Electrical Engineering Student/AI Enthusiast

Personal Skills:

Technical Skill	Soft Skill	Tools
C	Problem Solving	TensorFlow
C++	Critical Thinking	OpenCV
Python	Adaptability	MySQL
HTML	Communication	Scikit-Learn
CSS	Project Management	Android Studio
Java	Leadership	GitHub
SQL	English Intermediate	Pandas
Git	Teamwork 🤝	Docker
Haskell	Analytical Thinking 🧠	Tableau
Kotlin	Creative Problem Solving 💡

Education

• Electrical Engineering, Cumulative GPA:3.60/4.00 | University of Brawijaya (2021-Present)
• Machine Learning Learning Path, Avg Score: 95.1/100 | Bangkit Academy led by Google, GoTo, & Traveloka (2023)
• Machine Learning Learning Path, Expert Class | Lintasarta Cloudeka Digischool 2023 (2023-2024)

Experience

Aeronautics Workshop

• Research projects focusing on Unmanned Aerial Vehicles (UAV) or other air flight-capable machines
• Competed in the KRTI 2024 VTOL division as team captain 🚁 Aeronautics

Computer & Network Laboratory, Brawijaya University

• Assisted in creating learning modules for laboratory experiments (C & Python)
• Conducted research on various programming languages, including game development 🎮

NUS x Faculty of Engineering, Brawijaya University

• Collaborated with NUS students to explore Sustainable Development Goals (SDGs) in Southeast Asia, focusing on Malang, Indonesia.
• Acted as a translator and tour guide for NUS students in Malang 🌏

Projects

DishDash: Smart Meal Planner App 🍽️

DishDash

DISHDASH, an innovative application for meal planning, addresses common challenges faced by students and the general public in Indonesia. It offers restaurant menu recommendations 🍱 to promote healthy eating habits 💪, assist in making nutritious choices ⏳, and manage budget constraints 💸.

Flower Classification Model (End to End) 🌸

Flower Classification

Developed a machine learning model to classify flowers, comparing six different models. Achieved highest accuracy using transfer learning with InceptionV3 and LeakyReLU activation. Deployed on TensorFlow Lite (TFLite) for real-time mobile classification📱.

NLP Model Using MLOps (End to End) 📰

Fake New Detection

Addressed the challenge of fake news detection, achieving Accuracy: 85.37% and AUC: 91.64%. Deployed on Lintasarta Cloudeka Server, supporting efforts to combat misinformation 🚫.

LSTM Model for Weather Forecasting ☀️

Weather Forecasting

Developed a time series forecasting model for predicting maximum temperatures in London. Combined CNN and LSTM layers, trained with Huber loss and SGD optimizer. Demonstrated practical applications in weather prediction 🌦️.