Performance comparison of support vector machine and gaussian naive bayes classiﬁer for youtube spam comment detection

Ifriza, Yahya Nur; Sam'an, Muhammad

doi:10.52465/joscex.v2i2.42

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…Smoothing method is a method for avoiding the results of the classification is zero because the testing data was not found on the training data [21]. Laplace smoothing is a method that adjucts a maximum probability estimate to correct probability instead of zero for data that is invisible and increases model accuracy because of frequency of data [22], [23]. The naive bayes classifier theorem if combined with Laplace smoothing can be seen in equation 5.…”

Section: 𝑃(𝐶 𝑖 |𝑋) = 𝑃(𝑋|𝐶 𝑖 )𝑃(𝐶 𝑖 ) 𝑃(𝑋) (4)mentioning

confidence: 99%

Implementation Data Mining with Naive Bayes Classifier Method and Laplace Smoothing to Predict Students Learning Results

Pradana

Sugiharti

2023

Recursive J. of Informatics

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Abstract. The application of information technology in the field of education produces big data. It retains information that can be treated as useful. Having data mining, can be used to model highly useful student performance for educators performing corrective actions against weak students. Purpose: The study was to identify the application and accuracy algorithm Naive Bayes Classifier to predict students' study results. Methods: The prediction system for student learning outcomes was built using the Naive Bayes Classifier and Laplace Smoothing methods using a combination of two Information Gain and Chi Square feature selections. The experiment was carried out 2 times using different dataset comparisons. Result: In the first experiment using a dataset of 80:20, the accuracy Naive Bayes Classifier method with Laplace Smoothing and without Laplace Smoothing showed the same results as 94.937%. On the second experiment to equate dataset 60:40 results of the Naive Bayes Classifier accurate method without Laplace Smoothing only 86.076%, then score a 91.772% accuracy using the Laplace Smoothing. The improvement is caused by a probability of zero that can be worked out with Laplace Smoothing. Novelty: The selection feature process is very important in the classification process. Thus, in this study, information gain and chi square double selections of such features as information gain and so promote accuracy.

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Section: 𝑃(𝐶 𝑖 |𝑋) = 𝑃(𝑋|𝐶 𝑖 )𝑃(𝐶 𝑖 ) 𝑃(𝑋) (4)mentioning

confidence: 99%

Implementation Data Mining with Naive Bayes Classifier Method and Laplace Smoothing to Predict Students Learning Results

Pradana

Sugiharti

2023

Recursive J. of Informatics

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“…Spam emails are unsolicited, often irrelevant or malicious messages sent in bulk to a large number of recipients without their consent [10]. SMS spam may be exploited to spread hate speech and criminal activity, or to disseminate commercial information, commonly known as spam [11]. Machine learning is part of the Artificial Intelligence (AI) process [12].…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Machine Learning Algorithms for Performing Ham or Spam Classification in SMS

Astuti,

Sari,

Rachmawanto

et al. 2024

SJI

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Purpose: Fraud is rampant in the current era, especially in the era of technology where there is now easy access to a lot of information. Therefore, everyone needs to be able to sort out whether the information received is the right information or information that is fraudulent. In this research, the process of classifying messages including ham or spam has been carried out. The purpose of this research is to be able to build a model that can help classify messages. The purpose of this research is also to determine which machine learning method can accurately and efficiently perform the ham or spam classification process on messages.Methods: In this research, the ham or spam classification process has been using machine learning methods. The machine learning methods used are the classification process with Random Forest, Logistic Regression, Support Vector Classification, Gradient Boosting, and XGBoost Classifier algorithms. Results: The results obtained after testing in this study are the classification process using the Random Forest algorithm getting an accuracy of 97.28%, Logistic Regression getting an accuracy of 94.67%, with Support Vector Classification getting an accuracy of 97.93%, and using XGBoost Classifier getting an accuracy of 96.47%. The best precision value obtained in this study is 98% when using the random forest algorithm. The best recall value is 94% when using the SVC algorithm. While the best f1-score value is 95% when using the SVC algorithm.Novelty: This research has been compared with several algorithms. In previous research, it is still very rarely done using XGBoost to classify the ham or spam in messages. We focus on giving brief information based con comparison algorithm and show the best algorithm to classify classify the ham or spam in messages. And for the novelty that exists from this research, the machine learning model built gets better accuracy when compared to previous research.

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Comparative Analysis of Image Processing Methods using GLCM and LBP Feature Extraction for Lung Cancer Detection

Ahnaf,

Wahyuni

2023

2023 6th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

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Performance comparison of support vector machine and gaussian naive bayes classiﬁer for youtube spam comment detection

Cited by 3 publications

References 21 publications

Implementation Data Mining with Naive Bayes Classifier Method and Laplace Smoothing to Predict Students Learning Results

Implementation Data Mining with Naive Bayes Classifier Method and Laplace Smoothing to Predict Students Learning Results

Comparative Study of Machine Learning Algorithms for Performing Ham or Spam Classification in SMS

Comparative Analysis of Image Processing Methods using GLCM and LBP Feature Extraction for Lung Cancer Detection

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