Advantage

Transforming Out-of-Home (OOH) advertising in Indonesia with a modern marketplace that integrates traffic data with advertising spaces.

Overview

Project Advantage aims to transform Out-of-Home (OOH) advertising in Indonesia by introducing a modern marketplace that integrates traffic data with advertising spaces. This initiative addresses the inefficiencies of traditional OOH ads by providing a data-driven platform for advertisers to maximize their impact.

Problem Statement

Traditional OOH advertising methods in Indonesia are plagued by inefficiencies:

• Lack of real-time data for measuring ad effectiveness.
• Time-consuming booking processes.
• Limited targeting capabilities leading to suboptimal ROI for advertisers.

Solution

Advantage offers a comprehensive solution:

• Marketplace: A centralized platform for buying and selling OOH ad spaces.
• Traffic Data Integration: Utilizes machine learning models to analyze traffic patterns, enabling precise ad placement.
• Mobile App: User-friendly interface for advertisers to browse, book, and manage ad campaigns seamlessly.

Key Features

Machine Learning Component:

• Creates and utilizes a traffic dataset.
• TensorFlow-based recommendation system for targeted ad placement.

Mobile App Development:

• Built with Kotlin for Android.
• Features include Google Maps integration, state management with View Model and LiveData, and MVVM architecture for scalability and maintainability.

Cloud Computing Infrastructure:

• Backend development using Google Cloud Platform (GCP).
• Utilizes Docker for containerization and Cloud Run for scalable deployment.
• Firestore for data storage and Cloud Storage for machine learning model deployment.

Team Collaboration

• Machine Learning Team: Focused on data analysis and building recommendation systems.
• Mobile Development Team: Designed and developed the user-friendly mobile app, integrating seamlessly with backend services.
• Cloud Computing Team: Established a robust backend infrastructure on GCP to support app functionality and machine learning model deployment.

Future Enhancements

• Enhanced Analytics: Implement real-time analytics for advertisers to monitor campaign performance.
• Expanded Ad Inventory: Increase the variety of OOH ad spaces available on the platform.
• Personalized Recommendations: Utilize user behavior data to offer personalized ad recommendations.

License

This project is licensed under the MIT License.

Authors

Acknowledgments

Usage