• About the Project
• Team
• Repostory
  • Cloud Computing
  • Machine Learning
  • Mobile Development
  • Web Landing Page
• Special Thanks
• Contributing
• Acknowledgements

Our team was inspired by the issue of limited stock or options available to most people who only require them for a short period or specific events. As a solution, we developed an e-commerce-based rental application called "BarKit (Barang Kita)" that shares similarities with traditional e-commerce platforms but includes options for returning items according to agreed-upon timeframes. Our application utilizes Machine Learning to validate uploaded item listings with inputted categories, thereby avoiding clickbait often encountered on other e-commerce platforms.

ID	NAME	PATH	University	CONTACT
C177DSX0867	Muhammad Thoriq Ali Said	Cloud Computing	Hasanuddin University
C151DSY3280	Dian Ayu Rahmawati	Cloud Computing	Universitas Brawijaya
A366DSX3591	Robby Alamsyah	Mobile Development	Trunojoyo University
M177DSX0401	Awang Mulya Nugrawan	Machine Learning	Hasanuddin University
M281DSY2306	Andi Illa Erviani Nensi	Machine Learning	Makassar State University
M163DSX3465	Naufal Mahardika Putra Rumdani	Machine Learning	Dian Nuswantoro University

BarKit API built using Exprees.js, you can see all the BackEnd API code & installation in this BackEnd Repository

Teach Stack:

1. Express.js, used for developing APIs. With this popular framework, we can easily create high-performing APIs.
2. Firestore, which serves as a solution for storing text data due to its high speed, scalability, and flexibility.
3. Cloud Storage, selected as the storage service for image data.
4. Cloud Run, chosen based on our need to quickly deploy APIs without directly managing the infrastructure.
5. Firebase Auth, We chose Firebase Auth to set up authorization because of its easy integration with Express.js

BarKit App Using Machine Learning to predict images product. The model were build using Convolutional Neural Network.

You can see information about our machine learning code at this Machine Learning Repostory

Teach Stack:

1. Scikit-learn, for create system recommendation with algorithm cosine Similarity to provide recommendations for similar items.

2. Convolutional Neural Networks, using VGG16 transfer learning for classification of 8 categori of item images

3. TensorFlow.js, to save the model to Json and deploy it to express.js on the backend side

BarKit is an application built using Kotlin programming language, you can see all the mobile development code in this Mobile Repository

Teach Stack:

1. Android Jetpack, which makes the app development process easier, more efficient, and reduces boilerplate code.
2. Retrofit, which is used to fulfill all API-related needs in the app.
3. Koin, which simplifies dependency management by implementing dependency injection (DI).
4. Firebase Analytics, which is used to track user interactions

BarKit Landing built using React.js, you can see all the BarKit Landing Page web code in this Landing Page Repostory

1. React.js, is a popular JavaScript framework known for its component-based approach, allowing for modular development and easier code maintenance.
2. Vite, is a fast and lightweight web development tool designed for modern JavaScript and TypeScript development, providing quick compilation and responsive development server.
3. Tailwind CSS is, a flexible and user-friendly utility-first CSS framework, offering a wide range of utility classes for effortless layout design, resulting in consistent and responsive landing page styling.

Contributions are always welcome! All types of contributions are encouraged and valued. Please make sure to read the contribution.md file at every repostory before making your contribution. It will make it a lot easier for us maintainers and smooth out the experience for all involved. The community looks forward to your contributions. 🎉

And if you like the project, but just don't have time to contribute, that's fine. There are other easy ways to support the project and show your appreciation, which we would also be very happy about:

• Star the project
• Tweet about it
• Refer this project in your project's readme
• Mention the project at local meetups and tell your friends/colleagues

Bellow is useful resource that we used to make this readme.md

• Official Github Readme.md
• Github Docs
• Readme.md Inspiration
• Emoji Resource