Indonesian Words Error Detection System using Nazief Adriani Stemmer Algorithm

Abstract

Stemming in each language has a different process and is determined according to the structure of the language. Stemming is mostly used as a complete step in the processing of words and phrases. There are many stemming algorithms available, and some used as a process for word processing. One function of stemming is to detect word errors in Indonesian. In this study, researchers created the Indonesian words error detection system using Nazief and Adriani algorithm. In the trials conducted, the system will accept text input obtained from the user. Then the system will preprocess the text. In this study, there are three stages of preprocessing, namely tokenization, case folding, and filtering. After the stages in preprocessing are finished, the system will call each word for the process of stemming. The results of the stemming will be compared with the base words available in the database. If it does not match, then the word is highlighted and is considered an error word. The first finding is the Nazief Adriani's algorithm can be able to detect words error until 100%. The second finding is the Nazief Adriani's algorithm also detect non-words error, the accuracy of detecting is 97.464%.

Application of the Finite State Automata (FSA) Method in Indonesian Stemming using the Nazief & Adriani Algorithm

“…The first finding is that Nazief Adriani algorithm can detect word errors up to 100%. The second finding is that Nazief Adriani algorithm also detects non-word errors, with a detection accuracy of 97.464% [7]. Furthermore, previous research conducted by Ardiles Sinaga et al aims to analyze the comparison between two stemming algorithms, namely the Nazief Adriani algorithm and the Arifin Setiono algorithm, to measure the performance of each algorithm through testing on 30 Indonesian text documents.…”

Analisis Sentimen Pada Proyeksi Pemilihan Presiden 2024 Menggunakan Metode Support Vector Machine

“…Preprocessing digunakan untuk menghapus karakter yang tidak relevan dalam dokumen (Yudhana et al, 2018). Langkah pengolahan teks diantaranya case folding, tokenizing, stopword removal, dan stemming (Yudhana, Fadlil, et al, 2019).…”

Monitoring the Performance of Lecturers Using Behaviorally Anchor Rating Scale and Management by Objectives Method

“…Human Resources (HR) of universities continue to develop technology that previously used paper in the form of questionnaire that lacked of real-time data, especially from manual systems and then, is gradually changed with technology by creating an integrated information system [5]- [6]. The information system at STIKES-MM has been implemented well, showing campus profiles and attractive website designs as the main attraction for users or prospective students [7]- [9]. Several systems that have been running until now still use the manual method using Excel, one of which is the monitoring system for data on the performance of lecturers or lecturers [10].…”

“…STIKES-MM has SPMI (Internal Quality Assurance System), a systemic activity of development of quality assurance taken out independently by each institution to manage and enhance higher education implementation in a planned and reliable manner as a part of quality assurance in managing HR performance in terms of processing data on the performance of lecturers. HR data can be used as an indicator of overall work results that become achievements in work and workplace [7], [13]- [14].…”