Articles
<![CDATA[Kejadian Obat-Obatan Penginduksi Kerusakan Liver pada Pasien Sirosis Rawat Inap di RSUD Dokter Soedarso Kalimantan Barat ]]>
<![CDATA[Robiyanto, Robiyanto]]>;
<![CDATA[Liana, Jesica]]>;
<![CDATA[Purwanti, Nera Umilia]]>
<![CDATA[ JSFK (Jurnal Sains Farmasi & Klinis) Vol 6 No 3 (2019): J Sains Farm Klin 6(3), Desember 2019 ]]>
Publisher : <![CDATA[Fakultas Farmasi Universitas Andalas ]]>
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DOI: 10.25077/jsfk.6.3.274-285.2019
<![CDATA[Potensi terjadinya hepatotoksisitas karena penggunaan obat merupakan masalah klinis yang perlu diperliverkan. Risiko ini dapat menyebabkan bertambah parahnya penyakit liver yang diderita oleh pasien yang memang sudah menderita penyakit liver tertentu. Penelitian ini bertujuan untuk menentukan jenis obat yang paling berpotensi menginduksi kerusakan liver atau drug-induced liver injury (DILI) dan persentase peresepan obat berdasarkan kategori Likelihood Scores (A. B. C. D. E. E*. X). Penelitian ini merupakan penelitian observasional deskriptif dengan desain cross-sectional. Pengambilan data dilakukan secara retrospektif melalui rekam medik pasien sirosis rawat inap tahun 2017. Sebanyak 36 pasien sirosis yang memenuhi kriteria inklusi diambil sebagai sampel penelitian. Analisa data menggunakan LiverTox database (https://livertox.nih.gov/) untuk mengetahui kategori Likelihood Scores dari masing-masing obat. Hasil penelitian menunjukkan jenis obat yang paling banyak diresepkan dan berpotensi menginduksi kerusakan liver adalah paracetamol (kategori A), ranitidin (B), cetirizin (C), spironolakton (D), furosemid (E), dan ketorolac (E*). Persentase peresepan obat berdasarkan Likelihood Scores kategori A=1.7%. B=11.6%. C=0.4%. D=13.7%. E=23.6%. E*=3.4% dan X=32.2%. Dapat disimpulkan bahwa peresepan obat yang berpotensi menginduksi keparahan fungsi liver (kategori A.B.C.D.E.E*) pada pasien sirosis rawat inap di RSUD dr. Soedarso Pontianak masih tergolong tinggi (54.4%) dan memerlukan pertimbangan klinis yang hati-hati.]]>
<![CDATA[Simulation-Based Pricing and Settlement Price Distributions of Indonesian Structured Warrants ]]>
<![CDATA[Sasongko, Leopoldus Ricky]]>;
<![CDATA[Mahatma, Tundjung]]>;
<![CDATA[Robiyanto, Robiyanto]]>
<![CDATA[ JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 2 (2025): April ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Mataram ]]>
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DOI: 10.31764/jtam.v9i2.29282
<![CDATA[The Indonesian capital market has experienced significant growth, marked by the introduction of Structured Warrants (SWs) as innovative financial instruments. This study aims to develop a robust simulation-based pricing model for Indonesian Call SWs utilizing the Geometric Brownian Motion (GBM) framework and to determine their settlement price distributions. Monte Carlo simulations were employed to accurately capture the specific characteristics of Indonesian Call SWs, notably their average-price settlement mechanism and conversion rates. The results indicate that the settlement prices conform to a lognormal distribution, validating the GBM assumption and aligning with key trading metrics such as implied volatility, which is widely utilized in the Indonesian SW market. Additionally, the Symmetrical Auto Rejection rule, which imposes realistic constraints on underlying asset price movements, significantly enhances model realism and better reflects actual market conditions. The findings reveal that simulated Indonesian Call SW prices are slightly lower compared to values derived from the Black-Scholes model adjusted for conversion rates, highlighting opportunities for further refinement of pricing methodologies. Investors can leverage these insights to better assess risks and returns by anticipating volatility and price trends, with paying close attention to conversion rates and settlement mechanisms. Issuers may benefit from improved pricing accuracy, thus minimizing mispricing risks, while regulators can utilize this research to assess current market rules and design policies aimed at increasing market efficiency and transparency. ]]>
