Improving Stemming Algorithm Using Morphological Rules

Abstract

Stemming words to remove suffixes has applications in text search, translation machine, summarization document, and text classification. For example, Indonesian stemming reduces the words "kebaikan", "perbaikan", "memperbaiki" and "sebaikbaiknya" to their common morphological root "baik". In text search, this permits a search for a player to find documents containing all words with the stem play. In the Indonesian language, stemming is of crucial importance: words have prefixes, suffixes, infixes, and confixes that make them match to relate difficult words. This research proposed a stemmer with more accurate word results by employing an algorithm which gave more than one word candidate results and more than one affix combinations. New stemming algorithm is called CAT stemming algorithm. Here, the word results did not depend on the order of the morphological rule. All rules were checked, and the word results were kept in a candidate list. To make an efficient stemmer, two kinds of word lists (vocabularies) were used: words that had more than one candidate words and list of root word as a candidate reference. The final word results were selected with several rules. This strategy was proved to have a better result than the two most known about Indonesian stemmers. The experiments showed that the proposed approach gave higher accuracy than the compared systems known.

Performance comparison between naive bayes and k- nearest neighbor algorithm for the classification of Indonesian language articles

“…This calculation as were requires a expansive sum of preparing information to decide the assessed parameters required within the classification prepare [9]. Subsequent research was conducted by Winarti et al for analysis on Indonesian texts with large data [10]. The results of this study indicate that the k-nearest neighbor algorithm can provide good performance because the algorithm is resilient to data noise [11].…”

Pendeteksi Lokasi Kejadian Covid-19 Menggunakan Social Media dengan Kombinasi Algoritma Stemming Bahasa Indonesia

“…Dari beberapa referensi berkaitan dengan algoritma stemming itu adalah inti pemrosesan bahasa alami teknik Pengambilan Informasi yang efisien dan efektif, dan salah satu yang diterima secara luas oleh pengguna. Itu sudah biasa mengubah varian kata menjadi akar kata yang sama dengan menerapkannya dalam banyak kasus aturan morfologi (Winarti et al, 2017).…”

“…Selanjutnya adalah perbandingan yang merupakan kejadian atau tidak dengan ujicoba tanpa algoritma dan yang dikombinasikan dari ketiga Algoritma Stemming khususnya Bahasa Indonesia yaitu, Nazief-Adriani, Idris, dan Arifin-Setiono Di sini membedakan dari dua kata dengan "covid" dan "covid 19". Dari beberapa referensi untuk mengetahui akurasinya (Bhadoria & Kumar Patel, 2014;Winarti et al, 2017). Dari hasil tabel 3, persentase paling besar untuk mendapatkan kata "covid" dan "covid 19" adalah dikombinasikan dari ketiga Algoritma Stemming khususnya Bahasa Indonesia yaitu, Nazief-Adriani, Idris, dan Arifin-Setiono sebesar rata-rata 96,56%.…”

Indonesian Words Error Detection System using Nazief Adriani Stemmer Algorithm

“…10, No. 12, 2019 220 | P a g e www.ijacsa.thesai.org Nazief Adriani's algorithm has been used in research [6], [7], [8], dan [9]. The algorithm created by Bobby Nazief and Mirna Adriani has the following stages:…”