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All Journal ELINVO (Electronics, Informatics, and Vocational Education) ProBisnis : Jurnal Manajemen
Kurniawan , Hendra
Unknown Affiliation
Humanities Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Social Sciences

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Mapping of Tax Avoidance Behavioral Patterns in The Digital Business Environment (Implications for Tax Regulations and Financial Risk Management) Arum, Mega; Kurniawan , Hendra
ProBisnis : Jurnal Manajemen Vol. 16 No. 3 (2025): June: Management Science
Publisher : Lembaga Riset, Publikasi dan Konsultasi JONHARIONO

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Abstract

The development of digital technology has significantly affected the business landscape, including in terms of taxation. In the context of digital business, the phenomenon of tax avoidance has become a major concern. This study aims to understand the pattern of tax avoidance behavior in the digital business environment and analyze its implications for tax regulations and financial risk management. The research method used involves literature analysis to identify common patterns of tax avoidance behavior in digital businesses. The results of the analysis show that digital businesses often take advantage of loopholes in tax regulations to reduce their tax obligations, either through transfer pricing, entity location determination, or profit shifting practices. The implications of this behavior are very complex, including challenges in enforcing relevant tax regulations and managing financial risks for affected countries. This study also highlights the importance of cross-border cooperation in addressing the tax challenges faced by digital businesses. While international tax regulations have evolved to accommodate the changing business environment, there is still a need for further reform to address increasingly complex tax avoidance practices. This study proposes several policy recommendations, including increasing cooperation between countries in the exchange of tax information, the use of technology to detect and prevent tax avoidance, and updating tax regulations that are more adaptive to the development of digital businesses. Thus, this study contributes to a better understanding of the dynamics of tax avoidance in the context of digital business and provides useful insights for policy makers, practitioners and academics in addressing this challenge.
Optimizing 4-ary Huffman Trees and Normalizing Binary Code Structures to Minimize Redundancy and Level Reduction Hidayat, Tonny; Kurniawan , Hendra; Mustopa , Ali; Kuswanto, Jeki
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 10 No. 1 (2025): May 2025 (In Press)
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/elinvo.v10i1.78722

Abstract

Since the present data expansion and increase are occurring at an increasingly rapid pace, the solution of adding storage space is not sustainable in the long run. The growing need for storage media can be addressed with lossless compression, which reduces stored data while allowing complete restoration. Huffman remains a potent method for data compression, functioning as a "back end" process and serving as the foundational algorithm in applications, among others, Monkey's PKZIP, WinZip, 7-Zip, and Monkey's Audio. Lossless compression of 16-bit audio requires binary structure adjustments to balance speed and optimal compression ratio. The use of a 4-ary Huffman tree (4-ary) branching procedure to generate binary code generation and to insert a maximum of 2 dummy data symbol variables that are given a binary value of 0 with the condition that if the number of MOD 3 data variables = remaining 2, then two dummy data are added, if the result is the remainder 0 = 1 dummy data, and if the remainder = 1 then it is not required. This process effectively maintains a high ratio level while speeding up the 4-ary Huffman code algorithm's performance in compression time. The results show that the efficiency reaches 95.94%, the ratio is 38%, and the comparison is 1/3 of the Level based on calculations, testing, and comparison with other generations of the Huffman code. The 4-ary algorithm significantly optimizes archived data storage, reducing redundancy to 0.124 and achieving an entropy value of 2.91 across various data types.
Co-Authors Arum, Mega Jeki Kuswanto Mustopa , Ali TONNY HIDAYAT