Stemming Indonesian

Adriani, Mirna; Asian, Jelita; Nazief, Bobby Achirul Awal; Tahaghoghi, S. M. M.; Williams, Hugh E.

doi:10.1145/1316457.1316459

Cited by 141 publications

(40 citation statements)

References 25 publications

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“…Next, we do stemming to lemmatize words in every tweet. In this paper, stemming was done using Nazief-Adriani Algorithm (Adriani et al, 2007) that implemented using Sastrawi Library 9 . For stop words removal, we used stop word list given by (Tala, 2003).…”

Section: Experiments and Discussionmentioning

confidence: 99%

Multi-label Hate Speech and Abusive Language Detection in Indonesian Twitter

Ibrohim¹,

Budi²

2019

Proceedings of the Third Workshop on Abusive Language Online

155

122

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Hate speech and abusive language spreading on social media need to be detected automatically to avoid conflicts between citizens. Moreover, hate speech has a target, category, and level that also need to be detected to help the authority in prioritizing which hate speech must be addressed immediately. This research discusses multi-label text classification for abusive language and hate speech detection including detecting the target, category, and level of hate speech in Indonesian Twitter using machine learning approaches with Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest Decision Tree (RFDT) classifier and Binary Relevance (BR), Label Power-set (LP), and Classifier Chains (CC) as the data transformation method. We used several kinds of feature extractions which are term frequency, orthography, and lexicon features. Our experiment results show that in general the RFDT classifier using LP as the transformation method gives the best accuracy with fast computational time.

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Section: Experiments and Discussionmentioning

confidence: 99%

Multi-label Hate Speech and Abusive Language Detection in Indonesian Twitter

Ibrohim¹,

Budi²

2019

Proceedings of the Third Workshop on Abusive Language Online

155

122

View full text Add to dashboard Cite

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“…Doing the process of cutting the words that compose the sentence in the document into tokens (tokenizing stage), the process of removing common and unimportant words (stoplist) and returning the words to their basic form (stemming stage). The stemming algorithm uses the Porter algorithm which is adapted to Indonesian [5]. 3.…”

Section: System Designmentioning

confidence: 99%

Mixed Heuristic Algorithm As String Matching For Search Document

Sari¹,

Rahmadani²,

Putri³

2020

Proceedings of the Proceedings of the the 3rd Annual Conference of Engineering and Implementation on Vocational Education, ACE

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The computer provides document search based on the document file title. So this makes it difficult for users to find documents, if the user forgets the file title of the document. String matching algorithm is the basic component for data searching. Search engine requires an algorithm that can work quickly and can sort the documents according to the level of compatibility. One of the algorithms that match is the Mixed Heuristic. These algorithms perform a search pattern or query not just against a word, but can be a sentence of more than one word. In addition, these algorithms also perform ranking of relevant documents. This paper shows the analysis of the level of accuracy using precision and recall of the results given by search engines by using the Mixed Heuristic algorithms for string matching, and the analysis of documents from the results given by search engines.

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“…h. Stemming aims to turn a word into a root word by removing the phrase prefix and prefix [8]. This study uses Sastrawi Stemmer adapted from the Nazief-Andriani [18] algorithm with a modified confix-stripping [19]. i. Stopwords Removal is a word that often appears and does not have any meaning [9].…”

Section: Preprocessingmentioning

confidence: 99%

The Effect of Stemming and Removal of Stopwords on the Accuracy of Sentiment Analysis on Indonesian-language Texts

Pradana¹,

Hayaty²

2019

KINETIK

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Preprocessing is an essential task for sentiment analysis since textual information carries a lot of noisy and unstructured data. Both stemming and stopword removal are pretty popular preprocessing techniques for text classification. However, the prior research gives different results concerning the influence of both methods toward accuracy on sentiment classification. Therefore, this paper conducts further investigations about the effect of stemming and stopword removal on Indonesian language sentiment analysis. Furthermore, we propose four preprocessing conditions which are with using both stemming and stopword removal, without using stemming, without using stopword removal, and without using both. Support Vector Machine was used for the classification algorithm and TF-IDF as a weighting scheme. The result was evaluated using confusion matrix and k-fold cross-validation methods. The experiments result show that all accuracy did not improve and tends to decrease when performing stemming or stopword removal scenarios. This work concludes that the application of stemming and stopword removal technique does not significantly affect the accuracy of sentiment analysis in Indonesian text documents.

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Stemming Indonesian

Cited by 141 publications

References 25 publications

Multi-label Hate Speech and Abusive Language Detection in Indonesian Twitter

Multi-label Hate Speech and Abusive Language Detection in Indonesian Twitter

Mixed Heuristic Algorithm As String Matching For Search Document

The Effect of Stemming and Removal of Stopwords on the Accuracy of Sentiment Analysis on Indonesian-language Texts

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