Research & AIstatus:// completedFeatured Case Study
Network Intrusion Detection System (Thesis Research)
role:// titleResearcher & Full-Stack Developer
client:// organizationPoliteknik Negeri Cilacap
domain:// categoryResearch & AI
executive-summary://
“A Random Forest and Gemini AI system that reads network traffic like a story and flags the chapters that don't belong — 98.59% accuracy.”
Technologies & Architecture
stack:// Pythonstack:// Flaskstack:// Random Foreststack:// Gemini AIstack:// Cybersecurity
System Specification & Impact
Engineering thesis project at Politeknik Negeri Cilacap (Cybersecurity Engineering). Implemented a hybrid intrusion detection architecture combining supervised Random Forest feature classification with Large Language Model (Gemini AI) contextual log analysis to achieve 98.59% malicious payload detection accuracy.
System Diagram & Media Screenshots
No external media attachments linked for this build.