Agus Dendi Rochendi
Papers
1
Total Citations
5
H-Index
1
About
Agus Dendi Rochendi is a researcher focused at the intersection of financial technology, machine learning, and accessible data visualization. His work addresses a critical modern challenge: equipping retail investors with transparent, data-driven tools to navigate volatile markets and avoid fraudulent schemes. His most cited study, "Trading Simulation Using Python and Visualization on Streamlit with Machine Learning Decision Tree" (2022, 5 citations), demonstrates a practical, open-source framework that integrates a decision tree algorithm with an interactive Streamlit interface. This contribution is particularly notable for its educational value, offering a clear, replicable methodology for simulating trades and visualizing outcomes—directly countering the opaque "black box" promises of illegal trading robots that have caused significant investor losses. By prioritizing transparency and user empowerment, Rochendi’s work serves as a bridge between complex machine learning models and everyday financial decision-making. His research is a valuable resource for students and practitioners seeking to build ethical, explainable trading systems, proving that robust simulation and visualization can be powerful tools for both learning and risk mitigation in the digital asset space.
Research Focus
Key Achievements
Top Papers
- 1