Edrick Louis
A Bit About Me
Hi, I'm a final-year Computer Science student at BINUS University with a strong interest in Data Science and financial markets. I enjoy exploring
data analytics and machine learning through projects and research to extract insights and solve real-world problems.
Come and see what projects I have
worked on →
Projects
Systematic Stock Selection Engine: High-Efficiency Filtering with XGBoost
Tech Stack :
yfinance, Pandas, NumPy, scikit-learn, XGBoost, Matplotlib, and Seaborn
A sophisticated machine learning solution designed to identify market outliers with precision. Utilizing XGBoost model and specialized feature
engineering, this model acts as an intelligent filter, efficiently sifting through large datasets to highlight high-potential stocks. It bridges
the gap between massive raw data and strategic decision-making by prioritizing assets with the strongest predictive signals.
Emotion Detection in Indonesian Music Comments Using BERT, Traditional Machine Learning and Their Hybrid Model
Tech Stack :
Python, Pandas, scikit-learn, Transformers, NumPy, Matplotlib, XGBoost, and LightGBM
This project is part of my undergraduate thesis research that explores emotion detection in Indonesian music-related social media comments. It
analyzes 1,000 comments from YouTube and TikTok and classifies them into five emotions using three approaches: traditional machine learning, BERT,
and a hybrid model that combines both methods.
Air Quality Analysis Based on City Population
Tech Stack :
Tableau, Python, and Pandas
This research project aims to explore the relationship between air quality and city population size by analyzing air pollution data from cities
worldwide. Air quality is measured using the Air Quality Index (AQI), which indicates the level of pollution in the air and its potential health
effects. The study categorizes cities into five groups based on population size: small, medium, large, very large, and giant cities, providing
insights into how population density may influence air pollution levels.
A Comparative Analysis of BERT and RoBERTa Model for Sentiment Analysis on Twitter Text
Tech Stack :
Python, scikit-learn, TensorFlow, Transformers, NumPy, Pandas, Matplotlib, and Seaborn
NLP transformers are widely used in sentiment analysis tasks. In this research, we analyze BERT and RoBERTa models in performing sentiment
analysis. The purpose of this research is to know the pros and cons of the both models and the best method possible to achieve the best results.
This paper focuses more on analyzing the effect of hyperparameter tuning (learning rate, epoch, and batch size) of both models in regards to their
accuracy and validation accuracy.
Evaluating the Impact of Latitude and Longitude on Air Quality Prediction: A Comparison Between Random Forest and Gradient Boosting
Regressors
Tech Stack :
Python, scikit-learn, NumPy, Pandas, Matplotlib, and Seaborn
This research compares the effectiveness of Random Forest Regressor and Gradient Boosting Regressor models in predicting Air Quality Index (AQI),
focusing on the role of geographical factors like latitude and longitude as predictive features. The study aims to determine the most accurate
model for AQI prediction by evaluating performance metrics such as Mean Squared Error (MSE) and R² score. Results highlight the predictive value
of adding geographical features, offering insights for improving AQI forecasting accuracy and model selection in environmental data analysis.
ERamen Front-End Website
Tech Stack :
HTML, CSS, JavaScript, and GitHub Pages
ERamen is a front-end-based marketplace website designed to sell various types of ramen online. Built using HTML and CSS, the website has three
main features: a Home page to promote featured ramen, a Menu page where users can view and select their favorite ramen, and a Contact Us page that
allows users to provide feedback or suggestions. The project focused on an attractive and functional design to provide an intuitive user
experience, while also training my skills in front-end-based website development.
Certificates
STATEMENT FROM THE BOARD OF EXAMINERS
Successfully passed the S1 Thesis Defense Examination conducted on Friday, 6 February 2026
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Data Analysis with Python
Succesfully completed and received a passing grade in Data Analysis with Python. A course on cognitiveclass.ai, powered by IBM Developer Skills
Network.
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SQL and Relational Databases 101
Succesfully completed and received a passing grade in SQL and Relational Databases 101. A course on cognitiveclass.ai, powered by IBM Developer
Skills Network.
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Professional Office
Succesfully completed English C1.2-Level courses and received passing grade for Professional Office. A digital language learning system provided
by Binus University.
Advanced English
Succesfully completed English C2.1-Level courses and received passing grade for Advanced English. A digital language learning system provided by
Binus University.