Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal Efektor JASa (Jurnal Akuntansi, Audit dan Sistem Informasi Akuntansi) BIP'S : Jurnal Bisnis Perspektif
Kan Wen Huey
Wawasan Open University
Computer Science & IT Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Social Sciences

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Beyond the Price: How Trading Activity Shapes Bitcoin Volatility Diky Paramitha; Etik Ipda Riyani; Nadhira Hardiana; Kan Wen Huey
BIP's JURNAL BISNIS PERSPEKTIF Vol. 18 No. 2 (2026): Juli
Publisher : Universitas Katolik Darma Cendika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37477/bip.v18i2.921

Abstract

Bitcoin has a tendency of price volatility that is much higher than other cryptocurrency assets, this makes a very significant difference from other financial assets that can go beyond conventional market logic thus creating a major obstacle in risk management. This study aims to dissect the extreme anomalies of bitcoin trading volume against the volatility of Bitcoin returns. Using a quantitative time series approach, the study analyzed monthly data on bitcoin price and trading volume using Bitcoin prices in the period February 2015 to December 2025. We assess volatility using the GARCH-X model to introduce trading volume as an exogenous variable. The basic GARCH shows significant volatility persistence, indicating a clustering of high volatility in Bitcoin's returns. This finding results that trading volume is not just a static transaction number but reflects a very crucial information proxy. Every movement of trading activity generates new signals in which aggressive price react. Trading volume is also highly correlated with the volatility of returns, although the volatility of the model indicates the need for careful interpretation. Bitcoin's volatility is not solely due to historical volatility dynamics, but also the impetus from trading activity, highlighting the need to consider accurate volatility modeling in the digital asset market. This research adds value by embedding trading volumes into the GARCH model to evaluate its contribution in explaining Bitcoin's volatility through empirical insights for investment decisions and risk management in the cryptocurrency market
Mengungkap Hubungan Tersembunyi Antara Harga Kripto dan Saham Pertambangan: Bukti dari ANTM dan TINS Diky Paramitha; Etik Ipda Riyani; Kan Wen Huey
Efektor Vol 12 No 2 (2025): Efektor Vol.12 No.2 Tahun 2025
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/e.v12i2.27205

Abstract

The high volatility of the crypto market has raised questions about its potential impact on other financial instruments, including shares of mining sector issuers. This study aims to reveal the relationship between the price of crypto assets, especially Bitcoin, and the stock price movements of two major mining issuers in Indonesia, namely Antam (ANTM) and Tin (TINS). The analysis was carried out with a quantitative approach with a simple linear regression analysis method, to test the influence of the price of bitcoin on each stock. The results of the analysis show the direction of a positive and significant relationship between the price of Bitcoin and ANTM shares with a regression coefficient of 0.008776. This value indicates that any increase in the price of Bitcoin is potentially followed by an increase in the price of ANTM's shares, with a contribution of variation of 45% (R² = 0.45). Meanwhile, the influence on TINS shares was also positive but weaker, with a regression coefficient of 0.004999 and a contribution of variation of 28% (R² = 0.28). These findings indicate that the crypto market could be one of the external factors influencing the dynamics of real sector stocks, especially those related to metal commodities. The conclusion of this study confirms the importance of including crypto variables in the risk analysis and investment strategies of mining sector stocks due to the adoption of blockchain technology that requires such commodities.
Integration of Artificial Intelligence and Data Mining in Accounting Information Systems for Analysis of New Student Uptake Patterns: An Open University Case Study Diky Paramitha; Etik Ipda Riyani; Kan Wen Huey
JASa (Jurnal Akuntansi, Audit dan Sistem Informasi Akuntansi) Vol. 10 No. 1 (2026): April
Publisher : Program Studi Akuntansi Universitas Langlangbuana Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36555/jasa.v10i1.2992

Abstract

Increased enrollment of new students is an important measure of the university's ability to meet the needs of Education. To remain relevant and competitive, institutions are increasingly expected to incorporate artificial intelligence (AI) technologies that improve data-driven decision-making. The combination of data mining methods and Accounting Information Systems, which utilize computational techniques to analyze vast data sets can reveal hidden patterns. This study investigates the application of artificial intelligence in creating a direct data mining tool that aims to identify trends in new student enrollment at the Open University. Using a research and development approach, the study examined admissions data from 2022 to 2024 to uncover underlying patterns and significant factors influencing enrollment dynamics. The results show that AI-integrated applications significantly improve the efficiency of data analysis while improving the reliability and usability of institutional data for strategic purposes. The study contributes to a larger conversation about how aligning AI implementation with institutional goals can drive innovation, accountability, and responsiveness in the context of open and distance learning. In summary, the findings imply that incorporating AI into data management and development processes provides greater clarity regarding future trends, making AI a powerful forecasting and decision-making resource for higher education institutions.
Co-Authors Diky Paramitha Etik Ipda Riyani Etik Ipda Riyani, Etik Ipda Riyani Nadhira Hardiana