Multi-Label Classification of Indonesian Hate Speech Detection Using One-vs-All Method

Utami, Ema; Rini, Rini; Iskandar, Ahmad Fikri; Raharjo, Suwanto

doi:10.1109/icitisee53823.2021.9655883

Cited by 6 publications

(3 citation statements)

References 13 publications

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“…We carefully reviewed each document to obtain the key information of each work. In this part, we focus on [11], [30], [17], [23], [12], [28], [27], [21], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40]…”

Section: B What Has Been Done So Far In Indonesian Abusive Language D...mentioning

confidence: 99%

Hate Speech Detection in Bahasa Indonesia: Challenges and Opportunities

Pamungkas¹,

Putri²,

Fatmawati³

2023

IJACSA

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This study aims to provide an overview of the current research on detecting abusive language in Indonesian social media. The study examines existing datasets, methods, and challenges and opportunities in this field. The research found that most existing datasets for detecting abusive language were collected from social media platforms such as Twitter, Facebook, and Instagram, with Twitter being the most commonly used source. The study also found that hate speech is the most researched type of abusive language. Various models, including traditional machine learning and deep learning approaches, have been implemented for this task, with deep learning models showing more competitive results. However, the use of transformer-based models is less popular in Indonesian hate speech studies. The study also emphasizes the importance of exploring more diverse phenomena, such as islamophobia and political hate speech. Additionally, the study suggests crowdsourcing as a potential solution for the annotation approach for labeling datasets. Furthermore, it encourages researchers to consider code-mixing issues in abusive language datasets in Indonesia, as it could improve the overall model performance for detecting abusive language in Indonesian data. The study also suggests that the lack of effective regulations and the anonymity afforded to users on most social networking sites, as well as the increasing number of Twitter users in Indonesia, have contributed to the rising prevalence of hate speech in Indonesian social media. The study also notes the importance of considering code-mixed language, out-of-vocabulary words, grammatical errors, and limited context when working with social media data.

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Section: B What Has Been Done So Far In Indonesian Abusive Language D...mentioning

confidence: 99%

Hate Speech Detection in Bahasa Indonesia: Challenges and Opportunities

Pamungkas¹,

Putri²,

Fatmawati³

2023

IJACSA

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show abstract

“…It is a learning algorithm based on the principle that instances within a data set will generally exist close to other instances with similar properties [17]. This methodology does not generate a model resulting from learning with training data, but rather learning happens at the same time that the test data is tested.…”

Section: K-nearest Neighbout (Knn)mentioning

confidence: 99%

Monitoring and Detection of Wind Turbine Vibration with KNN-Algorithm

Vives¹

2022

JCC

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Maintenance for wind turbines has been transformed using supervised machine learning techniques. This method of automatic and autonomous learning can identify, monitor, and detect electrical and mechanical components of wind turbines and predict, detect, and anticipate their degeneration. Using a machine learning classifier and frequency analysis, we simulate two failure states caused by bearing vibrations. Implementing KNN facilitates efficient monitoring, monitoring, and fault-finding for wind turbines. It is possible to reduce downtime, anticipate breakdowns, and import offshore aspects through these technologies.

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“…That is, the magnitude of f(x * ) indicates how far x * is from the separation hyperplane, and the further it is, the higher the level of confidence that the observation x * has been correctly classified [17].…”

Section: One-vs-all Rankingmentioning

confidence: 99%

SVM-Algorithm for Supervision, Monitoring and Detection Vibration in Wind Turbines

Vives¹,

Palací²,

Heart³

2022

JCC

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With the implementation of supervised machine learning techniques, wind turbine maintenance has been transformed. A wind turbine's electrical and mechanical components can be automatically identified, monitored, and detected to predict, detect, and anticipate their degeneration using this method of automatic and autonomous learning. Two different failure states are simulated due to bearing vibrations and compared with machine learning classifier and frequency analysis. A wind turbine can be monitored, monitored, and faulted efficiently by implementing SVM. With these technologies, downtime can be reduced, breakdowns can be anticipated, and aspects can be imported if they are offshore.

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Multi-Label Classification of Indonesian Hate Speech Detection Using One-vs-All Method

Cited by 6 publications

References 13 publications

Hate Speech Detection in Bahasa Indonesia: Challenges and Opportunities

Hate Speech Detection in Bahasa Indonesia: Challenges and Opportunities

Monitoring and Detection of Wind Turbine Vibration with KNN-Algorithm

SVM-Algorithm for Supervision, Monitoring and Detection Vibration in Wind Turbines

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