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All Journal Brilliance: Research of Artificial Intelligence
Pratama, Suria
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Decision Sciences, Operations Research & Management Mathematics Other

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Sentiment Analysis on Social Media X (Twitter) Against ChatGBT Using the K-Nearest Neighbors Algorithm Arwan Sulaeman, Asep; Danny, Muhtajuddin; Butsianto, Sufajar; Pratama, Suria
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.4105

Abstract

This research aims to analyze the public's response to ChatGPT through data obtained from Twitter. Apart from that, it is also to understand whether people's responses tend to be positive or negative towards ChatGPT, as well as to test the performance of the K-Nearest Neighbors (KNN) method in classifying sentiment patterns in tweet data. The sentiment analysis method is carried out by dividing public responses into positive and negative categories. Next, the performance of the K-Nearest Neighbors (KNN) method was tested with varying k values ??to classify sentiment patterns in tweet data. This testing includes dataset division, vectorization of text data using TF-IDF, initialization and training of the KNN model, and evaluation of model performance using metrics such as precision, recall, and f1-score. The results of sentiment analysis show that the majority of people's responses to ChatGPT are positive (74.3%), while 25.7% of responses are negative. Performance testing of the KNN model shows that the highest accuracy of 88% is achieved when the k value is 5. Evaluation of model performance also shows satisfactory levels of precision, recall and f1-score. Based on the research results, it was concluded that sentiment analysis and classification using KNN were effective in understanding people's responses to ChatGPT
Implementation of the Naive Bayes Algorithm for Death Due to Heart Failure Using Rapid Miner Surojudin, Nurhadi; Ermanto, Ermanto; Danny, Muhtajuddin; Pratama, Suria
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.4136

Abstract

Until now there is no treatment that can specifically treat heart failure problems. Heart failure treatment only functions to control symptoms, improve quality of life so that patients can carry out normal activities, and reduce the risk of complications due to heart failure such as heart rhythm disturbances, kidney and lung function disorders, stroke, and sudden death. Heart failure is a condition when the heart pump weakens so that it is unable to circulate sufficient blood throughout the body. This condition is also called congestive heart failure. Until now there is no treatment that can specifically treat heart failure problems. This research is a descriptive study which aims to describe the condition of heart failure. By using classification techniques in data mining on data from patients suffering from heart failure using the Naive Bayes algorithm. By using the Rapid Miner tool, data processing is based on the dataset, using classification techniques and data mining stages to classify data on patients suffering from heart failure. By using the Rapid Miner tool, the data processing that will be used as a data collection in this research is collected into 90% training data and 10% testing data. The research results showed an accuracy rate of 80.00%, precision of 66.67% and recall of 100.00%. Based on the research that has been conducted, it is concluded that classification techniques using the Naive Bayes algorithm can be used to determine the potential for life and death in heart failure sufferers.
Sentiment Analysis on Social Media X (Twitter) Against ChatGBT Using the K-Nearest Neighbors Algorithm Arwan Sulaeman, Asep; Danny, Muhtajuddin; Butsianto, Sufajar; Pratama, Suria
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.4105

Abstract

This research aims to analyze the public's response to ChatGPT through data obtained from Twitter. Apart from that, it is also to understand whether people's responses tend to be positive or negative towards ChatGPT, as well as to test the performance of the K-Nearest Neighbors (KNN) method in classifying sentiment patterns in tweet data. The sentiment analysis method is carried out by dividing public responses into positive and negative categories. Next, the performance of the K-Nearest Neighbors (KNN) method was tested with varying k values ??to classify sentiment patterns in tweet data. This testing includes dataset division, vectorization of text data using TF-IDF, initialization and training of the KNN model, and evaluation of model performance using metrics such as precision, recall, and f1-score. The results of sentiment analysis show that the majority of people's responses to ChatGPT are positive (74.3%), while 25.7% of responses are negative. Performance testing of the KNN model shows that the highest accuracy of 88% is achieved when the k value is 5. Evaluation of model performance also shows satisfactory levels of precision, recall and f1-score. Based on the research results, it was concluded that sentiment analysis and classification using KNN were effective in understanding people's responses to ChatGPT
Implementation of the Naive Bayes Algorithm for Death Due to Heart Failure Using Rapid Miner Surojudin, Nurhadi; Ermanto, Ermanto; Danny, Muhtajuddin; Pratama, Suria
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.4136

Abstract

Until now there is no treatment that can specifically treat heart failure problems. Heart failure treatment only functions to control symptoms, improve quality of life so that patients can carry out normal activities, and reduce the risk of complications due to heart failure such as heart rhythm disturbances, kidney and lung function disorders, stroke, and sudden death. Heart failure is a condition when the heart pump weakens so that it is unable to circulate sufficient blood throughout the body. This condition is also called congestive heart failure. Until now there is no treatment that can specifically treat heart failure problems. This research is a descriptive study which aims to describe the condition of heart failure. By using classification techniques in data mining on data from patients suffering from heart failure using the Naive Bayes algorithm. By using the Rapid Miner tool, data processing is based on the dataset, using classification techniques and data mining stages to classify data on patients suffering from heart failure. By using the Rapid Miner tool, the data processing that will be used as a data collection in this research is collected into 90% training data and 10% testing data. The research results showed an accuracy rate of 80.00%, precision of 66.67% and recall of 100.00%. Based on the research that has been conducted, it is concluded that classification techniques using the Naive Bayes algorithm can be used to determine the potential for life and death in heart failure sufferers.
Co-Authors . Ermanto Arwan Sulaeman, Asep Butsianto, Sufajar Muhtajuddin Danny Surojudin, Nurhadi