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All Journal J-CEKI Brilliant International Journal of Management and Tourism
Bustanul Ulum
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Humanities Economics, Econometrics & Finance Social Sciences

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Solvabilitas sebagai Cermin Nilai pada Perusahaan Properti dan Real Estate yang Terdaftar di BEI Fatimah, Siti; Anisaul Hasanah; Bustanul Ulum
J-CEKI : Jurnal Cendekia Ilmiah Vol. 4 No. 5: Agustus 2025
Publisher : CV. ULIL ALBAB CORP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56799/jceki.v4i5.10490

Abstract

This study investigates the effect of solvability on firm value in the property and real estate sector listed on the Indonesia Stock Exchange for the period 2019–2023. The research is grounded in agency theory and signaling theory, emphasizing the importance of financial risk management in influencing market perception. Using a quantitative associative approach with purposive sampling of 50 companies, solvability is measured by Debt to Equity Ratio (DER) and firm value by Tobin’s Q. The results indicate that solvability has a negative and insignificant effect on firm value, suggesting that high leverage may reduce investor confidence. The study highlights the need for prudent debt management to maintain financial stability and firm valuation.
Artificial Intelligence in Financial Forecasting : Enhancing Accuracy and Strategic Planning in Financial Management Sulistiani Sulistiani; Adiba Fuad Syamlan; Bustanul Ulum
Brilliant International Journal Of Management And Tourism Vol. 5 No. 2 (2025): June : Brilliant International Journal Of Management And Tourism
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/bijmt.v5i2.4455

Abstract

This study explores the implementation of Artificial Intelligence (AI) technologies in financial forecasting, aiming to improve prediction accuracy and enhance strategic financial decision-making. Traditional forecasting methods, such as ARIMA and linear regression, often fall short in modeling complex, nonlinear financial data, especially in volatile markets. In response, this research investigates the comparative performance of machine learning (ML), deep learning (DL), and hybrid AI-big data models. A qualitative exploratory approach was adopted, involving a systematic literature review and semi-structured interviews with financial practitioners and experts. The analysis revealed that hybrid models integrating Random Forest with big data analytics achieved the highest predictive accuracy (93.2%) and operational adaptability. LSTM models also demonstrated strong performance in handling time-series data but were limited by their lack of interpretability. Compared to traditional models, AI-based approaches significantly reduced prediction errors and offered real-time responsiveness, aligning with the dynamic needs of financial environments. The findings support the hypothesis that AI technologies can bridge the gap between accurate forecasting and strategic financial planning. However, challenges such as high computational requirements and low model transparency persist. Therefore, the study concludes that while AI models present a transformative potential for financial forecasting, successful implementation requires balancing model performance with organizational capabilities and regulatory considerations. These insights provide valuable guidance for financial managers and policymakers seeking to adopt AI-driven forecasting systems in increasingly complex and data-rich financial landscapes.
Co-Authors Adiba Fuad Syamlan ANISAUL HASANAH Siti Fatimah SULISTIANI SULISTIANI