<![CDATA[In the digital era, the ability to perform efficient and targeted information retrieval has become a pivotal necessity, particularly in the fields of research, cybersecurity, and Open-Source Intelligence (OSINT). Google Dorking is a powerful advanced search technique that leverages specific operators to uncover information that is not readily accessible through conventional search methods. However, the manual composition of Google Dork queries often requires a level of technical expertise, posing a significant barrier for non-technical users. To address this challenge, this study presents the design and implementation of SmartDork, a web-based application built on the Flask framework. The application's core purpose is to assist users in automatically generating tailored Google Dork queries based on their specific needs. Its modular architecture is composed of five distinct search categories: general, academic journals, news articles, datasets, and people-related information. The application was developed using a software engineering approach with Python programming and HTML template rendering. The system's effectiveness was validated through rigorous testing to ensure the accuracy and relevance of the generated queries based on user input for each module. The results demonstrate that SmartDork is capable of generating relevant and structured Google Dork queries and presenting them through an intuitive interface that simplifies complex search tasks. In conclusion, SmartDork proves to be an effective solution for users seeking to perform specific and efficient open data exploration. Future work may involve adding features for direct search execution or integration with external APIs.]]>
