Muhammad
Wibi Maulana
AI/ML Engineer & Industrial Engineering Graduate specialized in deep learning, time series forecasting, and business optimization solutions.
Core Expertise
Specialized in AI/ML technologies with strong foundation in data science and business optimization
Experience & Education
Professional journey combining technical expertise with practical industry experience
Work Experience
Freelance AI/Machine Learning Engineer
- Develop and optimize AI models for tasks like time series forecasting, computer vision, agentic AI integration and ensuring efficiency and scalability.
- Clean, preprocess, and analyze data to improve model accuracy and automate data pipelines for seamless integration
- Deploy and maintain AI solutions, implementing APIs, cloud services, and monitoring systems to ensure optimal performance.
PPIC and Warehouse Automation Intern
- Creating detailed daily production schedules based on demand forecasts, inventory levels, and production capacity.
- Collaborating with production teams to address scheduling conflicts, optimize workflow, and resolve any issues that may impact production efficiency.
- Ensuring accurate and timely updates of raw material stock levels when new shipments arrive. Maintaining data integrity to support procurement and production planning decisions.
- Enhancing warehouse efficiency by implementing automation and digitalization, leveraging data analysis and machine learning to optimize inventory management.
Education
Industrial Engineering
Institut Teknologi Sepuluh Nopember
Organization Experience
Head of Entrepreneurship Development
HMTI ITS
- Organized BIESCAMP, a major event attended by 289 participants, to enhance the skills and interests of ITS Industrial Engineering students in entrepreneurship.
- Managed the sales of various merchandise by developing effective marketing strategies to boost revenue.
- Oversaw the operations of a shop within the Industrial Engineering Department, including stock management and financial oversight to ensure profitability.
Research & Publications
AI-Based Inventory Optimization in the Tobacco Industry: A Comparative Study Between Deep Q-Learning and EOQ ROP
Industrial Engineering & Management Systems
This study demonstrates that implementing the Deep Q-Learning algorithm reduces inventory management costs by up to 20.21% compared to conventional EOQ and ROP methods in addressing demand uncertainty in the tobacco industry.
Projects
Computer Vision Object Tracking Project
Using computer vision for tracking object at warehouse case
Sign Language Detection Using Computer Vision
A deep learning-powered system that leverages computer vision to accurately detect and classify sign-language gestures in real time.