- Conducted a cluster analysis project on truck operation severity index (OSI) using Python and R on over 1,000 data points, reducing breakdowns and optimizing maintenance scheduling. - Performed exploratory data analysis to identify load distribution patterns, fuel efficiency trends, and outliers, and used PCA for dimensionality reduction to boost model efficiency. - Evaluated unsupervised learning models (K-Means, DBSCAN, agglomerative clustering, and GMM), fine-tuning parameters to improve clustering accuracy and generate actionable insights. - Developed a predictive model for spare parts demand forecasting with 120,000 records, integrating time-series sales data, usage patterns, and inventory movement to predict future part requirements, improving forecast accuracy and inventory management. - Refined forecasting by incorporating time-series techniques, resulting in a 20% accuracy improvement, enabling better alignment of spare parts with customer demand.

Data Scientist Intern at PT United Tractors Tbk

July 1, 2025 - July 1, 2025

Conducted a cluster analysis on truck operation severity index (OSI) using Python and R on over 1,000 data points, reducing breakdowns and optimizing maintenance scheduling. Performed exploratory data analysis to identify load distribution patterns, fuel efficiency trends, and outliers; utilized PCA for dimensionality reduction to boost model efficiency. Evaluated unsupervised learning models (K-Means, DBSCAN, agglomerative clustering, and GMM), fine-tuning parameters to improve clustering accuracy and generate actionable insights. Developed a predictive model for spare part demand forecasting using data with 120,000 records, integrating time-series sales data, usage patterns, and inventory movement to accurately predict future part requirements; improved forecast accuracy and streamlined inventory management. Refined the demand forecasting model by incorporating time-series analysis techniques, achieving a 20% increase in accuracy.