I am a Bachelor of Information Systems graduate from Digital Indonesia University of Technology. Having a great interest in web-based programming, I am skilled in programming languages such as Golang and Node.js, and have the ability to manage databases and build APIs. I am committed to continuously learning and deepening my knowledge and skills as a Backend Developer, Backend Engineer, and Software Engineer.

Task Tracker Plus is an application designed to assist students in organizing study schedules, built using the Go (Golang) programming language and implementing REST API and MVC concepts. This application is a monolith, where the entire system shares the same server, logic, database, and user interface. Users can register, login, and view task and category lists through various endpoints such as /user/register, /task/add, and /category/list. The application also allows adding, updating, and deleting tasks and categories, with interaction with the database using functions in the db/filebased subpackage.

Based on the analysis of the Data Analysts team, increasing user engagement in m-banking applications can be achieved by strengthening user engagement through personalization features, such as allowing users to upload profile photos. The suggestion encouraged the development team to develop a profile photo upload and delete feature, where users who have logged in or signed up can add or delete their own pictures, with a system that ensures only the user has control over their photos. The API designed in GoLang should include functions for adding and deleting profile photos, user identification through login/sign up, and preventing other users from changing or deleting other users' photos.

an open music player app called OpenMusic, which provides free licensed music to everyone. The app is designed to be constantly evolving, with features such as adding songs, creating playlists, putting songs into playlists, and sharing playlists with other users. OpenMusic is projected to become the number one music app in the world. Currently, the app is in the first version stage, but it cannot be released to the public because the API is not fully ready. At this stage, the API is expected to handle adding, deleting, and changing album and song data entered by users.

Heart failure is a condition in which the heart is weakened to the point that it is unable to pump enough blood throughout the body, often occurring in people over 65 years old and being one of the highest causes of death in the world. To overcome this problem, machine learning technology can be used as a solution by developing a heart failure disease prediction system. This system is expected to help medical personnel detect the disease earlier, so that prevention and treatment steps can be taken more quickly and effectively. This project uses data processing methods by removing irrelevant features, dividing the dataset into training and evaluation data with a ratio of 8:2, and performing data validation and preprocessing. The model architecture includes input layers for categorical and numerical data, two hidden layers, one Dropout layer, and an output layer. Model evaluation uses metrics such as AUC, Precision, Recall, ExampleCount, TruePositives, FalsePositives, TrueNegatives, FalseNegatives, and BinaryAccuracy to assess classification performance.