Optimizing nazief adriani's stemmer algorithm in detecting indonesian word errors using sastrawi - Muhamad Rosidin; Muhammad Fauzan Gustafi; Septia Ayu Pratiwi (Institute of Computer Science (IOCS), 2023)
The study aimed to enhance the performance of Nazief Adriani's Stemmer Algorithm for detecting word errors in Indonesian texts by utilizing the Sastrawi stemmer. The evaluation utilized a dataset containing Indonesian sentences from a specific source, consisting of 27,704 words. The processing time comparison between the two stemmer algorithms, Nazief Adriani and Sastrawi, is presented in the results. Each algorithm underwent three trials, and it was evident that Sastrawi demonstrated significantly faster processing times, averaging around 2.3254 seconds, while Nazief Adriani averaged approximately 301.7509 seconds. Additionally, the word error count detected by each algorithm in their respective trials is also presented. Nazief Adriani detected 3048 word errors in each trial, whereas Sastrawi detected an average of 1629 word errors. To provide a visual representation of the word error detection comparison, a graphical representation highlighted Sastrawi's superior performance in efficiently identifying word errors in Indonesian texts