Abstract
Spelling mistakes in the Indonesian language often occur due to lack of knowledge of spelling rules, the influence of foreign languages, and the use of informal language. This research aims to develop a Natural Language Processing (NLP) based spelling checking system that refers to the Dictionary of Indonesian Language (KBBI). The system is expected to be able to perform automatic spelling verification and correction with high accuracy. The method used is qualitative with an analytic descriptive approach, involving data collection from the KBBI and processing using Python. The research stages include collection, data cleaning, tokenization, and data analysis. The system was trained using a large Indonesian text dataset. The results showed the system achieved a spelling check accuracy of 81.64%, more accurate, easy to use, flexible, and adaptive than conventional systems. The number of words in the text affects the checking time, with longer text taking longer.
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