Node.js
Experienced
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1+ Year Work Experiences
Ex-Intern Telkom Indonesia
Ex-Intern Ministry of Finance
13 Total Projects
Telkom University (2020)
Software Engineering
Purwadhika School (2024)
Data Science & ML
Software Engineering graduate from Telkom University with a GPA of 3.91 and over a year of relevant work experience through internships at Telkom Indonesia and the Ministry of Finance. Skilled in building and maintaining backend systems, with expertise in Node.js, Golang, and Python. Proficient in database management across SQL and NoSQL systems, with a strong track record in both independent and collaborative settings. Actively seeking an entry-level role as a Software Engineer or Backend Developer.
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A handy web scraper to compare product costs from various e-commerce website like Tokopedia, Shopee, Lazada, and Bukalapak. This project uses tools such as Pupeteer for its web-scraping technology.
APIs developed using Golang, Gin, and Gorm for a Postman Clone desktop application. It is part of a final project on the TEFA Telkom Indonesia Program.
Real-time web-based chat application, hacker themed!. This project uses the power of web sockets using Socket.IO to build real-time communications.
Contains 50+ API Endpoints for SahabatCGI multi-dashboard community web application. Built with Express.js, and with 100+ positive test results using Jest. Integrated OpenAI API for one of the key features.
A full-stack web application for expense tracking. It is responsive for mobile devices and was built with React.js and Express.js
Developed a machine learning model for classification prediction to predict churn rate of Telco customers using comparison of various models (XGBoost, Voting Classifier, KNN, RandomForest, etc.) and techniques, including sampling, feature selection, and evaluation methods.
Conducted a deep data analysis on Airbnb Bangkok listings data using Python along with Tableau for story- based visualization to extract profitable insights and enhance business understanding.
A classification machine learning model developed using PySpark to predict the likelihood of a late delivery for a Brazillian E-Commerce called Olist. This project includes the use of RandomForest model, with techniques such as sampling and model-based feature selection.
Developed a machine learning model for forecasting future gold prices, comparing algorithms like ARIMA, SARIMA, and Prophet to determine the best-predicting model.
Identify supermarket customers based on their behaviour using RFM (Recency, Frequency, Monetary) scores. Each customers are grouped into distinct, controllable categories. This project applies RFM to effectively target each segment with tailored strategies.
Developed a machine learning model for multiple linear regression to predict student performance score using various models (Linear Regression, Ridge, Lasso, SVR, etc.) and common relevant metrics such as Adjusted R-Squared, RMSE, and Residual Analysis.
Conducted a thorough data analysis on retention of an e-commerce retail store customers using cohort retention methods and visualized using Google Looker. Customers are segmented into different cohorts to allow detailed retention analysis.
A simple yet effective terminal-based Python program that lets you manage an Apple Electronics warehouse with the core functions of CRUD (Create - Read - Update - Delete).
Simple and straightforward blog discussing about the importance of error handling in Python. It covers the basic use of try-catch block, complete with best practices and code examples.
This study aims to optimize marketing strategies through RFM (Recency, Frequency, Monetary) analysis on retail transaction datasets obtained from Kaggle. The dataset includes 64,682 transactions from 5,242 SKUs involving 22,625 customers over a period of one year.
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