<![CDATA[Corporate Finance and Firm Value in The Indonesian Manufacturing Companies ]]>
<![CDATA[Handriani, Eka]]>;
<![CDATA[Robiyanto, Robiyanto]]>
<![CDATA[ International Research Journal of Business Studies Vol. 11 No. 2 (2018): August-November 2018 ]]>
Publisher : <![CDATA[Universitas Prasetiya Mulya ]]>
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DOI: 10.21632/irjbs.11.2.113-127
<![CDATA[This study examines the impact of dividend policy, investment decision, and funding policy on firm value in the Indonesian manufacturing companies. This study was conducted by using 178 manufacturing companies listed in the Indonesia Stock Exchange (IDX) during the period 2009 - 2016. Samples were taken by using a purposive sampling method. This study aims to examine the firm value’s main determinants of the manufacturing companies in Indonesia based on basic theory of capital structure through building theoretical model. Partial Least Square through path analysis was used to analyze the data. The findings shows that dividend policy, investment decision, and funding policy have a positive impact on the Indonesian manufacturing companies’ firm value. This study supports the theory of pecking order and agency theory.]]>
<![CDATA[Determinants of the Stock Price Volatility In the Indonesian Manufacturing Sector ]]>
<![CDATA[Handayani, Heny]]>;
<![CDATA[Muharam, Harjum]]>;
<![CDATA[Mawardi, Wisnu]]>;
<![CDATA[Robiyanto, Robiyanto]]>
<![CDATA[ International Research Journal of Business Studies Vol. 11 No. 3 (2018): December 2018 - March 2019 ]]>
Publisher : <![CDATA[Universitas Prasetiya Mulya ]]>
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DOI: 10.21632/irjbs.11.3.179-193
<![CDATA[This study aimed to analyze the influence of return on equity, debt to equity ratio, sales growth, firm size, cash ratio, and dividend payout ratio to stock price volatility companies listed on the Indonesia Stock Exchange in the period 2011-2015. The populations of this study are all manufacturing companies listed in Indonesia Stock Exchange (IDX) in the period 2011 to 2015. It obtained eight companies samples with technique purposive sampling method. The data analysis technique used is the regression model panel then be adjusted again by using GARCH (Generalized Autoregressive Conditional Heteroscedasticity). The results showed that the volatility of the stock price only affect without any effect ARCH-GARCH therein. Determining the best models of each prediction is based on estimated volatility GARCH (p, q). The determination of whether there is influence of the factors believed to be the determinants of stock price volatility was done by using panel data regression analysis. The results of panel data regression analysis showed that the company’s stock price volatility in the research samples can be explained by 4.84% by ROE, CR, DER, DPR, company size and sales growth while the remaining 95.16% explained by other variables outside the research. Only sales growth has significant positive effect on stock price volatility.]]>
<![CDATA[Nexus between Cryptocurrency Markets and Hedge Funds in Period Before and During Russia-Ukraine War ]]>
<![CDATA[Wibowo, Stefanus Chandra]]>;
<![CDATA[Robiyanto, Robiyanto]]>;
<![CDATA[Huruta, Andrian Dolfriandra]]>;
<![CDATA[Triyanto, Triyanto]]>
<![CDATA[ Media Ekonomi dan Manajemen Vol 40, No 2 (2025): July 2025 ]]>
Publisher : <![CDATA[Fakultas Ekonomika dan Bisnis UNTAG Semarang ]]>
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DOI: 10.56444/mem.v40i2.5476
<![CDATA[The purpose of this study is to identify the pre- and post-war impact of the Russia-Ukraine war on the interaction between cryptocurrencies, cryptocurrency hedge funds, and traditional hedge funds. This study provides a deeper understanding of how geopolitical events can affect the behavior of financial markets involving cryptocurrencies and hedge funds. In addition, this study also seeks to fill the knowledge gap that exists in the current literature, specifically with regards to hedge fund strategies during specific geopolitical conflicts. This study utilizes secondary data involving the cryptocurrency hedge fund index, global hedge fund index, and eight proposed hedge fund strategies. The study period runs from February 2018 to July 2023. Granger Causality Test and ARDL used in this study. The finding shows that there is a significant relationship between cryptocurrency hedge funds and conventional hedge funds. Statistical analysis revealed cointegration between cryptocurrency hedge funds and conventional hedge funds, indicating a significant long-term relationship. This study identified a significant impact of changes in market behavior before and after the Russia-Ukraine war on cryptocurrency hedge funds.]]>
