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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. 

Modern retail businesses in Indonesia are growing rapidly thanks to open market opportunities, manufacturing businesses that support product supply, and government support for economic growth through retail development. The purpose of this study is to determine the intrinsic value of shares of companies engaged in the food and staples retail sub-sector which in this case is represented by PT. Sumber Alfaria Trijaya, Tbk. (AMRT), PT. Diamond Food Indonesia, Tbk. (DMND), PT.  Midi Utama Indonesia, Tbk. (MIDI), PT. Enseval Putera Megatrading, Tbk. (EPMT), and PT. Millenium Pharmacon Internatioanl, Tbk. (SDPC) listed on the Indonesia Stock Exchange (IDX). This research uses historical data for 2018-2022 which will be used as the basis for projections for 2023-2027. The results showed that by using the Discounted Cash Flow (DCF) method, specifically the Free Cash Flow to Firm (FCFF) approach, under an optimistic scenario, the intrinsic values of AMRT, SDPC, and DMND were undervalued. EPMT and MIDI stocks are overvalued. In the moderate scenario, the intrinsic values of AMRT, MIDI, EPMT, and DMND are overvalued, while SDPC is undervalued. Calculations on a pessimistic scenario, all companies studied are overvalued. In this study, the valuation of stocks was also conducted using the Relative Valuation (RV) method with the Price to Earnings Ratio (PER) and Price to Book Value (PBV) approaches. In an optimistic scenario, the stocks of AMRT, MIDI, and SDPC are considered expensive, while EPMT and DMND are seen cheap. In a moderat scenario, the stocks AMRT, EPMT, and SDPC are considered undervalued, while MIDI and DMND are seen expensive. In a pessimistic scenario, it is observed that AMRT, EPMT, and SDPC are undervalued, whereas MIDI and DMND are considered overvalued. This research aims to provide an objective assessment of the company, which investors can use to consider investments and by companies to enhance corporate value.

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.

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.

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. 

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.

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. 

This study delves into the performance of Indonesian state-owned enterprises (BUMN) stocks from February 2021 to October 2023, attracting investor attention due to their strategic position in the national economy. However, the stock performance during this period exhibited suboptimal movements, failing to surpass the returns of the Indonesia Stock Exchange Composite Index (IHSG). To address this, a prudent investment strategy is essential, encompassing portfolio construction and management. In portfolio construction, the study employs proven smart beta strategies, known for delivering superior returns compared to traditional portfolios. Portfolio management involves testing two strategies: a passive approach utilizing the buy-and-hold technique and an active approach employing portfolio recomposition. This quantitative descriptive study utilizes historical closing price data, forming portfolios based on selected beta groups. Weighting is adjusted, with higher beta stocks receiving a larger allocation in the portfolio. The research compares two beta groups (high and low) and two investment strategies (active and passive) using the Sharpe, Treynor, and Jensen indices. The results indicate that the high-beta group and active strategy outperform, aligning with the investment adage "high risk, high return." The high-beta group yields higher returns due to its increased risk profile, consistent with the notion of high-risk, high-reward investments. The active strategy provides opportunities to discard underperforming stocks and enhance the weighting of well-performing ones during recomposition. These findings are valuable for investors seeking to capitalize on BUMN stocks at lower prices. The study aids in minimizing negative returns (losses) by identifying the most suitable strategy for BUMN portfolios. Investors can leverage this research to make informed decisions and navigate the challenges associated with investing in state-owned enterprises.