The internet, like the workplace, is at the heart of every aspect of communal life in the modern digital era. Many platforms already provide job vacancies, especially for independent contractors. To identify relevant job openings, consumers typically need to access multiple websites to gather this information. One way to overcome this problem is to use web scraping. BeautifulSoup and Selenium libraries will be used to collect data in accordance with previous research findings. The vector space model approach is used to determine the degree of data similarity between queries and documents to perform data searches. After examining the data, a mean accuracy value of 56% was obtained and a mean perfect recall value of 100%. This is because, even if the context does not match, data searches use three parameters, increasing the likelihood of returning irrelevant material if the document contains words from the user's query. Users can manage the processes of web scraping, data processing, and data searching with the help of the Streamlit framework in Python, which displays the results of data processing. To obtain data from the Sribulancer, Project and freelancing freelancer websites, this research will use web scraping techniques. Users can search for data from multiple websites using a vector space model approach rather than accessing each loose website one by one. Web scraping results can also be processed to be displayed in a more user-friendly format and save time by using data visualization in the form of a web application built using the Streamlit framework.