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All Journal EIGEN MATHEMATICS JOURNAL Advances in Taxation Research
puspita, tika
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Economics, Econometrics & Finance Mathematics

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Tax Planning in Transition: Evaluating the Impact of Evolving Legislation on Financial Strategies Puspita, Tika
Advances in Taxation Research Vol. 1 No. 3 (2023): June - September
Publisher : Yayasan Pendidikan Bukhari Dwi Muslim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60079/atr.v1i3.238

Abstract

Purpose: This research investigates the dynamics of tax planning amidst legislative changes and transitions in the modern tax environment, focusing on the interaction between tax law changes, economic dynamics, and strategic tax planning. Research Design and Methodology: This research design adopts qualitative analysis to explore various dimensions of tax planning, including economic, political, ethical, and strategic perspectives. Findings and Discussion: The findings suggest that tax planning is multifaceted, with the importance of constantly monitoring legislative changes, cross-disciplinary collaboration, and proactive strategic management. Implications: The research emphasizes that effective tax planning requires an approach responsive to regulatory change and integration with broader financial objectives.
Analisis Optimasi Hiperparameter Bayesian untuk Model Prediksi Kinerja Inovasi Berkelanjutan Puspita, Tika
Eigen Mathematics Journal Vol 8 No 2 (2025): December
Publisher : University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/emj.v8i2.266

Abstract

This study examines how well the Gaussian Process Regression (GPR) model performs in interpreting the optimization outcomes achieved through Bayesian Optimization (BO) with Keras Tuner, specifically in the context of Sustainable Innovation Performance (SIP). The GPR surrogate model serves to examine the outcomes of optimization and offers valuable insights into the strategies of exploration and exploitation while seeking the most effective hyperparameters. The evaluation of the effectiveness of GPR involved calculating the Mean Absolute Error (MAE), which was bootstrapped 1000 times to establish a 95\%. Confidence Interval (CI). This study's findings demonstrate the dependability of GPR in forecasting the validation loss generated by BO, characterized by minimal prediction errors and consistent confidence intervals. The results indicate that GPR serves as a dependable statistical method for assessing uncertainty in Bayesian-based optimization. Additionally, they offer valuable perspectives on how exploration and exploitation strategies can be utilized to attain optimal hyperparameter configurations. By strategically balancing exploitation and exploration, Bayesian Optimization can enhance the process of identifying optimal hyperparameter configurations within continuous innovation prediction models.
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