Automatic multilabel classification for Indonesian news articles

Rahmawati, Dyah; Khodra, Masayu Leylia

doi:10.1109/icaicta.2015.7335382

Cited by 18 publications

(9 citation statements)

References 10 publications

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“…The study selected a subset of feature using a Genetic algorithm, and used the results for the classification process. Previous research also utilized several types of feature selection methods, including the Symmetrical Uncertainty (SU) method, Information Gain (IG), and Correlation Coefficient (CC) [3]. SU and CC are the methods resulting from the normalization of the IG method.…”

Section: Methodsmentioning

confidence: 99%

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Development of Rule-Based Feature Extraction in Multi-label Text Classification

Mediamer¹,

Adiwijaya²,

Faraby³

2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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Hadith is the second main guidelines after the Holy Quran in the Islamic religion, which was revealed through the Messenger of Allah. Today, Hadith can classified by more than one class such as advice class, prohibited, and information to facilitate readers of Hadith in filtering the appropriate classes for each Hadith of Rasulullah SAW. In the course of research, there are many kinds of data involved in a text classification study. Therefore, special handling that fit with the characteristics of certain data is required. This study investigates the handling of multi-label data-Hadith Bukhari in Indonesian translation-focusing on feature extraction, feature weighted, and preprocessing methods. This study uses a rule-based feature extraction combined with several types of preprocessing along with three types of feature-weighted methods: TF-IDF, Word2vec, and Word2vec weighted with TF-IDF, the five preprocessing stages in this research: Case Folding, Tokenization, Remove Punctuation, Stopword Removal, and Stemming. From the 13 experiments conducted in this study consist of 2000 hadiths, it was found that the best performance for multi-label classification of Hadith data produced by the combination of the proposed rule-based feature extraction, Word2vec feature weighted method, and without using Stemming and Stopword Removal in the preprocessing phase. The Hamming Loss value obtained from this combination was 0.0623. The results show that our rule-based feature extraction method better than baseline method.

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Section: Methodsmentioning

confidence: 99%

“…Text classification on Hadith proposed in [2], but the task is not to classify Hadith into multi-label class. The studies of [3], [4], [5], and [6] explained how multi-label classification task was done using different data for each study.…”

Section: Introductionmentioning

confidence: 99%

Development of Rule-Based Feature Extraction in Multi-label Text Classification

Mediamer¹,

Adiwijaya²,

Faraby³

2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…The paper focused on four factors, i.e., feature weighting, feature selection method, multi-label classification, approach and single-label classification approach. The experimental results showed that the Support Vector Machine (SVM) algorithm performed the best when it was used with TF-IDF [3].…”

Section: Literature Surveymentioning

confidence: 99%

Analysis on News Articles Classification using Machine Learning

Ravikumar¹

2020

IJRASET

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“…Banyak penelitian yang telah dilakukan dalam mengklasifikasikan berita menggunakan klasifikasi teks, dalam penelitian yang dilakukan Septian dan tim [4] serta Asy'arie dan partnernya [5] dalam mengklasifikasikan berita menggunakan metode klasifikasi Naïve Bayes menunjukkan bahwa walaupun Naïve Bayes merupakan metode yang sederhana dengan akurasi yang tinggi, namun menimbulkan masalah performansi jika memproses data yang besar. Sedangkan penelitian yang dilakukan Tej dan partnernya [6] serta penelitian yang dilakukan Dyah dan partnernya [7] menunjukkan bahwa metode Support Vector Machine dapat mengatasi masalah dimensi dan memberikan hasil lebih baik dibandingkan metode lainnya dalam mengklasifikasikan berita.…”

Section: Pendahuluanunclassified

“…Sehingga pada penelitian ini, untuk membuktikan bahwa SVM merupakan metode terbaik untuk mengklasifikasikan data teks maka penulis membandingkan penggunaan metode Support Vector Machine dengan metode klasifikasi lainnya seperti K-Nearest Neighbor (KNN), Neural Network (NN), dan Naïve Bayes (NB) untuk mengklasifikasikan berita Bahasa Indonesia. Untuk menangani masalah fitur yang sangat besar pada klasifikasi teks [5] [6] [7] dan meningkatkan kinerja sistem klasifikasi, maka pada penelitian ini akan dilakukan pengurangan fitur dengan metode seleksi fitur yaitu Mutual Information. Mutual Information dipilih sebagai metode seleksi fitur pada penelitian ini dikarenakan memiliki titik fokus terhadap hubungan term kata dengan suatu kelas, sehingga fitur yang dihasilkan dari proses ini mampu meningkatkan akurasi dari proses klasifikasi yang digunakan [9].…”

Section: Pendahuluanunclassified

Klasifikasi Berita Bahasa Indonesia Menggunakan Mutual Information dan Support Vector Machine

Irham

Adiwijaya

Wisesty

2019

mib

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News is a source of information disseminated in various types of media. In order to make it easier for news readers to obtain the desired news, the news needs to be classified. The large number of scattered news creates difficulties in classifying the news based on the topic. Therefore the author conducted a study to classify news into 12 classes (culture, economy, entertainment, law, health, life, automotive, education, politics, sports, technology, and tourism) automatically against 360 Indonesian news data. In this study several test scenarios were conducted to see the effect of stopword removal and stemming methods on data preprocessing, the effect of mutual information in selecting features, and performance of Support Vector Machine in classifying news data. The test results showed that the data using only stemming without stopword removal, using the MI selection feature and SVM classification method produced the best results of 94.24%, compared to the other methods.

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Automatic multilabel classification for Indonesian news articles

Cited by 18 publications

References 10 publications

Development of Rule-Based Feature Extraction in Multi-label Text Classification

Development of Rule-Based Feature Extraction in Multi-label Text Classification

Analysis on News Articles Classification using Machine Learning

Klasifikasi Berita Bahasa Indonesia Menggunakan Mutual Information dan Support Vector Machine

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