Computer Science Student
CS student working across machine learning, quantitative research, and full-stack development. I build things like Bitcoin optimization engines and neural network classifiers.

Hello! I'm Benedict Pepper, a Computer Science student with a deep passion for exploring the intersection of data analytics and human-centric software. My journey started with simple scripts, but quickly evolved into building complex machine learning models and full-stack applications.
I focus on building software that handles complex data while staying easy to use. My work ranges from quantitative finance research on Bitcoin market cycles and optimizing trading strategies, to health-tech innovation with an AI-powered herbal medicine scanner, and applied machine learning for sign language recognition. I enjoy making technical backend logic accessible through clean, functional interfaces.
STACK
A timeline of my academic, professional, and leadership growth.
National Student Creativity Program | March 2026 - Present
Collaborating on FitMate, a health-tech innovation project aimed at improving medication safety for Mandarin herbal medicine (TCM). Together with the team, built a working prototype consisting of a Python-based rule-based chatbot and website integrated with the WhatsApp API to help users translate TCM labels and check for potential drug interactions and contraindications.
Ma Chung University | Feb 2026 - Present
Conducting advanced quantitative research on optimizing CBBI (Colin Talks Crypto Bitcoin Bull Run Index) indicator parameters for Bitcoin portfolio performance. Developing an optimization framework using Python and backtesting to tune buying/selling thresholds and asset allocation, validated through In-Sample and Out-of-Sample testing against 3 Bitcoin halving cycles.
Kalam Kudus & Kawai Edulab | Feb 2025 - Mar 2025
Instructed programming to elementary, junior high, and early learner students across two institutions. Delivered hands-on sessions covering Scratch, Python, and Roblox Studio, with a focus on computational thinking and introductory programming concepts.
Informatics Engineering Student Association (HMP) | Oct 2023 - Jun 2025
Managed financial planning and cross-functional teams for major university programs. Coordinated the 'IT School' workshop for 100+ high school students and led the 'Harmony IT' outdoor integration program, successfully optimizing the budget by 53%.
Academic Projects | 2023 - 2024
Spearheaded full-stack and data science projects, coordinating with team members to deliver high-quality applications like 'Lapor Aman' and various analytical tools under tight deadlines.
Various Initiatives | 2023 - Present
Actively engaged in diverse community initiatives. Served on the Health Division for the Ma Chung Festival committee and managed operations for the Smile 3.0 blood donation event. Handled social media and e-commerce platforms for Riverkids Special Education School. Additionally, contributed to campus sports events like the Malang Sportival run and conducted technical workshops for high school students.
A Progressive Web App that helps consumers safely navigate Traditional Chinese Medicine products. Users scan TCM labels with their phone camera — the app extracts and translates Mandarin ingredient text via a Gemini multimodal pipeline, then cross-references a validated toxicity database to flag dangerous compounds and contraindications. When warnings are detected, users are bridged to a rule-based WhatsApp chatbot for hallucination-free medical guidance. Built with a Next.js PWA frontend, Python FastAPI backend, and MongoDB knowledge base.
A web application demonstrating the EfficientNetB4 pneumonia classification research, featuring Grad-CAM visualization for interpretability. It allows users to upload chest X-rays and receive both class predictions and heatmaps indicating the diagnostic regions the model focused on.
An automated medical image analysis tool that applies the Fuzzy C-Means (FCM) soft clustering algorithm to segment brain tumor regions from MRI scans. Built as a Streamlit web application, users can adjust the number of clusters and fuzziness parameter to analyze segmentation results in real time.
An interactive Bitcoin backtesting simulator built as the public deliverable of a PKL undergraduate research project. Users run live backtests with fully customizable CBBI threshold and allocation parameters across 14 years of BTC data, powered by a Numba JIT-compiled engine for fast computation. The app also surfaces optimization outcomes from 1.29 million parameter combinations, with interactive equity curves, CBBI signal overlays, sensitivity heatmaps, and export for trade logs and top parameter sets.
This web application uses an EfficientNet B0 CNN model to identify Indonesian Batik motifs from uploaded images. Created for a Digital Image Processing final exam, the app not only classifies the motif but also provides historical and philosophical information about its origin. It features a Streamlit interface with confidence level histograms and statistical tables.
Keywords: Quantitative Finance, Optimization, Backtesting, On-chain Metrics
This research develops a quantitative optimization framework to find the ideal combination of trading parameters based on historical data. It optimizes buying and selling thresholds and asset allocation using the CBBI indicator to maximize Total Return and Sharpe Ratio while minimizing Maximum Drawdown. The study validates performance through In-Sample and Out-of-Sample testing and involves an interactive simulation dashboard.
Currently open for internships, collaborations, or a chat about machine learning and software development.