Personal Portfolio
Anwar Robiansyah
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Informatics Graduate | Data & Analytical Enthusiast. Fresh graduate in Informatics with experience in data-related projects, organizational activities, and collaborative learning.
About Me
I'm a recent Informatics graduate from Universitas Siliwangi with a GPA of 3.76/4.00. Along the way, I found myself drawn to data — how it's collected, cleaned, and turned into something useful. That interest took shape through a Junior Data Science experience focused on data collection and presentation, the Samsung Innovation Campus, and a final project on fraud detection. Outside the classroom, I stayed active in organizational work, which sharpened how I communicate and collaborate. I see myself as someone who is curious, organized, and always ready to learn something new.
My Journey
- 1UniversityInformatics
- 2Junior Data ScienceData Collection & Presentation
- 3Samsung Innovation CampusProject & Collaboration
- 4Organizational ExperienceCommunication & Leadership
- 5Final ProjectData Processing & Analysis
Institution
Universitas Siliwangi
Degree
S1 Informatika
Academic Standing
Fresh Graduate
GPA
3.76 / 4.00
Areas of Focus
- Statistics
- Data Analysis
- Research
- Information Systems
- Programming
- Problem Solving
Data Experience
- 1COLLECT → CLEAN → PROCESS → ANALYZE → PRESENTJunior Data Science — Hands-on data collection and presentation work
- 2Final ProjectAcademic research on fraud detection using data
- 3Academic ProjectsCoursework involving data-driven problem solving
Junior Data Science Experience
- Details
Data Collection & Data Presentation
Junior Data Science
Challenge: Finding data that was both accurate and relevant.
- Searched for relevant data sources
- Collected data for the team
- Presented data before it was processed
- Checked relevance and validity of sources
- Learned that data quality matters before any analysis begins
Junior Data Science Skills
- Data Collection
- Research
- Validation
- Teamwork
- Presentation
Samsung Innovation Campus Batch 7
- Details
Team Leader
Samsung Innovation Campus — Batch 7
Project: IoT-Based Room Condition Classification
- Developed an IoT-based room condition classification system using DHT11, ESP32, and Random Forest to classify temperature and humidity into Hot, Comfortable, and Humid conditions, with real-time monitoring through Streamlit and LED indicators.
Samsung Innovation Campus Technology
- Python
- Random Forest
- ESP32
- DHT11
- Streamlit
- Machine Learning
Featured Project: Fraud Detection Using Stacking Ensemble
Detecting fraudulent transactions is challenging because fraudulent transactions represent only a very small portion of the dataset.
Dataset
Credit Card Transaction Dataset
Process
Preprocessing → Feature Engineering → SMOTE → Cost-Sensitive Learning → Stacking Ensemble → Evaluation
Type
Final Project / Thesis
Project Deep Dive — From Data to Insight
- 1Raw DataStarting point: the credit card transaction dataset.
- 2PreprocessingData preparation and cleaning for modeling.