Fresh graduate in Information Systems (Big Data Analytics) with real-world experience at PT Bank Central Asia. I build data pipelines, analytics dashboards, and machine learning models that drive measurable business outcomes.
I'm a recent Information Systems graduate from Universitas Multimedia Nusantara (GPA 3.91/4.0), specialising in Big Data Analytics. My focus sits at the intersection of data engineering, analytics, and machine learning — building systems that don't just report the past, but help organisations act on the future.
Over the past year I've interned across two divisions at PT Bank Central Asia (BCA) — one focused on customer analytics and automation, the other on ETL infrastructure and internal reporting platforms. I've shipped production-grade dashboards, ETL pipelines serving 5 business domains, and API integrations used by bank employees daily.
Outside of work, I research predictive modelling for financial assets. My thesis explored Bitcoin and Ethereum price prediction using LSTM, GRU, and XGBoost with macro-financial indicators — the best model (XGBoost) achieved a MAPE of 2.18% for Bitcoin and was deployed as a live Streamlit application.
Here are some technologies I work with regularly:
Multivariate time-series forecasting for next-day Bitcoin and Ethereum prices using LSTM, GRU, and XGBoost, with macro-financial indicators (Gold, US Dollar Index, S&P 500). Two feature scenarios were evaluated; the best-performing models were deployed in an interactive Streamlit application with a forecast log and model info panel.
Included Wilcoxon signed-rank statistical significance tests and SHAP feature importance analysis to validate model quality and interpretability.
Interactive Power BI dashboard visualising telesurvey data (CSI, NPS, CSAT) for BCA's executive management. Python preprocessing pipeline with drill-through views and period-comparison analytics — replaced an entirely manual Excel-to-PowerPoint workflow.
End-to-end ETL pipelines using SSIS covering extraction, transformation, staging, and final table creation across 5 business domains. Automated report distribution via email and self-service SSRS web portal with standardised layouts and parameters.
Compared six machine learning models (Gradient Boosting, Random Forest, Decision Tree, SVM, Neural Network, Bayesian Network) to predict customer churn. Gradient Boosting achieved the best accuracy at 86.88%; identified Age as the strongest churn predictor.
CNN model classifying 12 categories of household waste using MobileNetV2 with fine-tuning on a 6,000+ image dataset. Achieved 85.56% accuracy — outperforming vanilla MobileNetV2 (80.49%). Deployed as a real-time Streamlit web application.
Applied predictive, prescriptive, text, and sentiment analytics using the DCOVA framework to identify drivers of bank deposit subscriptions. Integrated forecasting, machine learning classification, and NLP sentiment analysis on customer comments.
I'm actively looking for full-time opportunities in data analytics, data engineering, or machine learning. Whether you have an open role, a project idea, or just want to say hello — my inbox is always open.
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