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This study conducts a comprehensive comparison of spell-checking methods in Bahasa Indonesia, specifically focusing on three approaches: Peter Norvig's method, Long Short-Term Memory (LSTM), and N-gram. The primary metric for evaluation is the accuracy in correcting spelling errors. Notably, Peter Norvig's method outperforms the others, with N-gram following closely, and LSTM trailing behind. The study draws valuable insights that contribute to the enhancement of spelling correction accuracy in the Bahasa Indonesia language. To carry out the evaluation, the research employs SPECIL data (Spell Error Corpus for Indonesian Language), which includes documents with various error types such as insertion, deletion, transposition, and substitution. The testing dataset consists of 150 words, aligning with the 150-word corpus references from the 'Leipzig Corpora Collection' used for Peter Norvig's and N-gram methods. It's noteworthy that the LSTM method utilizes a reference dataset from SPECIL, comprising 150 data points and specifically focusing on insertion errors for the test data. This research provides valuable insights for researchers, developers, and language technology enthusiasts seeking to refine spell-checking techniques for the Bahasa Indonesia language. By leveraging diverse error types and a standardized testing dataset, the study aims to contribute to the continual improvement of spell-checking tools
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