Klasifikasi Berita Clickbait Menggunakan K-Nearest Neighbor (KNN)

Sagita, Riska; Enri, Ultach; Primajaya, Aji

doi:10.33633/joins.v5i2.3705

Cited by 7 publications

(13 citation statements)

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“…Clickbait news classification [6] with three scenarios KNN with euclidean distance and the value of parameter K ranging from 1 to 15 with data of 1000 news published from January 2020.. The scenario used a combination number of training and testing data (80:20, 50:50 and 20:80).…”

Section: Previous Workmentioning

confidence: 99%

“…Testing stage is not explained detail calculation in the testing used. KNN [6] Classification of clickbait news with KNN obtained an accuracy of 71% at K = 11 with 80:20 data sharing.…”

Section: Thismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Jabar Saber Hoaks [3] Hoax news was found on news portals [4] social media [5] and clickbait news [6]. Machine learning models used by researchers in detecting hoax news, such as Naive Bayes [4], SVM [5], and KNN [6]. KNN uses distance value d(a,b) between the training data and testing data with parameter K (number of neighbor) [6] [7].…”

Section: Introductionmentioning

confidence: 99%

“…Machine learning models used by researchers in detecting hoax news, such as Naive Bayes [4], SVM [5], and KNN [6]. KNN uses distance value d(a,b) between the training data and testing data with parameter K (number of neighbor) [6] [7]. Euclidean distance performance in KNN has not shown its best performance.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Covid-19 Hoax Detection Using KNN in Jaccard Space

Utami¹,

Iskandar²,

Hidayat³

et al. 2021

Indonesian J. Comput. Cybern. Syst.

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Social media has become a communication key to spark thinking, dialogue and action around social issues. Hoax is information that added or subtracted from the content of the actual news. The spread of unconfirmed Covid-19 news can cause public concern. The purpose of this research was to modify KNN with Jaccard Space in the classification of hoax news related to Covid-19. The data used from Jabar Saber Hoaks and Jala Hoaks. The classification results with KNN with Jaccard Space and stemming Nazief & Adriani get the highest accuracy than other models in this research. The accuracy of the KNN model on the Jaccard Space with stemming Nazief & Adriani and K = 5 was 75.89%, while for Naïve Bayes was 65.18%.

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Section: Previous Workmentioning

confidence: 99%

“…Testing stage is not explained detail calculation in the testing used. KNN [6] Classification of clickbait news with KNN obtained an accuracy of 71% at K = 11 with 80:20 data sharing.…”

Section: Thismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Covid-19 Hoax Detection Using KNN in Jaccard Space

Utami¹,

Iskandar²,

Hidayat³

et al. 2021

Indonesian J. Comput. Cybern. Syst.

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Clickbait Detection of Indonesian News Headlines using Fine-Tune Bidirectional Encoder Representations from Transformers (BERT)

Putri

Pratomo

2022

Inf. J. Ilm. Bid. Teknol. Inf. dan Komun.

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The problem of the existence of news article that does not match with content, called clickbait, has seriously interfered readers from getting the information they expect. The number of clickbait news continues significantly increased in recent years. According to this problem, a clickbait detector is required to automatically identify news article headlines that include clickbait and non-clickbait. Additionally, many currently existing solutions use handcrafted features and traditional machine learning methods, which limit the generalization. Therefore, this study fine-tunes the Bidirectional Encoder Representations from Transformers (BERT) and uses the Indonesian news headlines dataset CLICK-ID to predict clickbait (BERT). In this research, we use IndoBERT as the pre-trained model, a state-of-the-art BERT-based language model for Indonesian. Then, the usefulness of BERT-based classifiers is then assessed by comparing the performance of IndoBERT classifiers with different pre-trained models with that of two word-vectors-based approaches (i.e., bag-of-words and TF-IDF) and five machine learning classifiers (i.e., NB, KNN, SVM, DT, and RF). The evaluation results indicate that all fine-tuned IndoBERT classifiers outperform all word-vectors-based machine learning classifiers in classifying clickbait and non-clickbait Indonesian news headlines. The IndoBERTBASE using the two training phases model gets the highest accuracy of 0.8247, which is 0.064 (6%), outperforming the SVM classifier's accuracy with the bag-of-words model 0.7607.

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

Afif,

Ula,

Rosnita

et al. 2024

Jo. Adv. Comp. Know. Algo

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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.

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Klasifikasi Berita Clickbait Menggunakan K-Nearest Neighbor (KNN)

Cited by 7 publications

References 0 publications

Covid-19 Hoax Detection Using KNN in Jaccard Space

Covid-19 Hoax Detection Using KNN in Jaccard Space

Clickbait Detection of Indonesian News Headlines using Fine-Tune Bidirectional Encoder Representations from Transformers (BERT)

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

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