Ardhi Bagas Rangga Wardhana

I am presently pursuing a Bachelor's degree in Computer Science at Bina Nusantara University. My fervor lies within the complex domain of web development and internet networking. I derive satisfaction from delving into inventive solutions and advancing technological frontiers. I am keenly enthusiastic about learning, expanding my skills, and making meaningful contributions to the dynamic digital realm. I look forward to connecting with like-minded individuals and collectively embracing the opportunities this journey presents. Possesses versatile skills in project management, problem-solving, and collaboration. Brings fresh perspective and strong commitment to quality and success. Recognized for adaptability and proactive approach in delivering effective solutions.

• CERTIFICATE OF APPRECIATION, BINUS University, August 2024 - Present
• ENGLISH FOR MARKET RESEARCH & BUSINESS COMMUNICATION, BINUS University, August 2024 - Present
• Certificate of Appreciation as Committe in the Augmented Reality Competition at DIGIFEST 2024: Metaverse Contest Augmented World, HIMTI BINUS University, June 2024 - Present
• ENGLISH FOR ADVANCED ENGLISH, BINUS University, May 2024 - Present
• Ecommerce Website with HTML & CSS, Great Learning, March 2024 - Present
• English for Professional Office, BINUS University, January 2024 - Present
• Backend Development, MySkill, September 2023 - Present
• English for Creative Writing, BINUS University, September 2023 - Present
• Introduction to UX Research, MySkill, September 2023 - Present
• Basic Statistics and Data Visualisation, MySkill, March 2023 - Present
• Introduction to Frontend, MySkill, March 2023 - Present

Date: September 2024
Project Type: Final Project – Enterprise Network Infrastructure Design

Overview:
Proud to share the results of our comprehensive final project — a full-scale enterprise network architecture design for a fictional e-commerce company, Luisale. We planned and implemented a robust network to support 80+ users across multiple departments, including Development, IT Support, Finance, and Server Operations.

Project Highlights:

Tools & Specifications:

• Devices: Lenovo ThinkPad X1 / X1L5, Custom-built PCs (Intel i7)
• Network Equipment: TP-Link ER605, D-Link DES-1024D, Fiber Dropcore
• Design Tools: Network diagram and layout planning software
• Key Focus: High reliability, modular design, realistic hardware implementation

Date: March 2024
Project Context: 2nd Semester, Computer Science

Overview:
Contributed to the development of Med-X, an Augmented Reality (AR) application designed to enhance health education through interactive 3D disease visualizations. By scanning markers from a medical textbook, users can explore detailed disease information and engage with educational mini-games.

Key Features:

• Login & Registration: Secure account creation and user data management
• AR Feature: Interactive 3D disease models displayed through marker scans
• Mini-Games: Flash Cards and Hangman to test and reinforce medical knowledge
• Library: Save and organize scanned disease entries with searchable tags
• Profile & Scoreboard: Track user progress, preferences, and achievements

Tools Used:

• Unity: AR development
• Figma: UI/UX design
• Microsoft Word: Project documentation

My Role:
I designed the UI/UX in Figma, ensuring an intuitive and engaging user experience. Additionally, I handled project documentation, detailing the development stages, features, and implementation process.

Enhancing Decision Tree Performance through Stacking Ensemble Learning for Sentiment Analysis

This research proposes a novel stacking ensemble model to enhance the accuracy and robustness of decision tree classifiers for sentiment analysis. By strategically combining diverse base learners to leverage their strengths and mitigate weaknesses, the approach delivers superior performance compared to traditional models, advancing scalable solutions for text classification challenges. Here's the accepted paper link: https://tinyurl.com/paperenhancingdtThis research proposes a novel stacking ensemble model to enhance the accuracy and robustness of decision tree classifiers for sentiment analysis. By strategically combining diverse base learners to leverage their strengths and mitigate weaknesses, the approach delivers superior performance compared to traditional models, advancing scalable solutions for text classification challenges. Here's the accepted paper link: https://tinyurl.com/paperenhancingdt

Skills: Research Skills · Critical Thinking · Report Writing · Scientific Writing · Teamwork