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This study examines the implementation of the vMedis ERP system at Greencare Pharmacy using Balanced Scorecard (BSC) and SWOT analysis. The research evaluates financial, customer, internal process, and learning perspectives, revealing significant improvements post-implementation. Key findings include a 15% profit margin increase, enhanced customer satisfaction, streamlined inventory management, and improved staff competencies. Statistical analysis using the Wilcoxon Signed-Rank Test confirmed that these improvements across the BSC perspectives were significant (p < 0.05). SWOT analysis highlights internal strengths, such as financial stability and advanced technology adoption, and external opportunities like market growth potential. Challenges include regulatory complexity and high competition. Overall, vMedis demonstrates its effectiveness in enhancing operational efficiency and competitiveness in the healthcare sector.

This study investigates the application of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) models for predicting Bitcoin prices, with the objective of enhancing investment approaches. Given Bitcoin's significant volatility and inherent risk, accurate price forecasting is vital for making strategic decisions. The research evaluates model performance by analyzing Bitcoin price data spanning May 25, 2020, to May 25, 2024, employing quantitative methodologies like experimental design and predictive analysis. Results indicate the LSTM model achieves superior predictive accuracy over the RNN model, demonstrated by reduced Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) figures. Additionally, the research delves into how these models affect the Dollar Cost Averaging (DCA) investment strategy, offering perspectives on the advantages and drawbacks of using machine learning to optimize cryptocurrency investments. 

Idiosyncratic risk refers to a distinct category of risk that impacts individual firms and is shaped by multiple aspects, including carbon performance and Environmental, Social, and Governance (ESG) disclosures. This risk, particularly concerning environmental sustainability, can provide considerable hurdles for companies. This study aims to examine the influence of carbon performance and ESG disclosure on idiosyncratic risk, with the speed of adjustment serving as a moderating variable. This research examines coal production firms listed on the Indonesia Stock Exchange (IDX) from 2021 to 2023, with a sample of 26 companies selected through purposive sampling. This research utilizes quantitative methodologies, specifically multiple linear regression and a moderating variable approach. Data is derived from annual reports, financial statements, and sustainability reports of coal firms, as well as information from investing.com and the Katadata Corporate Sustainability Index. The data indicate an inverse correlation between carbon performance and idiosyncratic risk, as well as between ESG disclosure and idiosyncratic risk, suggesting that organizations with superior ratings in both domains encounter diminished idiosyncratic risk. Moreover, whereas the speed of adjustment does not reinforce the connection between carbon performance and idiosyncratic risk, it does augment the association between ESG disclosure and idiosyncratic risk.

The arrival of foreign nationals in Indonesia for various purposes, including state visits, tourism, business, and education, has become common. This study aims to analyze the factors influencing the application process for study permits for foreign nationals and map the ecosystem of this process, particularly for those studying at Indonesian universities. The research follows a five-step method to analyze the digital business ecosystem: 1) defining the ecosystem boundaries; 2) identifying actors and their roles; 3) outlining value propositions; 4) exploring actor interactions; and 5) verifying the ecosystem design. Using qualitative methods like interviews and literature review, the study finds that Indonesia's foreign student licensing ecosystem involves multiple actors and complex procedures, leading to delays and confusion. The lack of integration between agencies makes the process inefficient. The study suggests digital integration to enhance speed, transparency, and coordination, aiding the internationalization of higher education in Indonesia.

Stock returns reflect the investment returns investors receive from owning company shares, encompassing both dividends and stock price increases. High stock returns indicate strong company performance and are attractive to investors, often reflecting market expectations of the company's future profitability and contributing to increased company value. Conversely, low stock returns can reduce market confidence and negatively impact company value. This study examines factors influencing stock returns in cyclical consumer sector companies listed on the Indonesia Stock Exchange from 2019 to 2023, with a sample of 63 companies out of 124. The research utilizes principal component analysis (PCA) with regression mediation, analyzing variables such as liquidity, solvency, activity, profitability ratios, financial performance, good corporate governance, and macroeconomic factors. SPSS 25 is used for data analysis. Key findings reveal that Market Value Added (MVA) significantly impacts both stock returns and firm value, while exchange rates and GDP do not significantly affect stock returns or firm value in this sector. The money supply (M2) significantly influences stock returns but not firm value. The Sobel mediation test shows that stock returns significantly mediate the relationship between MVA and M2 with firm value, whereas they do not mediate the effects of exchange rates and GDP on firm value. These results offer insights into the economic and financial factors impacting stock performance and firm value in Indonesia's cyclical consumer sector.