lgamal

Hi 👋, I'm William Wibowo Ciptono

A data & software enthusiast from Indonesia.

William

I. About Me

🌱 I’m currently learning Frontend QA Automation Tools (Espresso, uiautomation, xcuitest, etc.) using Java & Obj. C
💬 Ask me about System Design, Complex Query, etc.
📫 Reach me via williamciptono@gmail.com or Linkedin
📄 Know about my experiences by reading my curriculum vitae

II. Some of My Projects

1. Blanche: Minimalist E-Commerce Full-Stack Website

Repository: https://github.com/lgamal/blanche
Deployment: -

2. Sushee: Japanese Restaurant Online Order Full-Stack Website

Repository: https://github.com/lgamal/sushee
Deployment: -

3. Simple E-Wallet Backend Service

This is the E-Wallet API. This whole project was built using Golang, PostgreSQL, and JWT in a RESTful manner. For serving the data to the endpoints & also connecting the API to the database, I’m using the Gin & Gorm packages. The user may sign-up, login & get credentials, and also reset their password. Besides, they are also able to see transaction history, top up, and transfer to another account. More details about the API’s features could be seen in the documentation. You can access the documentation by requesting a GET method to /docs.

Repository: https://github.com/lgamal/e-wallet
Deployment: -

4. MasTrendy: Discord Bot - Google Trend Broadcaster

Mas Trendy is an Open Source, Google-trend & Google-search-powered Discord Bot. Equipped with daily-push-notification about the current trends based on the Google search engine and manual-push-notification to search current trends in each country.

Repository: https://github.com/lgamal/MasTrendy
Deployment: -

5. Hotel Booking Cancellation Prediction Model

A successful predictive model has been built to categorize orders as either having a potential for cancellation or being less likely to be canceled, achieving an accuracy of 83.56%. The model, based on a random forest with hyperparameters ‘max_features’ set at 10 and ‘n_estimators’ at 200, utilized a range of chosen features, including both direct selections and engineered attributes through feature engineering. Four key factors crucial to order success were identified using feature importance and the highest average Gini score; these factors encompass lead time, ADR, arrival date week number, and deposit type.

Repository: https://github.com/lgamal/Hotel-Booking-Cancelation-Prediction-Model
Deployment: -