research-article

News Article Text Classification in Indonesian Language

Authors:

Ferdinand Ariandy Luwinda,

Brandon Christian Trisnajaya,

Olivia RusliAuthors Info & Claims

Procedia Computer Science, Volume 116, Issue C

Pages 137 - 143

https://doi.org/10.1016/j.procs.2017.10.039

Published: 01 November 2017 Publication History

Abstract

This research intends to find the appropriate algorithm to automatically classify a news article in Indonesian Language. We obtain our dataset which is taken by using a web crawling method from www.cnnindonesia.com. First of all, the document will first undergo some Text Preprocessing method in the form of Lemmatization and Stopwords Removal. The reason we are doing the Text Preprocessing step before anything else is to minimize the noise in the document. Next, we apply Feature Selection onto the document to further separate important words and less important words inside the document. After applying Feature Selection, the document will be classified by the classifier. We are comparing the TF-IDF and SVD algorithm for feature selection, while also comparing the Multinomial Nave Bayes, Multivariate Bernoulli Nave Bayes, and Support Vector Machine for the Classifiers. Based on the test results, the combination of TF-IDF and Multinomial Nave Bayes Classifier gives the highest result compared to the other algorithms, which precision is 0.9841519 and its recall is 0.9840000. The result outperform the previous similar study that classify news article in Indonesian language which obtained 85% of accuracy.

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Cited By

Magalhães Serra AEstima JRodrigues da Silva A(2023)Evaluation of Maestro, an extensible general-purpose data gathering and data classification platformInformation Processing and Management: an International Journal10.1016/j.ipm.2023.10345860:5Online publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1016/j.ipm.2023.103458
Rahman R(2020)A Benchmark Study on Machine Learning Methods using Several Feature Extraction Techniques for News Genre Detection from Bangla News Articles & TitlesProceedings of the 7th International Conference on Networking, Systems and Security10.1145/3428363.3428373(25-35)Online publication date: 22-Dec-2020
https://dl.acm.org/doi/10.1145/3428363.3428373
Hajar EMohammed B(2019)Using Synonym and Definition WordNet Semantic relations for implicit aspect identification in Sentiment AnalysisProceedings of the 2nd International Conference on Networking, Information Systems & Security10.1145/3320326.3320406(1-5)Online publication date: 27-Mar-2019
https://dl.acm.org/doi/10.1145/3320326.3320406

News Article Text Classification in Indonesian Language
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning approaches

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Published In

cover image Procedia Computer Science

Procedia Computer Science Volume 116, Issue C

November 2017

656 pages

ISSN:1877-0509

EISSN:1877-0509

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Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 November 2017

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Cited By

Magalhães Serra AEstima JRodrigues da Silva A(2023)Evaluation of Maestro, an extensible general-purpose data gathering and data classification platformInformation Processing and Management: an International Journal10.1016/j.ipm.2023.10345860:5Online publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1016/j.ipm.2023.103458
Rahman R(2020)A Benchmark Study on Machine Learning Methods using Several Feature Extraction Techniques for News Genre Detection from Bangla News Articles & TitlesProceedings of the 7th International Conference on Networking, Systems and Security10.1145/3428363.3428373(25-35)Online publication date: 22-Dec-2020
https://dl.acm.org/doi/10.1145/3428363.3428373
Hajar EMohammed B(2019)Using Synonym and Definition WordNet Semantic relations for implicit aspect identification in Sentiment AnalysisProceedings of the 2nd International Conference on Networking, Information Systems & Security10.1145/3320326.3320406(1-5)Online publication date: 27-Mar-2019
https://dl.acm.org/doi/10.1145/3320326.3320406

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