<![CDATA[Industrial material master data in Enterprise Resource Planning (ERP) and Enterprise Asset Management (EAM) systems accumulates duplicate records that distort inventory, procurement, and analytics. Traditional deduplication relies on string-similarity measures such as Levenshtein, Jaro–Winkler, and TF-IDF cosine, which can struggle on catalogs mixing Indonesian and English terminology—e.g. Valve versus Keran—and on paraphrastic variants with different word order or abbreviation style. This study formally specifies a semantic deduplication pipeline that encodes material descriptions as sentence embeddings using Sentence-BERT (SBERT) and compares them via cosine similarity, then diagnostically evaluates the extent to which SBERT improves over those baselines. Following Design Science Research, the pipeline specifies normalisation, encoding with a multilingual paraphrase-tuned SBERT variant, and pairwise comparison within candidate sets produced by hybrid blocking; the diagnostic evaluation reports scores on the raw descriptions to expose baseline behaviour before domain-specific harmonisation. A sample of 291,000 records from two Indonesian industrial power plants motivates the design. On a diagnostic set of 100 record pairs derived from existing engineer-annotated duplicate markers, Jaro–Winkler achieves F1 = 0.925 (precision 1.000, recall 0.860) and SBERT achieves F1 = 0.875 (precision 0.913, recall 0.840) at threshold τ = 0.65; qualitative analysis of twelve representative pairs further reveals that SBERT excels on structural paraphrase (cosine 0.73–0.88 where character-level methods score below 0.50), while Jaro–Winkler remains competitive on abbreviation, unit-standard, and cross-language pairs—particularly those involving Indonesian technical vocabulary under-represented in the model’s training distribution. The central finding is that Sentence-BERT complements rather than replaces string baselines, which motivates future work on multi-channel architectures combining textual semantics with structural context. ]]>
