Pengembangan Website Pembelajaran Keamanan Web Berbasis Kecerdasan Buatan Dengan Implementasi Large Language Model DeepSeek

Fathoni, Muhammad Azril (2025) Pengembangan Website Pembelajaran Keamanan Web Berbasis Kecerdasan Buatan Dengan Implementasi Large Language Model DeepSeek. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Perkembangan teknologi digital yang pesat menjadikan keamanan web sebagai aspek krusial dalam sistem berbasis internet. Namun, pembelajaran keamanan web sering menghadapi tantangan seperti kurangnya materi yang interaktif dan mudah diakses, dominasi teori dibandingkan praktik, serta minimnya pengalaman belajar yang dipersonalisasi. Banyak peserta kesulitan menyelesaikan tantangan keamanan, sehingga cenderung bergantung pada write-up daripada mengeksplorasi solusi secara mandiri. Penelitian ini bertujuan mengembangkan platform pembelajaran keamanan web berbasis kecerdasan buatan dengan implementasi Large Language Model (LLM) DeepSeek. Platform ini memberikan panduan interaktif secara bertahap, membantu pengguna mengatasi kendala tanpa langsung memberikan jawaban akhir. Dengan integrasi elemen gamifikasi, platform ini meningkatkan keterlibatan pengguna dan menyediakan simulasi skenario serangan siber dalam lingkungan yang aman. Pendekatan ini diharapkan dapat meningkatkan pendidikan keamanan web dengan mendorong pemikiran kritis serta keterampilan pemecahan masalah.
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The rapid development of digital technology has made web security a crucial aspect of internet-based systems. However, learning web security often faces challenges such as a lack of interactive and accessible materials, a theoretical focus rather than practical application, and a lack of personalized learning experiences. Many learners struggle with security challenges, leading them to rely on write-ups instead of actively exploring solutions. This research aims to develop an AI-based web security learning platform using the Large Language Model (LLM) DeepSeek. The platform provides interactive step-by-step guidance, helping users overcome obstacles without revealing the final solution. By integrating gamification elements, the platform enhances user engagement and provides simulated cybersecurity attack scenarios in a safe environment. This approach is expected to improve web security education by fostering critical thinking and problem-solving skills.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Keamanan Web, Kecerdasan Buatan, Large Language Model, DeepSeek, Pembelajaran Gamifikasi, Web Security, Artificial Intelligence, Large Language Model, DeepSeek, Gamified Learning
Subjects:	T Technology > T Technology (General) > T58.5 Information technology. IT--Auditing
Divisions:	Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Information Technology > 59201-(S1) Undergraduate Thesis
Depositing User:	Muhammad Azril Fathoni
Date Deposited:	01 Aug 2025 02:44
Last Modified:	01 Aug 2025 02:44
URI:	http://repository.its.ac.id/id/eprint/124997

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