Sulistiyono Sulistiyono

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About

Sulistiyono Sulistiyono is a researcher focused at the intersection of financial technology, machine learning, and data visualization. His work addresses critical challenges in modern trading, particularly the need for transparent, educational tools that help investors avoid fraudulent schemes. His most-cited paper, "Trading Simulation Using Python and Visualization on Streamlit with Machine Learning Decision Tree" (2022, 5 citations), demonstrates a practical approach to demystifying trading by combining Python-based simulation with interactive Streamlit dashboards and decision tree algorithms. This contribution is especially timely given the rise of illegal trading robots and misleading investment platforms in early 2022, offering users a safe, simulated environment to learn trading strategies and understand machine learning-driven decision-making. Sulistiyono’s research bridges the gap between complex financial models and accessible education, empowering both novice and experienced traders to make informed decisions. By integrating visualization with predictive analytics, he provides a replicable framework for risk-aware trading simulation. His work continues to influence the development of ethical, user-friendly tools in fintech, promoting financial literacy and safer investment practices in an increasingly digital economy.