<![CDATA[Liquidity, Volatility, and Herding Behavior: A Study of the Indonesia Stock Exchange during the Covid-19 Pandemic ]]>
<![CDATA[Nahar, Zuwan Nisfu]]>;
<![CDATA[Robiyanto, Robiyanto]]>
<![CDATA[ Media Ekonomi dan Manajemen Vol 40, No 2 (2025): July 2025 ]]>
Publisher : <![CDATA[Fakultas Ekonomika dan Bisnis UNTAG Semarang ]]>
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DOI: 10.56444/mem.v40i2.5942
<![CDATA[This research aims to determine liquidity and volatility conditions and differences in herding behavior during the pre-pandemic, early pandemic, and the new normal period of the COVID-19 pandemic in Indonesia. This research examines the microstructure and proves herding behavior on the Indonesian Stock Exchange (IDX). This research will also look at liquidity and volatility to see market sentiment because investor behavior can be reflected in liquidity and volatility. The population used in this research is the IDX-80 index, with 48 companies as the sample. This study uses CSAD to test herding behavior because it is not sensitive to outliers. This research found that the highest market liquidity and volatility occurred during the early pandemic, and the highest herding behavior occurred during the new normal period. The market response to each event can also determine the direction of stock movements, so investors can take advantage of this period to collect shares and sell them again when prices rise. This research offers a summary of the Indonesian capital market during significant events, which can assist investors in developing investment plans that consider the course of events. Practically, this study offers actionable insights for investors by explaining how market conditions during different phases of the pandemic influence investment strategies.]]>
<![CDATA[Exchange Rate and Gold Price’s Effect on Jakarta Composite Index during Ukraine-Russian War ]]>
<![CDATA[Gunadi, Patrisinus Ceasar]]>;
<![CDATA[Robiyanto, Robiyanto]]>
<![CDATA[ AJAR Vol. 7 No. 01 (2024): Atma Jaya Accounting Research (AJAR) ]]>
Publisher : <![CDATA[Magister Akuntansi - Universitas Atma Jaya Makassar ]]>
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DOI: 10.35129/ajar.v7i01.483
<![CDATA[This research aims to examine how exchange rates and gold prices influence the Indonesia Composite Index during the Ukrainian and Russian wars. The research object used in this research is the USD/IDR exchange rate, gold prices and Jakarta Composite Index (JCI) from February 24, 2022 to October 25, 2023. The data analysis technique uses Generalized Autoregressive Conditional Heteroscedasticity (GARCH). The results of this research indicate that the research model follows GARCH patterns. Furthermore, Rupiah exchange rate against the Dollar does not significantly influence the Indonesia Composite Index. Meanwhile, the price of gold influences significantly and has a positive influence on the Indonesia Composite Index. In conclusion, investors can pay attention to the price of gold because the price of gold has a significant influence in a positive direction on the Indonesia Composite Index.]]>
<![CDATA[Factors Influencing Profit Distribution Management of Sharia Commercial Banks in Indonesia ]]>
<![CDATA[Ernayani, Rihfenti]]>;
<![CDATA[Robiyanto, Robiyanto]]>;
<![CDATA[Sudjinan, Sudjinan]]>
<![CDATA[ Journal of Economics, Business, and Accountancy Ventura Vol. 20 No. 2 (2017): August - November 2017 ]]>
Publisher : <![CDATA[Universitas Hayam Wuruk Perbanas ]]>
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DOI: 10.14414/jebav.v20i2.1055
