The Stemming Application on Affixed Javanese Words by using Nazief and Adriani Algorithm

Soyusiawaty, Dewi; Jones, Anna Hendri Soleliza; Lestariw, Nora Lestari

doi:10.1088/1757-899x/771/1/012026

Cited by 11 publications

(4 citation statements)

References 1 publication

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“…In contrast to [22] using 219 Javanese words, the accuracy is also greater, namely 93.6%. there are more data is used than that study, namely, previously 400 words were also the same as a research [7] while for this study 1000 words.…”

Section: Discussionmentioning

confidence: 75%

ANoM STEMMER: Nazief & Andriani Modification for Madurese Stemming

Enni Lindrawati,

Ema Utami,

Yaqin

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Madurese is one of the regional languages in Indonesia. This is a cultural property that needs to be preserved. With various uniqueness and word formation rules, the Madurese language can be used in information retrieval, namely stemming. The Madurese language has a close relationship with the Javanese language; in several studies, the stemming method is often used, such as the modification of the Nazief and Adriani method, which has good performance for the Javanese language, but there has never been any research on the Madurese language and it has not been proven successful. Previous studies also have not used morphophonemic rules that influence word formation in Madurese. Therefore, this research was developed by modifying Nazief and Adriani's algorithm for Madurese based on Madurese language morphology by removing affixes, namely ter-ater (prefix), panoteng (suffix), and morphophonemic rules. Corpus uses 1000 words from the Madurese language dictionary that have received affixes. The accuracy of the algorithm is 89% with 890 words that match; the prefix has an accuracy of 93.81%; the suffix has an accuracy of 83.78%; and the confix has an accuracy of 80.07%. As for the overall performance, it produces an accuracy of 89.0% with an error rate of 11%. Understemming is found in 104 words, and overstemming in 6 words. The time it takes to compile is 31.31 seconds.

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Section: Discussionmentioning

confidence: 75%

ANoM STEMMER: Nazief & Andriani Modification for Madurese Stemming

Enni Lindrawati,

Ema Utami,

Yaqin

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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“…Due to the complexity of the structure of the Indonesian language, text mining with Indonesian data sources still has room for improvement. The Nazief and Adriani Algorithm is one of the text-mining techniques utilized for the analysis of Indonesian data [7]. The modern era has seen a continuous development of text mining (TM) software.…”

Section: Introductionmentioning

confidence: 99%

Twitter sentiment analysis about economic recession in indonesia

Eka Putra,

Maulana,

Akbar

et al. 2023

Bull.Socinf.The.App

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As one of the most popular social media platforms, Twitter enables users to express their opinions on diverse concepts, products, and services. Large quantities of data shared as tweets can be mined for user feedback and used to improve the quality of products and services. Using Twitter data and social media sentiment analysis, tracking how people feel about the recession in real time is possible. As a consequence, relevant organizations or governments can take preventative measures against the disinformation and unlawful conduct caused by the effects of the recession. This study aims to determine if there is a correlation between how people on Twitter feel about the recession. This study's data acquisition utilized "Recession"-tagged Twitter remarks from 2023. This study analyses filtered tweets for sentiment, emotion, word usage, and trends. According to the findings, 94% of tweets had benign sentiments, 4% had positive sentiments, and 2% had negative sentiments. Tweets with moderate subjective valence cluster in the middle of the polarity scale (between 1 and +1), while tweets with strong subjective valence are dispersed throughout the scale

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“…The stemming process performs the process of reducing the inflection or the resulting term into the main word, root, or original form [35]. The stemming process adds reduplication rules, prefixes, and suffixes to increase the accuracy of each word [36]. The stemming process can perform analysis in information retrieval, speech tagging, syntactic parsing, and machine translation [37].…”

Section: Introductionmentioning

confidence: 99%

Development of natural language processing on morphologybased Minangkabau language stemming algorithm

Sovia

Defit

Yuhandri

et al. 2023

IJEECS

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Minangkabau language (ML) is one of the daily communication tools used by the people of West Sumatra, Indonesia. ML is a challenge in communicating. The ML language translation process is necessary to facilitate communication. This study aims to build a translation system for ML into Indonesian by developing the concept of natural language processing (NLP). NLP development adopts the performance of morphology-based Minangkabau language stemming algorithm (MLSA) which can separate basic words with affixes and endings. The research dataset adopts 600 basic ML words sourced from the big Minangkabau dictionary. The results of this study provide analytic output that can translate ML into Indonesian well. These results are presented based on the testing process on basic word input with an accuracy rate of 97.16% and based on text documents of 91.65%. Thus, the MLSA performance process presents the accuracy of the translation process. Based on these results, this research contributes to developing a stemming algorithm model in carrying out the process of removing prefixes, inserts, and suffixes in the Minangkabau language. Overall, this research can be useful as a tool for translating the ML into Indonesian.

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The Stemming Application on Affixed Javanese Words by using Nazief and Adriani Algorithm

Cited by 11 publications

References 1 publication

ANoM STEMMER: Nazief & Andriani Modification for Madurese Stemming

ANoM STEMMER: Nazief & Andriani Modification for Madurese Stemming

Twitter sentiment analysis about economic recession in indonesia

Development of natural language processing on morphologybased Minangkabau language stemming algorithm

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