Information Systems student at BINUS University · Building at the intersection of data, systems, and business decisions
I enjoy the part of data work where technical detail meets business context: defining useful metrics, cleaning imperfect data, finding the signal, and communicating it clearly. My foundation is in analytics and BI, and I am currently expanding toward data engineering to build more reliable end-to-end data workflows.
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01 · Analyze Explore data, define KPIs, and turn business questions into measurable insights. |
02 · Communicate Build focused dashboards and explain findings in language stakeholders can act on. |
03 · Engineer Develop stronger pipelines, modeling habits, and dependable data foundations. |
| Project | What it demonstrates | Stack |
|---|---|---|
| Kimia Farma Performance Analytics | Business performance analysis from 2020–2023, translated into an interactive decision-support dashboard. | BigQuery Looker Studio |
| SaleCraft Data Wrangling | Auditable cleaning, data-quality checks, and feature preparation from messy retail invoices. | Python Pandas NumPy |
| Retail Sales & Customer Analysis | KPI design and RFM segmentation used to surface retention and sales opportunities. | SQL Server Power BI |
| Smartphone Price Prediction | Leak-safe regression workflow with a tuned SVR model and a deployed prediction app. | scikit-learn Streamlit |
Now: strengthening my data engineering fundamentals through the Data Engineering Zoomcamp, with an emphasis on reproducible workflows and solid foundations.
Build clearly · Measure honestly · Keep learning
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