Hello, Welcome to Aedentrisa Yasmanda Paulindino's Portfolio
I am a Student Of Computer Science in Binus University, Jakarta, Indonesia.
I am a Student Of Computer Science in Binus University, Jakarta, Indonesia.
Toefl Absence is one of the features that I created for the TOEFL BNEC (Binus English Club) Website when I joined the IT division of BNEC. This feature contains functions to search for registered student ids and fill in attendance absences.
Red velvet fansite is a website for red velvet fansite to see the details about their idol. The contents of the websites are informartion about Red Velvet, their latest song and music video, also the awards that they have recieved.
Doto 2 is a modest online project targeted for gamers. This project is done in groups, and I'm in charge of the "Merchandises" menu, which covers all of the items available for purchase on the Doto 2 website.
Binus Peduli is a website prototype project designed to show the user interface and user experience of a fundraising website. This project was done in groups, and I was given the task of writing a feature story that included any current news stories in Binus Peduli.
Office Hold TM is a store that provides furniture. I created an overview of the user interface for the home page that will be presented when the user opens this application in this project. There are two versions of the Office Hold TM user interface (Website and Mobile).
PWNI is a website prototype project that I created to complete a Human and Computer Interaction (HCI) course which contains an overview of the UI of a data management website. This website prototype has various features such as storage for storing files, and also online chat.
Coffe Vibes is an application built to manage Coffee Vibes cafe business activities. This project was carried out in groups, where I was in charge of creating a “Manager” menu where I could see the detail transactions products and fire employees.
This project was carried out in teams, with the goal of determining which features have the greatest influence in diagnosing whether or not a patient has diabetes using diagnostic measurements from the dataset. We use models like the "Random Forest Classifier," "TPOT Classifier," and "XGBoost Classifier" to do this.
