Imaddudin, Zakki (2025) RANCANG BANGUN APLIKASI HTTP OBSERVATORY DAN WEB VULNERABILITY SCANNER UNTUK ANALISIS KEAMANAN WEBSITE. S1 thesis, Universitas Mercu Buana-Menteng.
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Abstract
Keamanan website merupakan isu kritis dalam era digital saat ini, terutama di Indonesia yang mengalami 189 kasus web defacement pada tahun 2023 berdasarkan data Badan Siber dan Sandi Negara (BSSN). Sebanyak 93,1% serangan tersebut menyasar halaman tersembunyi (hidden pages), dengan sektor pemerintahan menjadi target utama yang mencatat 167 kasus, diikuti sektor kesehatan dan pertahanan. Untuk mengatasi permasalahan ini, penelitian ini bertujuan merancang dan membangun aplikasi berbasis web yang mengintegrasikan dua modul utama untuk melakukan analisis keamanan website secara komprehensif. Aplikasi dikembangkan menggunakan arsitektur modern dengan Python Flask sebagai backend, MongoDB sebagai basis data, dan React.js untuk antarmuka pengguna. Sistem terdiri dari dua modul utama: HTTP Observatory yang mengevaluasi konfigurasi server, header keamanan, dan implementasi SSL/TLS; serta Web Vulnerability Scanner yang mendeteksi kerentanan seperti SQL Injection, XSS, CSRF, dan berbagai jenis Code Injection. Keunggulan sistem ini terletak pada penerapan klasifikasi berbasis aturan (rule based classification) untuk mengklasifikasikan tingkat risiko kerentanan menjadi tiga kategori: tinggi, sedang, dan rendah. Hasil analisis disajikan melalui dashboard interaktif yang menampilkan visualisasi data, tren kerentanan, dan rekomendasi perbaikan yang dapat ditindaklanjuti langsung oleh pengelola website. Sistem ini menyediakan otomatisasi analisis keamanan yang sistematis, terukur, dan mudah digunakan, sehingga diharapkan dapat memberikan kontribusi nyata terhadap peningkatan keamanan web nasional dengan membantu organisasi mengidentifikasi dan mengatasi kerentanan keamanan secara proaktif. Website security is a critical issue in today's digital era, particularly in Indonesia which experienced 189 cases of web defacement in 2023 according to data from the National Cyber and Encryption Agency (BSSN). As many as 93.1% of these attacks targeted hidden pages, with the government sector being the primary target recording 167 cases, followed by health and defense sectors. To address this problem, this research aims to design and develop a web-based application that integrates two main modules to conduct comprehensive website security analysis. The application is developed using modern architecture with Python Flask as the backend, MongoDB as the database, and React.js for the user interface. The system consists of two main modules: HTTP Observatory that evaluates server configuration, security headers, and SSL/TLS implementation; and Web Vulnerability Scanner that detects vulnerabilities such as SQL Injection, XSS, CSRF, and various types of Code Injection. The advantage of this system lies in the implementation of rules based classification methods to classify vulnerability risk levels into three categories: high, medium, and low. The analysis results are presented through an interactive dashboard that displays data visualization, vulnerability trends, and remediation recommendations that can be directly acted upon by website administrators. This system provides systematic, measurable, and user-friendly security analysis automation, which is expected to make a real contribution to improving national web security by helping organizations identify and address security vulnerabilities proactively.
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