Article Per Year (5 Year)
p-Index From 2021 - 2026
2.345
P-Index
This Author published in this journals
All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Journal of Computer Networks, Architecture and High Performance Computing
Wedha, bayu Yasa
Unknown Affiliation
Computer Science & IT Education

Published : 14 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 14 Documents
Search

 1   2 
The Impact of Big Data on Enterprise Architectural Design: A Conceptual Review Sholihati , Ira Diana; Wedha, Bayu Yasa; Ningsih, Sari; Sari, Ratih Titi Komala
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i1.3449

Abstract

A conceptual analysis of the impact of big data on enterprise architecture design is provided in this article. Within the framework of expanding digitalization, big data has emerged as a pivotal component in delineating the strategy and framework of organizations. The objective of this study is to investigate the ways in which big data can impact and facilitate the growth of efficient enterprise architecture. Qualitative analysis is the method utilized by researchers to comprehend the intricacies of the interaction between enterprise architecture and big data. This article examines several facets by conducting an extensive review of the literature, including the ways in which big data can facilitate the enhancement of analytical capabilities, innovation in business processes, and strategic decision-making. Emerging challenges, including data security, privacy, and the necessity for IT infrastructure adaptation, are also considered in this study. The outcomes of the review indicate that the implementation of big data in enterprise architecture may substantially alter business strategies and operations. These encompass enhanced system adaptability, customized service provision, and predictive functionalities. Nonetheless, these modifications necessitate modifications to privacy policies, risk management, and data governance. This study presents novel findings regarding the influence of big data on enterprise architecture and provides researchers and practitioners with recommendations for developing and executing successful big data strategies. This research thereby enhances the current body of literature and offers practical guidance in the field.
Optimizing Transportation Logistics through Enterprise Architecture: A Case Study of Integrated Management Systems Wedha, Bayu Yasa
Journal of Computer Networks, Architecture and High Performance Computing Vol. 5 No. 2 (2023): Article Research Volume 5 Issue 2, July 2023
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v5i2.2779

Abstract

This abstract captures the essence of optimizing transportation logistics through Enterprise Architecture (EA) by presenting an Integrated Management Systems (IMS)-centered case study. Effective logistics management is crucial for enhancing operational effectiveness and attaining competitive advantages in today's rapidly changing business environment. Achieving this optimization, however, requires a comprehensive strategy that harmonizes disparate aspects such as operational processes, data administration, and technological infrastructure. This study demonstrates that Enterprise Architecture has the potential to function as the link between these domains. This study examines the difficulty of dispersed logistics operations and the resulting inefficiencies, cost escalation, and compromised responsiveness. The case study is presented as Integrated Management Systems powered by Enterprise Architecture principles. This integration intends to optimize workflows, encourage cross-functional collaboration, and provide real-time visibility into logistical activities. This investigation demonstrates how a well-designed EA framework can effectively mitigate transportation logistics and supply chain management disparities. By analyzing the results of the case study, this study aims to identify the benefits and challenges of implementing Enterprise Architecture within the realm of transportation logistics. This study explains how EA can improve operational efficiency, resource utilization, lead time, and customer satisfaction. Therefore, the research contributes to a broader comprehension of how Enterprise Architecture can optimally resolve the complexities inherent to contemporary transportation logistics. The potential for increased operational agility and competitiveness in the logistics industry is highlighted by applying EA principles to the context of integrated management systems.
Maximizing ERP Benefits with Enterprise Architecture: A Holistic Approach Wedha, Bayu Yasa; Hindarto, Djarot
Journal of Computer Networks, Architecture and High Performance Computing Vol. 5 No. 2 (2023): Article Research Volume 5 Issue 2, July 2023
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v5i2.2790

Abstract

Enterprise Resource Planning systems must strategically align with Enterprise Architecture to maximize benefits. In a business environment that is undergoing rapid change, organizations increasingly rely on ERP systems to integrate and streamline operations. However, the complete potential of ERP benefits may only be realized with an approach encompassing the entire organizational architecture. This study examines the importance of aligning ERP implementation with EA principles to establish a cohesive technological ecosystem. Organizations can facilitate seamless interactions and data flows by harmonizing business processes, data structures, applications, and technology infrastructure, allowing for efficient decision-making and resource optimization. The abstract describes how EA provides a structured blueprint to guide the integration of ERP systems, assuring compatibility, minimizing redundancies, and maximizing overall system efficiency. By conducting a comprehensive literature review and case study analysis, this study demonstrates the benefits of an integrated approach, including increased visibility, reduced operational divisions, improved scalability, and faster response to changes. The abstract also emphasizes the role of EA in adapting and evolving ERP systems as business requirements change, enabling organizations to resolve challenges and proactively leverage emerging technologies. Overall, this research contributes to a deeper understanding of the symbiotic relationship between EA and ERP by highlighting their combined ability to drive business growth, agility, and competitiveness. The abstract emphasizes the importance of strategic alignment. It guides practitioners, researchers, and decision-makers who wish to maximize the benefits of ERP implementations through a holistic Enterprise Architecture approach.
A Blockchain-Assisted Neural Network Model for Flood Detection and Data Integrity Assurance Melanza, Fattan Rezky; Hindarto, Djarot; Wedha, Bayu Yasa; Sani, Asrul
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15487

Abstract

Flooding is one of the most frequent natural disasters and has substantial impacts on social, economic, and environmental conditions. Therefore, early detection plays a critical role in minimizing potential damage and supporting effective disaster response. This study proposes a Flood Detection System Using an Artificial Neural Network (ANN) with Blockchain-Based Data Integrity, which integrates predictive analytics and secure data management in a unified framework. The ANN model processes multisource environmental data such as satellite imagery, rainfall intensity, water level fluctuations, and soil moisture obtained from Google Earth Engine (GEE). Training is conducted using a sigmoid activation function and backpropagation algorithm to identify spatial and temporal patterns associated with flood-prone areas. The resulting classification outputs are stored in a blockchain ledger to ensure immutability, transparency, and protection against unauthorized data modification. Experimental evaluations demonstrate that the proposed hybrid approach achieves an accuracy of 95.82%, supported by precision, recall, and F1-score values that indicate consistent model performance across varying environmental conditions. The integration of blockchain provides verifiable and tamper-proof documentation of ANN predictions and related metadata. Overall, this research contributes a reliable, secure, and technically robust method for early flood detection, offering valuable support for data-driven decision-making in disaster mitigation and environmental risk management.
Co-Authors Asrul Sani Haryono Haryono Hindarto, Djarot Karjadi, Daniel Avian Melanza, Fattan Rezky Rahman, Ben Ratih Titi Komalasari Santoso , Handri Santoso, Handri Sari Ningsih Sholihati , Ira Diana Sholihati, Ira Diana Vasandani, Michael Sagar Wedha, Alessandro Benito Putra Bayu Wedha, Alessandro Enriqco Putra Bayu