysis Efficiency Dahule, Pratik
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 02 (2023): 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.v1i02.494

Abstract

In data analysis projects, effective project management is critical to ensuring timely execution, resource optimization, and quality deliverables. This paper explores the integration of project management principles with the Kanban methodology to enhance workflow efficiency, task prioritization, and cross-functional collaboration. By providing a structured yet flexible approach, Kanban enables teams to visualize processes, limit work in progress, and mitigate bottlenecks. A case study from a utility company illustrates the practical application of Kanban, highlighting its impact on improving operational efficiency, reducing resolution times, and increasing customer satisfaction. Through data-driven techniques such as cohort analysis and sentiment analysis, the study evaluates internal performance improvements and shifts in customer perception. The findings demonstrate that Kanban, when coupled with data-driven decision-making, can significantly enhance project execution and service quality in data-intensive environments. This paper contributes to the growing body of research on agile project management strategies for data analysis initiatives.
Secure Remote Access in CloudStack: Implementation and Performance Evaluation of an L2TP-over-IPsec VPN Domakonda, Dileep
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 02 (2023): 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.v1i02.513

Abstract

This paper presents the design and deployment of a remote access VPN function in CloudStack, an open-source platform for virtualized cloud management. The Remote Access VPN offers secure connectivity for remote users to communicate with virtual machines (VMs) within guest networks. Users can safely connect to cloud-based systems from external networks by using a VPN that uses L2TP-over-IPsec as the underlying protocol. With certain routing mechanisms that guarantee that only guest network traffic is routed through the VPN, the feature supports both "Road Warrior" (dynamic IP clients) and "Site-to-Site" (pre-configured IP clients) VPN connections. In addition to discussing upcoming scalability and usability improvements, this paper covers the technical design, implementation, and testing strategies for the Remote Access VPN feature.
AI-Powered Systems for Detecting Financial Fraud in Real Time Chaudhary, Arjun; Behl, Sagar
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 02 (2023): 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.v1i02.653

Abstract

The rise of sophisticated financial fraud schemes in an increasingly digital economy has underscored the limitations of traditional rule-based detection systems. This study investigates the application of AI-powered systems for real-time financial fraud detection, integrating supervised, unsupervised, and hybrid machine learning approaches. A comparative evaluation of models such as Deep Neural Networks, Random Forests, Gradient Boosting, Autoencoders, and ensemble techniques was conducted using both static and streaming transaction data. Reports on accuracy, precision, recall, F1-score, latency and anomaly detection were reviewed. Deep Neural Networks had the most accurate results and Autoencoders were best at catching new fraud attempts with few false positives. It was established by statistical testing that model performance varied and concept drift detection indicated that retraining should be done continuously. Looking at feature importance confirmed that specific transaction details were explainable and useful in practice. Thanks to this work, we can identify how to make fraud detection systems more accurate, consistent and responsive which supports the growth of reliable and smart financial platforms.

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