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

Rianto, Rianto; Mutiara, Achmad Benny; Wibowo, Eri Prasetyo; Santosa, Paulus Insap

doi:10.21203/rs.3.rs-41431/v1

Cited by 2 publications

(2 citation statements)

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“…Because formal language is rigid, people rarely use formal sentences in everyday conversation. In daily communication, non-formal language is more intensively used because they are more practical and easier to understand [11]. We often encounter formal sentences in speeches, official events, official documents, or in scientific writing.…”

Section: Bahasa Indonesia Structurementioning

confidence: 99%

Developing Medical Virtual Teaching Assistant Based on Speech Recognition Technology

et al. 2021

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This paper proposes to present the results of the development of the Virtual Teaching Assistant (VTA). This system is an e-learning module as a learning aid for medical students currently pursuing professional medical doctor in hospitals. In Indonesia, students of the medical doctor profession education must study and work in hospitals like an experienced doctor. They interact directly with patients and provide the same services as doctors. Every student has a professional doctor at the hospital as a mentor or companion. However, student meetings with accompanying doctors are minimal. It is not uncommon for students to encounter difficulties when dealing with patients, but they do not immediately receive guidance. As students, it is natural that they sometimes forget the theory. These students need a theoretical learning source that is fast and practical, which students can use between activities. We developed VTA to meet the needs of information and fast learning resources. VTA can run on computer, laptop, or smartphones by utilizing speech recognition technology. Students only need to ask questions in the form of speech using their everyday language, and VTA will provide answers. Although the VTA answer is still not satisfactory, it potentially to support Question and Answer-based mobile learning for particularly learning subject.

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Section: Bahasa Indonesia Structurementioning

confidence: 99%

Developing Medical Virtual Teaching Assistant Based on Speech Recognition Technology

et al. 2021

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“…The approach applied is a classical machine learning model: Naïve Bayes [3][5] [6]. Studies related to this research such as The Effect of Stemmer of Bahasa [1], The Comparison of Stemmer of Bahasa [7], Text Classification on Sentiment Analysis [7][8] [9], and etc. These studies were done to optimize the accuracy value.…”

Section: Introductionmentioning

confidence: 99%

The Accuracy Improvement of Text Mining Classification on Hospital Review through The Alteration in The Preprocessing Stage

Saputro

Hermawan

2021

IJCIT

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Sentiment analysis is a part of text mining used to dig up information from a sentence or document. This study focuses on text classification for the purpose of a sentiment analysis on hospital review by customers through criticism and suggestion on Google Maps Review. The data of texts collected still contain a lot of nonstandard words. These nonstandard words cause problem in the preprocessing stage. Thus, the selection and combination of techniques in the preprocessing stage emerge as something crucial for the accuracy improvement in the computation of machine learning. However, not all of the techniques in the preprocessing stage can contribute to improve the accuracy on classification machine. The objective of this study is to improve the accuracy of classification model on hospital review by customers for a sentiment analysis modeling. Through the implementation of the preprocessing technique combination, it can produce a highly accurate classification model. This study experimented with several preprocessing techniques: (1) tokenization, (2) case folding, (3) stop words removal, (4) stemming, and (5) removing punctuation and number. The experiment was done by adding the preprocessing methods: (1) spelling correction and (2) Slang. The result shows that spelling correction and Slang method can assist for improving the accuracy value. Furthermore, the selection of suitable preprocessing technique combination can fasten the training process to produce the more ideal text classification model.

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

Cited by 2 publications

References 20 publications

Developing Medical Virtual Teaching Assistant Based on Speech Recognition Technology

Developing Medical Virtual Teaching Assistant Based on Speech Recognition Technology

The Accuracy Improvement of Text Mining Classification on Hospital Review through The Alteration in The Preprocessing Stage

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