Formal and Non-Formal Indonesian Word Usage Frequency in Twitter Profile Using Non-Formal Affix Rule

Utami, Ema; Hartanto, Anggit Dwi; Adi, Sumarni; Putra, Rahardyan Bisma Setya; Raharjo, Suwanto

doi:10.1109/icoris.2019.8874908

Cited by 9 publications

(12 citation statements)

References 10 publications

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“…1-0.67=0.33. In contrast, Figure 5 showed that stemming by using "Incorbis" increased the accuracy by indicating the number of True Positive and True Negative on 8, so the error rate is 1/9 or 0.11 or 1 -0.89 = 0.11 (2). The small error rate showed that the level of accuracy is high.…”

Section: Resultsmentioning

confidence: 91%

“…The accuracy is the ratio of True Positive and True Negative to the total number of accounts, while the error rate is the ratio of False Positive and False Negative to the total number of accounts. The formula of accuracy is defined as: Accuracy=(TP+TN)/(TP+TN+FP+FN) (1) To obtain error rate, the formula that can be used to calculate it was defines as: Error rate=1-Accuracy (2) As mentioned, that the dataset was split into data testing and data training by using a ratio of 70%:30%. The number of rows in the dataset is 30, so the number of data testing is 9.…”

Section: Resultsmentioning

confidence: 99%

“…Indonesian language is classified into two categories namely formal and non-formal in the method of use. Indonesian formal is used in formality situation, while Indonesian non-formal is widely used in casual situation like in social media conversations [2]. In formal language, people use "standardized" language as prescribed by linguistic rules of the language.…”

Section: Introductionmentioning

confidence: 99%

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Improving the Accuracy of Text Classification using Stemming Method, A Case of Informal Indonesian Conversation

Rianto

Mutiara

Wibowo

et al. 2020

Preprint

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As social beings, humans always interact with one another using either verbal or non-verbal language. Language is an arbitrary sound-symbol system, which is used by members of a community to cooperate, interact, and identify themselves. Indonesian language is classified into two categories, namely formal and non-formal. The former meets the grammatical standard as prescribed by linguistic rules of the language, while the latter tends to deviate it. In daily communication, however, non-formal language is more intensively used because they are more practical and easier to understand. With this tendency, non-formal language causes problems in linguistic computation because most linguistic computations use formal standard languages that already have standardized rules. This research aims to develop a dynamic Indonesian closed corpus related to airline ticket reservation, namely "Incorbiz". The "Incorbiz" will be used as stemming tool for formal and non-formal Indonesian. Text processing, text normalization, and auto-update data were proposed in this research. This research also compared two stemming techniques i.e. "Sastrawi" and "Incorbiz" to process the 30-sample dataset. The algorithm used to process the classification is Support Vector Machine (SVM). The data used to develop the "Incorbiz" were taken from conversations between customer service staff and consumers in airline ticket reservations. The result showed that "Incorbiz" had higher accuracy than "Sastrawi" on 0.89 and 0.67, respectively.

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Section: Resultsmentioning

confidence: 91%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improving the Accuracy of Text Classification using Stemming Method, A Case of Informal Indonesian Conversation

Rianto

Mutiara

Wibowo

et al. 2020

Preprint

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“…1-0.73=0.27. In contrast, Figure 5 showed that stemming by using "Incorbis" increased the accuracy by indicating the number of True Positive and True Negative on 140, so the error rate is 25/165 or 0.15 or 1 -0.85 = 0.15 (2). The small error rate showed that the level of accuracy is high to predict a class of sentences.…”

Section: Resultsmentioning

confidence: 91%

Improving the Accuracy of Text Classification using Stemming Method, A Case of Non-formal Indonesian Conversation

Rianto

Mutiara

Wibowo

et al. 2020

Preprint

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Stemming has long been used in data pre-processing in information retrieval, which aims to make affix words into root words. However, there are not many stemming methods for non-formal Indonesian text processing. The existing stemming method has high accuracy for formal Indonesian, but low for non-formal Indonesian. Thus, the stemming method which has high accuracy for non-formal Indonesian classifier model is still an open-ended challenge. This study introduces a new stemming method to solve problems in the non-formal Indonesian text data pre-processing. Furthermore, this study aims to provide comprehensive research on improving the accuracy of text classifier models by strengthening on stemming method. Using the Support Vector Machine algorithm, a text classifier model is developed, and its accuracy is checked. The experimental evaluation was done by testing 550 datasets in Indonesian using two different stemming methods. The results show that using the proposed stemming method, the text classifier model has higher accuracy than the existing methods with a score of 0.85 and 0.73, respectively. In the future, the proposed stemming method can be used to develop the Indonesian text classifier model which can be used for various purposes including text clustering, summarization, detecting hate speech, and other text processing applications.

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“…Indonesian language is classified into two categories, namely formal and non-formal in the method of use. Indonesian formal is used in formality situation, while Indonesian non-formal is widely used in a casual situation like in social media conversations [2]. In formal language, people use "standardized" language as prescribed by linguistic rules of the language.…”

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confidence: 99%

Improving the accuracy of text classification using stemming method, a case of non-formal Indonesian conversation

et al. 2021

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Background Stemming has long been used in data pre-processing to retrieve information by tracking affixed words back into their root. In an Indonesian setting, existing stemming methods have been observed, and the existing stemming methods are proven to result in high accuracy level. However, there are not many stemming methods for non-formal Indonesian text processing. This study introduces a new stemming method to solve problems in the non-formal Indonesian text data pre-processing. Furthermore, this study aims to improve the accuracy of text classifier models by strengthening stemming method. Using the Support Vector Machine algorithm, a text classifier model is developed, and its accuracy is checked. The experimental evaluation was done by testing 550 datasets in Indonesian using two different stemming methods. Findings The results show that using the proposed stemming method, the text classifier model has higher accuracy than the existing methods with a score of 0.85 and 0.73, respectively. These results indicate that the proposed stemming methods produces a classifier model with a small error rate, so it will be more accurate to predict a class of objects. Conclusion The existing Indonesian stemming methods are still oriented towards Indonesian formal sentences, therefore the method has limitations to be used in Indonesian non-formal sentences. This phenomenon underlies the suggestion of developing a corpus by normalizing Indonesian non-formal into formal to be used as a better stemming method. The impact of using the corpus as a stemming method is that it can improve the accuracy of the classifier model. In the future, the proposed corpus and stemming methods can be used for various purposes including text clustering, summarizing, detecting hate speech, and other text processing applications in Indonesian.

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Formal and Non-Formal Indonesian Word Usage Frequency in Twitter Profile Using Non-Formal Affix Rule

Cited by 9 publications

References 10 publications

Improving the Accuracy of Text Classification using Stemming Method, A Case of Informal Indonesian Conversation

Improving the Accuracy of Text Classification using Stemming Method, A Case of Informal Indonesian Conversation

Improving the Accuracy of Text Classification using Stemming Method, A Case of Non-formal Indonesian Conversation

Improving the accuracy of text classification using stemming method, a case of non-formal Indonesian conversation

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