Deteksi Sistem Cerdas Kerentanan Kode PHP Menggunakan Hybrid ML dan LLM - Moh Erdda Habiby Habiby; Miki Wijana (INTERNAL (Information System Journal), 2026)
Security vulnerabilities in PHP programming code remain one of the major threats to the integrity of web applications, especially when software development processes are not supported by automated and adaptive security analysis mechanisms. This study aims to develop an intelligent system for vulnerability detection and automatic code remediation using a Hybrid Machine Learning (ML) and Large Language Model (LLM) approach. The system combines Term Frequency-Inverse Document Frequency (TF-IDF), Abstract Syntax Tree (AST) Parsing, and the Random Forest algorithm for vulnerability classification, while integrating the Google Gemini API as a dynamic recommendation layer to generate contextual and adaptive secure coding suggestions. The dataset consists of 320 PHP code snippets covering SQL Injection, XSS, File Inclusion, Command Injection, Unsafe File Upload, and safe code samples. Experimental evaluation using accuracy, precision, recall, F1-score, and confusion matrix shows that the model achieved an overall accuracy of 86.25% with a macro average F1-score of 0.86. This study demonstrates that integrating Hybrid ML and LLM-based remediation using the Gemini API has strong potential for developing intelligent static code analysis systems for PHP web applications.