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This study aims to implement an information retrieval system for cryptocurrency data using an attribute-based approach integrated with the Vector Space Model (VSM). The primary objective is to develop a system capable of retrieving the most relevant digital asset information according to specific search attributes, including positive sentiment, price fluctuation, and prediction confidence level. The research adopts a descriptive qualitative method combined with an experimental approach to evaluate the retrieval performance of the cosine similarity algorithm on normalized numerical data. Data preprocessing and attribute weighting were conducted to ensure consistency and improve retrieval accuracy. The experiment demonstrates that the proposed system achieves a Precision@5 value of 1.0, which indicates that all top-five retrieved results are fully relevant to user queries. These findings validate the effectiveness of the attribute-based VSM in analyzing multidimensional cryptocurrency datasets. Overall, this research contributes to the advancement of information retrieval applications in the cryptocurrency domain, particularly for supporting data-driven decision-making and intelligent financial analysis.

This study investigates the relationship between business ethics and VAT implementation with the aim of assessing the combined effect of both on the financial performance of B2B companies. The main theories underlying this study are the Ability to Pay Theory, Stakeholder Theory, and Ratio Analysis Theory. The study uses a positive research philosophy approach and a quantitative case study design with a structured questionnaire involving 30 respondents from PT Bhinneka Mentari Dimensi or Bhinneka.Com, the oldest B2B e-Commerce company in Indonesia. Data analysis was conducted using SPSS using descriptive and inferential statistics. The results show that VAT implementation indirectly affects financial performance through business morality. This mediating role is associated with stakeholder theory and the ability to pay. The low direct effect of VAT is related to an efficient company structure, company size, and reliance on self-reported data. B2B companies should embed business ethics principles in their operations, with management implementing a framework to ensure accountability in tax reporting and practices. The originality of this study contributes to the literature by emphasizing the distinctive role of business ethics as a bridge in the relationship between VAT implementation and financial performance of B2B companies, shifting attention from general tax compliance to specific VAT mechanisms and incorporating ethical dimensions in financial analysis.

Bankruptcy regulations in Indonesia, particularly in Law No. 37 of 2004 on Bankruptcy and Suspension of Debt Payment Obligations, are often contentious due to the absence of strict limitations on the filing of bankruptcy petitions. This has the potential to ensnare debtors who are still financially solvent, overriding the principle that bankruptcy should be the last resort in debt settlement.  Law Number 37 Year 2004 on Bankruptcy and Suspension of Debt Payment Obligations only requires the existence of two creditors with debts that are due and collectible through simple proof. This study aims to analyze in depth the legal arrangements and determination of insolvency in Law Number 37 of 2004. By using a normative juridical research method, as well as a statute approach that focuses on Law Number 37 of 2004 concerning Bankruptcy and Suspension of Debt Payment Obligations and other related regulations, secondary data will be collected through literature studies. Insolvency is defined as a condition where the debtor's total debt to all creditors exceeds the value of all its assets, otherwise known as a state of inability to pay. In Law No. 37 of 2004 on Bankruptcy and Suspension of Debt Payment Obligations, the term "insolvency" differs from commonly used terms, such as balance sheet insolvency, which means debts exceeding assets, or cash flow insolvency, which means the inability to pay debts due to liquidity.  Unlike the practice in the common law system, the determination of insolvency in Indonesia does not involve mandatory financial audits or court decisions based on independent financial analysis.

This study compares different machine learning models for time series forecasting in financial data analysis. Models including ARIMA, LSTM, and GRU are applied to predict stock price movements. We measure the accuracy and computational efficiency of each model on various datasets and discuss their strengths and weaknesses in financial forecasting contexts. The findings suggest that deep learning models show significant improvement in capturing complex temporal patterns over traditional methods.