Penerapan Natural Language Processing (NLP) dengan Metode Cosine Similarity pada Sistem E-Monev untuk Pencarian Program Pembangunan Daerah
DOI:
https://doi.org/10.24252/shift.v5i2.183Keywords:
E-Monev, Cosine Similarity, BERT Embedding, Local Government, Information RetrievalAbstract
The Evaluation and Monitoring System (E-Monev) is a digital tool for periodically monitoring performance achievement and budget absorption in Regional Government Organizations (OPD). A key challenge in its implementation is accurately identifying relevant regional development programs from user text input. Conventional keyword-based search cannot fully capture the semantic meaning of text and often returns inaccurate or contextually irrelevant results. This study designs and develops a semantic search feature for E-Monev using a Natural Language Processing (NLP) approach that combines BERT (Bidirectional Encoder Representations from Transformers) text representations with the Cosine Similarity method. The data were drawn from the Work Plan (Renja) documents of all OPDs in Bojonegoro Regency, prepared in accordance with Minister of Home Affairs Decree Number 900.1.15.5-3406 of 2024, the second amendment to Decree Number 050-5889 of 2021 on the verification, validation, and inventory of updates to the classification, codification, and nomenclature of regional development and financial planning. Both user queries and Renja data were converted into embedding vectors, and their semantic similarity was then calculated. Test results show that the system significantly improves search accuracy, achieving a precision of 0.884. This approach is expected to make the retrieval of regional development program information more efficient and relevant, thereby supporting more precise and contextual performance evaluation by the local government.
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