Design of an Automatic Indonesian Grammar Error Detection Application Using Machine Learning Algorithms

Abstract

Indonesian, as the country's official language, is crucial in both academic and professional settings. Therefore, writing well and adhering to grammatical standards is crucial. However, many grammatical errors persist in various types of writing. The objective of this research is to design and develop a web-based application that can automatically identify grammatical issues in Indonesian using machine learning techniques, specifically the Support Vector Machine (SVM). The SVM algorithm was chosen for its high accuracy in text classification. An Indonesian dictionary was used as the source dataset. This program can be used as a learning tool in addition to helping users identify and correct grammatical errors in real-time. With 100% accuracy, precision, and recall values, and 0% classification error, the test results demonstrate the application's excellent detection performance. These results demonstrate how well the SVM system is able to detect grammatical issues in Indonesian text.