Evolutionary deep belief networks with bootstrap sampling for imbalanced class datasets

Amri, A’inur A’fifah; Ismail, Amelia Ritahani; Mohammad, Omar Abdelaziz

doi:10.26555/ijain.v5i2.350

Cited by 4 publications

(3 citation statements)

References 41 publications

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“…Some performance details metrics are discussed briefly below [26]. The accuracy rate is the total number of correctly classified over the total number of samples (true positives and true negatives) [26,38]. The formula for the accuracy rate is shown in (1).…”

Section: Discussionmentioning

confidence: 99%

“…The formula for the accuracy rate is shown in (1). The recall is the proportion of actual positives which are predicted positive [38]. The formula for the recall rate is shown in (2).…”

Section: Discussionmentioning

confidence: 99%

“…The formula for the precision rate is shown in (3). F1-measure evaluates the harmonic value between recall and precision [38]. The formula for the F1-measure rate is shown in (4).…”

Section: Discussionmentioning

confidence: 99%

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Hate Speech Detection for Banjarese Languages on Instagram Using Machine Learning Methods

Alkaff,

Miqdad,

Fachrurrazi

et al. 2023

matrik

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Hate speech refers to verbal expression or communication that aims to provoke or discriminate against individuals. The Ministry of Communication and Information of Indonesia has encountered and dealt with 3,640 cases of hate speech transmitted through digital channels between 2018 and 2021. Particularly in South Kalimantan, hate speech in the local language, Banjarese has become increasingly prevalent in recent years. Surprisingly, there is a lack of research on using machine learning to detect hate speech in the Banjarese language, specifically on Instagram. Therefore, this study aimed to address this gap by constructing a dataset of Banjarese language hate speech and comparing various feature extraction and machine learning models to detect Banjarese language hate speech effectively. Thisresearch used several feature extraction techniques and machine learning methods to detect Banjareselanguage hate speech. The feature extraction methods used were Word N-Gram, Term Frequency- Inverse Document Frequency (TF-IDF), a combination of Word N-Gram and TF-IDF, Word2Vec, and Glove, while the machine learning methods used were Support Vector Machine (SVM), Na¨ıve Bayes, and Decision Tree. The results of this study revealed that the combination of TF-IDF for feature extraction and SVM as the model achieves exceptional performance. The average Recall, Precision, Accuracy, and F1-Score score exceeded 90%, demonstrating the model’s ability to identify Banjarese hate speech accurately.

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Section: Discussionmentioning

confidence: 99%

“…The formula for the accuracy rate is shown in (1). The recall is the proportion of actual positives which are predicted positive [38]. The formula for the recall rate is shown in (2).…”

Section: Discussionmentioning

confidence: 99%