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Journal : JAKBS

Bibliometric Analysis and Network Visualization in Credit Risk Studies: The Role of Machine Learning in Financial Risk Management Nur Fadila; Sri Andriani
Jurnal Akuntansi Keuangan dan Bisnis Vol. 3 No. 1 (2025): April - Juni
Publisher : CV. ITTC INDONESIA

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Abstract

This study explores the development and research trends related to credit risk through a bibliometric approach and network visualization using VOSviewer software. By analyzing internationally indexed scientific publications from 2015 to 2025, the study aims to identify major themes, topic evolution, and key contributors in the field. The results reveal a growing academic interest in credit risk, particularly in the application of advanced technologies such as machine learning for risk evaluation and management. This research not only provides a visual mapping of the evolving academic landscape but also uncovers under-researched areas. The findings offer strategic insights for future research directions and support risk-based decision-making in the modern financial sector.
Validation Of Matching NIK, NPWP And Assistance In Filling Out Annual Tax Adelia Octafia Nur Ramadhani; Sri Andriani
Jurnal Akuntansi Keuangan dan Bisnis Vol. 3 No. 1 (2025): April - Juni
Publisher : CV. ITTC INDONESIA

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Abstract

In Indonesia’s tax administration system, the integration of the National Identity Number (NIK) and the Taxpayer Identification Number (NPWP) is essential to ensure data accuracy, service efficiency, and improved taxpayer compliance. This study investigates the validation process of matching NIK and NPWP, as well as the need for assistance in completing the Annual Income Tax Return (SPT). A mixed-method approach was applied, combining quantitative bibliometric analysis using VOSviewer and qualitative literature review. The findings reveal a significant number of publications discussing the Tax Identification Number from 2019-2024, categorized into five main research clusters. The study proposes an assistance in SPT completion. Such a system is expected to minimize reporting errors and enhance voluntary taxpayer compliance in Indonesia’s digital tax administration.