Analisis Kesesuaian Enterprise Architecture terhadap Standar TOGAF Menggunakan Sentence-BERT dan Semantic Similarity

Abstract

Digital transformation encourages organizations to implement Enterprise Architecture (EA) to align business strategies with information technology. However, evaluating EA documents against TOGAF standards is still largely performed manually, making the process time-consuming and potentially subjective. This study aims to analyze the compliance of Enterprise Architecture with TOGAF standards using Sentence-BERT and Semantic Similarity. The dataset consists of TOGAF standard documents and publicly available Enterprise Architecture documents. The proposed method includes text preprocessing, sentence embedding generation using Sentence-BERT, document similarity measurement through Cosine Similarity, and calculation of the Enterprise Architecture Compliance Score (EACS). The results show that Business Architecture achieved the highest similarity score of 0.86, followed by Application Architecture (0.82), Technology Architecture (0.78), and Data Architecture (0.74). The calculated EACS value of 0.80 indicates a high level of compliance with TOGAF standards. These findings demonstrate that the Sentence-BERT-based approach effectively captures semantic similarities between documents and provides a more objective evaluation compared to conventional methods. The main contribution of this study is the development of EACS as a quantitative indicator to support Enterprise Architecture evaluation and governance in a more efficient and measurable manner.