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All Journal Jurnal Teknologi Informasi dan Komunikasi Journal of Management and Informatics
Wicaksono, Yosep Aditya
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Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Mechanical Engineering

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A LITERATURE REVIEW ON NETWORK SIMULATION APPLICATION DEVELOPMENT USING VISUAL PROGRAMMING LANGUAGES Wicaksono, Yosep Aditya; Archaqie, Haikal Nur Rachmanrachim
JURNAL TEKNOLOGI INFORMASI DAN KOMUNIKASI Vol. 16 No. 1 (2025): MARET
Publisher : UNIVERSITAS STEKOM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtikp.v6i1.1146

Abstract

This study investigates the development of network simulation applications using visual programming languages, aiming to map current trends, tool preferences, and key implementation challenges in educational and experimental contexts. Through a structured literature review of 28 academic sources, the research identifies the increasing adoption of visual-based interfaces, such as VB.NET, C# WinForms, and tools like Cisco Packet Tracer, as essential platforms to support interactive learning in computer networking. Visual simulation environments are found to enhance conceptual understanding, reduce cognitive load, and improve student engagement, particularly among non-technical users. Despite these advantages, the study reveals several limitations inherent in visual simulation tools. These include lack of modularity, limited protocol validation, and constrained interoperability across different vendors and platforms. Moreover, most existing tools operate in standalone environments and have not yet adapted to cloud-based or scalable architecture. The findings suggest a pressing need for hybrid simulation models that combine the intuitive user experience of GUI-based applications with the technical depth and flexibility of CLI or open API backends. This research contributes a systematic synthesis of the strengths and gaps within the current ecosystem of visual network simulators. It provides practical recommendations for educators, developers, and researchers aiming to build more adaptive, extensible, and context-aware simulation environments that align with modern educational and technological needs.
Enhancing Decision Quality and Transparency via Machine Learning-Based Goodwill Impairment Estimation in Banks Wibisono, Gunawan; Nikhlis, Neilin; Wicaksono, Yosep Aditya; Faradila, Silvia
Journal of Management and Informatics Vol. 4 No. 3 (2025): December Season | JMI: Journal of Management and Informatics
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jmi.v4i3.233

Abstract

Goodwill impairment assessment remains a judgment-intensive process in banking institutions, where managerial discretion, information asymmetry, and regulatory complexity often challenge the quality of decisions and transparency. While prior studies have widely applied machine learning to financial risk assessment and credit analytics, they have paid limited attention to its role in improving managerial accountability in goodwill impairment decisions. This study aims to address this gap by developing and evaluating a machine-learning–based estimation framework to enhance the quality of decisions and transparency in bank-level goodwill impairment assessments. Using simulation-based analysis on synthetic financial statements, the proposed framework evaluates the performance of impairment estimation using quantitative metrics that capture predictive accuracy, decision consistency, and traceability. The findings demonstrate that ML-assisted estimation can systematically improve decision quality while strengthening transparency and accountability compared to traditional judgment-driven approaches. Beyond technical performance, the results indicate that machine learning can function as a governance-supporting mechanism by enabling more traceable and internally auditable impairment decisions. The study contributes theoretically by operationalizing transparency and accountability as measurable decision outcomes in corporate finance, and practically by offering banks a simulation-based tool for internal evaluation that does not rely on field experiments or sensitive proprietary data. Overall, the research highlights the potential of ML-enabled decision support systems to enhance both the quality and governance of goodwill impairment practices in the banking sector.
Co-Authors Archaqie, Haikal Nur Rachmanrachim Faradila, Silvia Gunawan Wibisono Nikhlis, Neilin