<![CDATA[This study aims to determine and obtain empirical evidences regarding the effect of deposits, product asset management, and rate of inflation on profit distribution management at Sharia Commercial Banks in Indonesia during 2012-2014 periods. By using purposive sampling, 10 samples of Sharia Commercial Banks were observed on a quarterly basis, and the 120 panel data were gained. The data were analyzed by employing multiple linear regression. The result shows that Deposits have positive significant effects on Profit Distribution Management. Productive Assets Management has negative significant effects on Profit Distribution Management. Rate of Inflation has negative significant effect on Profit Distribution Management. Deposits, Productive Assets Management, Rate of Inflation simultaneously have significant effects on Profit Distribution Management.]]>
<![CDATA[The Effect of Liquidity Risk, Financing Risk, and Operational Risk toward Indonesian Sharia Bank’s Financing with Bank Size as a Moderating Variable ]]>
<![CDATA[Alsyahrin, Dea Prastica]]>;
<![CDATA[Atahau, Apriani Dorkas Rambu]]>;
<![CDATA[Robiyanto, Robiyanto]]>
<![CDATA[ Journal of Economics, Business, and Accountancy Ventura Vol. 21 No. 2 (2018): August - November 2018 ]]>
Publisher : <![CDATA[Universitas Hayam Wuruk Perbanas ]]>
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DOI: 10.14414/jebav.v21i2.1181
<![CDATA[Islamic banking is growing rapidly in Indonesia, so it needs to be done a lot of studies on sharia banking especially about the influence of risks to sharia financing. The purpose of this study is to analyze of the influence of liquidity risk, financing risk, and operational risk with bank size as moderating variable. This research uses financial statement of Sharia Commercial Bank for 2012-2016 period. By using purposive sampling method, 12 Sharia Commercial Bank were chosen as samples in this study. The data use in this study is panel data. Those data was collected from Sharia Commercial Bank’s website. Data analyzed by using moderated regression analysis. The result shows that liquidity risk, financing risk, and operational risk significantly influenced the financing of Indonesian sharia banking with bank size as it’s moderating variable.]]>
<![CDATA[Day-of-the-Week-Effect and Month-of-the-Year-Effect on Carbon Emissions Contract Trading ]]>
<![CDATA[Azalia, Kezia Maylani]]>;
<![CDATA[Robiyanto, Robiyanto]]>
<![CDATA[ Journal of Management and Entrepreneurship Research Vol. 5 No. 1 (2024) ]]>
Publisher : <![CDATA[Universitas Islam Nahdlatul Ulama Jepara ]]>
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DOI: 10.34001/jmer.2024.6.05.1-44
<![CDATA[Objective: This study aims to examine whether there is a potential Day-of-the-Week-Effect and Month-of-the-Year-Effect on carbon emissions trading. Research Design & Methods: This research uses secondary data obtained through the Investing.com website. The carbon market data used is daily closing data, then the daily effect test is carried out, and monthly closing data to determine the monthly effect. Generalized Autoregressive Conditional Heteroskedasticity (GARCH) (2,1) and (3,1) method used to analyse the data. Findings: The correlogram and GARCH (2,1) and GARCH (3,1) test results show that the carbon market does not move randomly, but there are Day-of-the-Week-Effect and Month-of-the-Year-Effect phenomena. From this study, it was also found that on Wednesday in April, there was a significant increase in returns. So, it can also be concluded that the carbon market is not efficient. Implications and Recommendations: There are opportunities that can be taken from carbon trading which turns out to have a Day-of-the-Week-Effect and Month-of-the-Year-Effect so that investors who want to join carbon trading can more easily learn about it to get maximum profit in the carbon market. Contribution & Value Added: It is hoped that the results of this study can prove whether or not there is an influence on seasonal patterns so that it can be useful for speculators and business people related to carbon trading to design the right strategy in the carbon emissions market.]]>
