Applying TF-IDF and K-NN for Clickbait Detection in Indonesian Online News Headlines

Muhammad Athallah Afif; Munirul Ula; Lidya Rosnita; Rizal Rizal

doi:10.29103/jacka.v1i2.15810

Applying TF-IDF and K-NN for Clickbait Detection in Indonesian Online News Headlines

Muhammad Athallah Afif, Munirul Ula, Lidya Rosnita, Rizal Rizal

Abstract

This research explores the application of TF-IDF (Term Frequency-Inverse Document Frequency) and K-Nearest Neighbor (K-NN) in constructing a clickbait detection system for Indonesian online news headlines. The TF-IDF method is employed to ascertain the significance of words in news headlines, utilizing a tokenization process to generate numeric representations. The TF-IDF matrix serves as features in the K-NN classification model, with k=1 determining the most similar class. Model evaluation yields outstanding results, achieving accuracy, precision, recall, and F1-Score all reaching 1.0. The confusion matrix unveils no misclassifications, affirming the model's adeptness in correctly classifying all samples.

Keywords

TF-IDF; k-Nearest Neighbor; Clickbait; Online News Headlines; Indonesian

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DOI: https://doi.org/10.29103/jacka.v1i2.15810