<![CDATA[Abstract: The development of transformer architecture has brought significant changes in Natural Language Processing (NLP) research, particularly in semantic parsing tasks. This study aims to analyze the development of transformer-based semantic parsing through a Systematic Literature Review (SLR) approach. The research method uses the PRISMA guidelines with a process of identification, selection, and analysis of articles from the Scopus, ScienceDirect, IEEE Xplore, Springer, and Google Scholar databases for the period 2019–2026. The results of the study show that transformer models such as BERT, T5, GPT, and transformer encoder–decoder have become the dominant approaches in modern semantic parsing because they are able to improve contextual understanding, semantic representation, and reasoning more effectively than previous methods. The most widely used datasets include Spider, WikiSQL, GeoQuery, ATIS, and Overnight, with the main evaluation being Execution Accuracy and Exact Match Accuracy. This study also identifies various challenges, such as limited labeled data, high computational costs, low model interpretability, the risk of hallucinations, and limitations in low-resource languages. This research contributes to a literature review, identifying research gaps, and recommending future research directions for transformer-based semantic parsing. Keywords: BERT, Natural Language Processing, Semantic Parsing, Systematic Literature Review, Transformer, T5, Text-to-SQL. Abstrak: Perkembangan arsitektur transformer telah membawa perubahan signifikan dalam penelitian Natural Language Processing (NLP), khususnya pada tugas semantic parsing. Penelitian ini bertujuan untuk menganalisis perkembangan semantic parsing berbasis transformer melalui pendekatan Systematic Literature Review (SLR). Metode penelitian menggunakan pedoman PRISMA dengan proses identifikasi, seleksi, dan analisis artikel dari database Scopus, ScienceDirect, IEEE Xplore, Springer, dan Google Scholar pada periode 2019–2026. Hasil kajian menunjukkan bahwa model transformer seperti BERT, T5, GPT, dan encoder–decoder transformer menjadi pendekatan dominan dalam semantic parsing modern karena mampu meningkatkan contextual understanding, semantic representation, dan reasoning secara lebih efektif dibandingkan metode sebelumnya. Dataset yang paling banyak digunakan meliputi Spider, WikiSQL, GeoQuery, ATIS, dan Overnight, dengan metrik evaluasi utama berupa Execution Accuracy dan Exact Match Accuracy. Penelitian ini juga mengidentifikasi berbagai tantangan, seperti keterbatasan data berlabel, tingginya biaya komputasi, rendahnya interpretabilitas model, risiko hallucination, serta keterbatasan pada low-resource language. Penelitian ini berkontribusi dalam memberikan pemetaan literatur, identifikasi research gap, dan rekomendasi arah penelitian semantic parsing berbasis transformer pada masa mendatang. Kata Kunci: BERT, Natural Language Processing, Semantic Parsing, Systematic Literature Review, Transformer, T5, Text-to-SQL.]]>
