Customer satisfaction is the most crucial for the marketplace. Unfortunately, poorly showcased product images often create confusion for customers, hindering seller success. While manual review by humans is possible, this might cause slow processing times and potential human error. It will significantly decrease marketplace efficiency.
In this project, we aim to encounter this challenge by automating image quality control through Machine Learning. This Machine Learning acts as an automated validation system, ensuring that product images can be showcased in the acceptable quality by classifying it into 3 classes (Blur, Bokeh, or HD). The system will provide sellers with feedback on their product images, which allows sellers to reupload them to meet the required quality standards. To give sellers flexibility, we’ll develop the system for both mobile and web platforms. Also, the admin role will be implemented to monitor the machine learning performance to ensure it successfully validates the product images.
BrainStore is a marketplace where we implement our product image quality validation system. As a result, BrainStore ensures product image quality that enhances sellers' success. BrainStore operates on two platforms: mobile application and website.
| Learning Path | Student ID | Name | University |
|---|---|---|---|
| Machine Learning | M172D4KY2673 | Frederic Davidsen | Universitas Mikroskil |
| Machine Learning | M700D4KX3318 | Patricia Ho | Universitas Pradita |
| Cloud Computing | C293D4KX1211 | Carissa Chandra | Universitas Pelita Harapan |
| Cloud Computing | C296D4KY0023 | Faiz Nur Budi | Universitas Pembangunan Nasional Veteran Jawa Timur |
| Mobile Development | A293D4KX3909 | Cecilia | Universitas Pelita Harapan |
| Mobile Development | A193D4KY4338 | Dhany Aulia Fajrianto | Universitas Bina Sarana Informatika |
