NAKNN: An Efficient Classification of Indonesian News Texts with Nazief-Adriani and KNN

Basirudin Ansor(1*), Aditya Putra Ramdani(2), Nova Christina Sari(3), Muhammad Zainudin Al Amin(4), Achmad Solichan(5), Kilala Mahadewi(6)


(1) Universitas Muhammadiyah Semarang, Semarang, Indonesia
(2) Universitas Muhammadiyah Semarang, Semarang, Indonesia
(3) Universitas Muhammadiyah Semarang, Semarang, Indonesia
(4) Universitas Muhammadiyah Semarang, Semarang, Indonesia
(5) Universitas Muhammadiyah Semarang, Semarang, Indonesia
(6) Universitas Muhammadiyah Semarang, Semarang, Indonesia
(*) Corresponding Author

Abstract


Internet usage in Indonesia has seen a significant increase, reaching 215.63 million users in 2022-2023, or 78.19% of the population. With the ease of internet access, digital news portals like Narasi TV have become a primary source of information for many people. However, the large number of news articles makes manual categorizing challenging. This study aims to classify Indonesian-language news documents from Narasi TV using the Nazief-Adriani algorithm for stemming and the K-Nearest Neighbor (KNN) method for classification. The text mining process begins with preprocessing, which includes case folding, tokenizing, stop-word filtering, and stemming. Using a dataset of 500 news documents, the study demonstrated that with a 90:10 data split, the average accuracy reached 93%, with the highest value being 100%. For the 80:20 data split, the average accuracy was 89%, with the highest value being 93%, and for a 70:30 data split, the average accuracy was 87%, with the highest value being 89%. In conclusion, the combination of the Nazief-Adriani algorithm and the KNN method with optimal k selection and random states obtained high accuracy, obtaining an average accuracy of 93%) in classifying Indonesian-language news documents. These results demonstrate the significant potential of text mining and classification techniques to manage digital news.

Keywords


Text Mining, News Classification; Nazief-Adriani; K-Nearest Neighbor;

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References


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DOI: https://doi.org/10.26714/jichi.v5i2.15420

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Journal of Intelligent Computing and Health Informatics (JICHI)
ISSN 2715-6923 (print) | 2721-9186 (online)
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Department of Informatics
Faculty of Engineering
Universitas Muhammadiyah Semarang

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