Text Classification Services Using Naïve Bayes for Bahasa Indonesia

Setiani, Evelyn; Ce, Win

doi:10.1109/icimtech.2018.8528258

Cited by 11 publications

(5 citation statements)

References 5 publications

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“…Penelitian tentang klasifikasi dengan metode text mining telah banyak dilakukan oleh peneliti sebelumnya, diantaranya: Penelitian yang berfokus pada otomatisasi klasifikasi tugas akhir menggunakan deskripsi singkat dibandingkan antara dua algoritma, K-Nearest Neighbor (K-NN) dan Naïve Bayes Classifier, sehingga Naïve Bayes Classifier memiliki akurasi tertinggi (65,4%) dibandingkan dengan K-NN (51,14%) [3]. Naïve Bayes Classifier merupakan algoritma yang memiliki metode yang lebih banyak digunakan karena kesederhanaan dan efektivitas komputasi [4]. Dalam penelitian lain diketahui bahwa algoritma Multinomial Naïve Bayes memiliki kinerja sedikit lebih baik dari algoritma Naïve Bayes Classifier [5], karena Multinomial Naïve Bayes menggunakan distribusi multinomial untuk masing-masing fitur.…”

Section: Pendahuluanunclassified

Pengaruh N-Gram terhadap Klasifikasi Buku menggunakan Ekstraksi dan Seleksi Fitur pada Multinomial Naïve Bayes

Mulyani

Muhamad

Cahyanto

2021

mib

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Libraries have the main task in the processing of library materials by classifying books according to certain ways. Dewey Decimal Classification (DDC) is the method most commonly used in the world to determine book classification (labeling) in libraries. The advantages of this DDC method are universal and more systematic. However, this method is less efficient considering the large number of books that must be classified in a library, as well as labeling that must follow label updates on the DDC. An automatic classification system will be the perfect solution to this problem. Automatic classification can be done by applying the text mining method. In this study, searching for words in the book title was carried out with N-Gram (Unigram, Bigram, Trigram) as a feature generation. The features that have been raised are then selected for features. The process of book title classification is carried out using the Naïve Bayes Multinomial algorithm. This study examines the effect of Unigram, Bigram, Trigram on the classification of book titles using the feature extraction and selection feature on Multinomial Naïve Bayes algorithm. The test results show Unigram has the highest accuracy value of 74.4%.

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Section: Pendahuluanunclassified

Pengaruh N-Gram terhadap Klasifikasi Buku menggunakan Ekstraksi dan Seleksi Fitur pada Multinomial Naïve Bayes

Mulyani

Muhamad

Cahyanto

2021

mib

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“…Preprocessing in other Indonesian language libraries is also used in this study. The library used to process Indonesian language is the Sastrawi library to carry out the stemming process and the stopword removal process [11]. The difficulty in preprocessing text using Indonesian is due to the large number of slang words in Indonesian language so it is necessary to add words to existing slang words to remove the words.…”

Section: B Preprocessingmentioning

confidence: 99%

Classification of User Comment Using Word2vec and Deep Learning

Kurnia¹,

Girsang²

2021

IJETAE

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This study will classify the text based on the rating of the provider application on the Google Play Store. This research is classification of user comments using Word2vec and the deep learning algorithm in this case is Long Short Term Memory (LSTM) based on the rating given with a rating scale of 1-5 with a detailed rating 1 is the lowest and rating 5 is the highest data and a rating scale of 1-3 with a detailed rating, 1 as a negative is a combination of ratings 1 and 2, rating 2 as a neutral is rating 3, and rating 3 as a positive is a combination of ratings 4 and 5 to get sentiment from users using SMOTE oversampling to handle the imbalance data. The data used are 16369 data. The training data and the testing data will be taken from user comments MyTelkomsel’s application from the play.google.com site where each comment has a rating in Indonesian Language. This review data will be very useful for companies to make business decisions. This data can be obtained from social media, but social media does not provide a rating feature for every user comment. This research goal is that data from social media such as Twitter or Facebook can also quickly find out the total of the user satisfaction based from the rating from the comment given. The best f1 scores and precisions obtained using 5 classes with LSTM and SMOTE were 0.62 and 0.70 and the best f1 scores and precisions obtained using 3 classes with LSTM and SMOTE were 0.86 and 0.87

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“…However, since the comment texts used are in the Indonesian language, a different library must also be used which is based on the Indonesian language. This research uses Sastrawi library for the stemming process and for eliminating stop words in the Indonesian language [17]. The difficulty in doing the preprocessing stage is eliminating various slang words in the Indonesian language, and so some slang words are added into the list of Indonesian stop words so they can be removed.…”

Section: Preprocessingmentioning

confidence: 99%

Classification of User Comment Using Word2vec and SVM Classifier

Kurnia¹

2020

IJATCSE

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Social media provides data that can be used for text classification, but these social media do not have a rating system. Comments taken from social media such as Twitter, does not provide a rating system which can help to classify comments based on their rating score. The goal of this study is to build a comment classification model using Word2vec and SVM classifier that can classify comments based on a rating scale from 1-5. The training data will be taken from user comments from play.google.com website in which each comment already has a rating. The purpose of the model is to classify comments from social media about mobile network applications. Comment classification is performed in order to help these businesses, as well as the users, to know the overall satisfaction of users who use these applications. The best F1 score obtained from this research using class elimination and stop word removal is 0.795.

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Text Classification Services Using Naïve Bayes for Bahasa Indonesia

Cited by 11 publications

References 5 publications

Pengaruh N-Gram terhadap Klasifikasi Buku menggunakan Ekstraksi dan Seleksi Fitur pada Multinomial Naïve Bayes

Pengaruh N-Gram terhadap Klasifikasi Buku menggunakan Ekstraksi dan Seleksi Fitur pada Multinomial Naïve Bayes

Classification of User Comment Using Word2vec and Deep Learning

Classification of User Comment Using Word2vec and SVM Classifier

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