GetFix

An App to fix your lovely gadget

About the Project

According to data from Ministry of Communication and Information Indonesia say that 89% of Indonesia citizen use gadget for their daily life, However, in its use, gadgets still have many shortcomings, one of which is vulnerable to water, impact, hot temperatures and so on..This issues create opportunities for repair service business owners to develop their business, by seeing the growing development of this business we took the initiative to create an application that can be a forum to bring together users with these repair service business owners

Team Members

Name	Student ID	Path	University
Dinda Lusita Fristiani Anisa	C2191F1816	Cloud Computing	Universitas Bina Darma
Siti Fatimatuzzahro	M7329G2853	Machine Learning	Universitas Tidar
Muhammad Nur Fauzi	A2329G2852	Mobile Development	Universitas Tidar
Sania Febriani	C2191F1817	Cloud Computing	Universitas Bina Darma
Andrea Satria Nagari	M7329G2854	Machine Learning	Universitas Tidar
Dendi Fazar Zaman	A2128F1571	Mobile Development	Politeknik Negeri Jakarta

1. Machine Learning

In this repository, files contain in the Image Classification and recomender directory are the final architecture that is most suitable for our project. We have done some research and development to build this project model.

Tasks for the Machine Learning team:

• Collecting and Cleaning Dataset
• Build Model
• Train and Test Model
• Model Deployment

Image Classification

the model have 2 classes first is screen crash and second in blue screen,for the dataset for image Classification it have 600 images which is split to 300 for screen crash and 300 for blue screen.We build a model using conv2d,max_pooling 2d,dense and flatten layer,after we build a model we train it and we reach 0.97 accuracy.

Recomender

we using Multiclass Classification using structured data model that have 16 class depend on what user choose in the input.for the build a model we using StringLookup,CategoryEncoding,Concatenate,and Dense layer.after the model done we train it and give a 0.93 accuracy.

For more information about the model.Please consider visit this link below:

• Image Classification
• Recomender

2. Mobile Development

In this repository,files contain in app directory are the code for build the android application

The tech we use for create this app :

• Kotlin
• Firebase
• Retrofit
• Material design
• hilt
• Image Slider Show denzcoskun
• Rating Bar zhanghai
• Glide
• Tensorflow

Here the screenshot of the app :

Link for download the APK can be found here

3. Cloud Computing

in this repository,file contain in cloud directory are the final code for this project

Tasks for Cloud Computing team:

• Creating the logo of the Application
• Creating UI design of each menu and feature of the Application
• Building RESTful API using NodeJS and others library
• Testing API locally with postman
• Creating a project in Google Cloud Platform and setting the billing
• Add team members and manage roles with IAM & Admin
• Preparing Storage with Google Cloud Storage
• Preparing Database with Cloud Firestore
• Deploying the backend using App Engine with standard environtment

for the API documentation visit this link