Abstract
The official website of the Ministry of Foreign Affairs of the Republic of Indonesia (MoFA RI) is an important platform for disseminating information to a diverse audience. Efficiently categorizing the vast amount of content available on the website is essential for enhancing user experience and optimizing information retrieval. These categories will also become an identifier and topic classification based on the content inside the article. This study presents a systematic approach to content classification of the Official Website of the Ministry of Foreign Affairs of the Republic of Indonesia (MoFA RI) using the Vector Space Model (VSM). The methodology involves preprocessing the text data, constructing a term-document matrix, and implementing cosine similarity to measure the relevance of documents to predefined categories. The study demonstrates the effectiveness of VSM in accurately classifying content, thus facilitating streamlined access to information for users navigating the website. Furthermore, the findings offer insights into enhancing the organization and accessibility of governmental online platforms, contributing to improved user experience and information dissemination.
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