Unjuk Kerja Term Frequency – Inverse Document Frequency dan K-Means dalam Identifikasi Layanan Pemerintah

Abstract

Term Frequency-Inverse Document Frequency (TF-IDF) is used to assess the importance of words in a document relative to the rest of the document set, while K-Means clusters documents based on content similarity. Utilizing a text dataset covering various government services, this study measures the effectiveness of these methods in identifying and clustering these services. Text pre-processing reduced the number of words from 30,753 to 15,783, indicating the elimination of irrelevant words. Visualization of the TF-IDF scatter plot shows a negative relationship between the frequency of occurrence of a word (TF) and its uniqueness (IDF). Clustering performance evaluation was performed using Silhouette Index (SI) and Davies Bouldin Index (DBI), which showed the consistency and good quality of the generated clusters. A stable SI value of about 0.620 and a consistent DBI value of about 0.551 indicate that the K-Means algorithm, both with the Euclidean and Manhattan approaches, is effective in grouping comments into clusters representing negative, neutral, and positive sentiments. The results of this research make a significant contribution to the development of information systems that are more efficient and responsive to public needs, as well as strengthening text data management in the context of government.