A Comparative Analysis of Classification Algorithms for Cyberbullying Crime Detection: An Experimental Study of Twitter Social Media in Indonesia

Muzakir, Ari; Syaputra, Hadi; Panjaitan, Febriyanti

doi:10.15294/sji.v9i2.35149

Cited by 6 publications

(4 citation statements)

References 15 publications

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“…The hate speech that is expressed is often inseparable from SARA (tribe, religion, race and inter-group) motives. This statement is said because it shows that users use the Twitter social media platform to convey ideas, criticize, disseminate information quickly and have arguments between people [8]- [11]. fellow social media users.…”

Section: Introductionmentioning

confidence: 99%

Indonesian Hate Speech Text Classification Using Improved K-Nearest Neighbor with TF-IDF-ICSρF

Saputra,

Aeni,

Saraswati

2024

SJI

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Purpose: Freedom in social media gives rise to the possibility of disturbing users through the sentences they send, which is limited by the Electronic Information and Transactions Law (UU ITE). This research aims to find an effective method for classifying hate speech text data, especially in Indonesian, with many categories expected to minimize this case.Methods: This study used 1.000 data from Twitter with five labels, including religion, race, physical, gender and other (invective or slander). The process started with several steps of preprocessing, data transformation using TF-IDF-ICSρF term weighting and data mining using an Improved KNN algorithm. Then, the results were compared with the TF-IDF and KNN methods to evaluate the differences.Result: Using TF-IDF-ICSρF and Improved KNN algorithms gets an average accuracy value of 88.11%, 17.81% higher compared with the same data and parameters to the K-Nearest Neighbor and TF-IDF algorithms, which get results of 70.30%.Novelty: Based on the comparison results, TF-IDF-ICSρF and Improved KNN methods can effectively classify hate speech sentences that have many labels with fairly good accuracy.

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Section: Introductionmentioning

confidence: 99%

Indonesian Hate Speech Text Classification Using Improved K-Nearest Neighbor with TF-IDF-ICSρF

Saputra,

Aeni,

Saraswati

2024

SJI

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“…This study seeks to address this gap by comparing the performance of SVM and Random Forest (RF) algorithms in hoax analysis on Twitter. SVM and RF were chosen due to their excellent performance in text mining [8], [9], [11], [18]. The study's contribution is twofold: first, it scientifically evaluates the performance of SVM and RF for hoax analysis, and second, it offers practical recommendations on government policy regarding the capital relocation.…”

Section: Introductionmentioning

confidence: 99%

Hoax News Analysis for the Indonesian National Capital Relocation Public Policy with the Support Vector Machine and Random Forest Algorithms

et al. 2023

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The decision of the Indonesian government to relocate the nation's capital outside Java to the North Penajam Paser Regency has sparked controversy and misinformation on social media platforms. While sentiment analysis studies have been conducted on this topic, no research has yet analyzed the issue of hoaxes related to the relocation of the national capital. This study aims to fill this gap by analyzing hoaxes related to the relocation of the Indonesian national capital on Twitter. The study utilizes data crawling, filtering with Hoax Booster Tools (HBT) ASE, data labeling, preprocessing, and TF-IDF weighting. The data is then classified using Support Vector Machine (SVM) and Random Forest (RF) algorithms, and the results of both algorithms are compared. The study found that 85% of tweets had a positive sentiment and 15% had a negative sentiment. Furthermore, the SVM algorithm outperformed the RF algorithm with an accuracy of 95.24% compared to 86.90%. This study contributes to the understanding of the hoax issues related to the relocation of the Indonesian state capital and provides recommendations for government policies to address community concerns.

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“…The proposed method uses a dataset of 1053 comments on Instagram, which obtains an accuracy of 79%. Then, Muzakir et al [14] also detect cyberbullying using a Support Vector Machine (SVM) and Bag of Words (BoW) as feature extraction. They reported that the accuracy score is 76% using a dataset from Twitter with 1065 tweets.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Cyberbullying Detection on Indonesian Twitter: Leveraging FastText for Feature Expansion and Hybrid Approach Applying CNN and BiLSTM

Nasution,

Setiawan

2023

RIA

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Cyberbullying, characterized by the transmission of threatening, intimidating, and derogatory messages via digital platforms such as Twitter, is a pervasive issue. Given the volume of approximately 867 million daily tweets, the potential scale of cyberbullying incidents is immense, underscoring the necessity for automated detection systems for such messages. However, the context-sensitive nature of tweets can pose challenges to understanding message content, particularly in languages like Indonesian with potential for significant vocabulary discrepancies. This study aims to enhance cyberbullying detection by employing feature expansion using FastText, thereby addressing vocabulary-related comprehension issues in Indonesian-language tweets. Furthermore, text classification is performed using a Hybrid Deep Learning approach, integrating Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM). This hybrid model leverages the strengths of both techniques, capturing local patterns and long-range dependencies within the data. The objective of this research is to evaluate the performance yielded by the application of FastText-enhanced feature expansion and Hybrid Deep Learning to an Indonesian Twitter dataset. This focus is motivated by the high accuracy of Hybrid Deep Learning for Twitter datasets in other languages, and the limited application of such methods to Indonesian-language datasets, which predominantly use supervised learning or deep learning. Analysis of 29,085 datasets demonstrated that the combined implementation of Hybrid Deep Learning and FastText-enhanced feature expansion achieved the highest accuracy, with CNN-BiLSTM and BiLSTM-CNN scoring 80.55% and 80.35% respectively. These findings validate the significant accuracy boost provided by FastText when integrated with Hybrid Deep Learning. It is anticipated that the outcomes of this study will facilitate the accurate identification and removal of cyberbullying tweets, thereby contributing to a safer digital communication environment on Twitter.

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A Comparative Analysis of Classification Algorithms for Cyberbullying Crime Detection: An Experimental Study of Twitter Social Media in Indonesia

Cited by 6 publications

References 15 publications

Indonesian Hate Speech Text Classification Using Improved K-Nearest Neighbor with TF-IDF-ICSρF

Indonesian Hate Speech Text Classification Using Improved K-Nearest Neighbor with TF-IDF-ICSρF

Hoax News Analysis for the Indonesian National Capital Relocation Public Policy with the Support Vector Machine and Random Forest Algorithms

Enhancing Cyberbullying Detection on Indonesian Twitter: Leveraging FastText for Feature Expansion and Hybrid Approach Applying CNN and BiLSTM

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