Muhammad Rizki, M. Hidayattullah, Dwi Intan Af’idah
{"title":"使用实时LSTM网站对推特上反对2024年印尼总统来电的公众意见进行分类","authors":"Muhammad Rizki, M. Hidayattullah, Dwi Intan Af’idah","doi":"10.35970/infotekmesin.v14i2.1908","DOIUrl":null,"url":null,"abstract":"The analysis of public opinions from Indonesian netizens regarding the potential presidential candidates for Indonesia in 2024 on Twitter is challenging. Human-based classification of the candidates on Twitter has limitations as it requires expertise and a considerable amount of time to process the data. Therefore, a system that provides realtime visualization of public opinion classification is necessary. Previous research only focused on model evaluation, while this study aims to implement the best model on a website. The objective of this research is to develop a system for monitoring the Twitter-based public opinion classification of the potential presidential candidates for Indonesia in 2024 within specific time frames. The training process utilizes the LSTM method, resulting in a model with an accuracy of 76%. Parameters such as batch size, dropout, and learning rate were tested. The data used in this study was obtained by crawling Twitter using the keywords Ganjar Pranowo, Anies Baswedan, and Prabowo Subianto. The LSTM model was then implemented in a website-based system that generates a dashboard with features such as a color-coded map displaying the highest levels of positive sentiment for each candidate in each province, the overall classification count for each candidate, and filters for sentiment classification based on province and specific time frames. ","PeriodicalId":33598,"journal":{"name":"Infotekmesin Media Komunikasi Ilmiah Politeknik Cilacap","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Klasifikasi Opini Publik di Twitter Terhadap Bakal Calon Presiden Indonesia Tahun 2024 Menggunakan LSTM Secara Realtime Berbasis Website\",\"authors\":\"Muhammad Rizki, M. Hidayattullah, Dwi Intan Af’idah\",\"doi\":\"10.35970/infotekmesin.v14i2.1908\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The analysis of public opinions from Indonesian netizens regarding the potential presidential candidates for Indonesia in 2024 on Twitter is challenging. Human-based classification of the candidates on Twitter has limitations as it requires expertise and a considerable amount of time to process the data. Therefore, a system that provides realtime visualization of public opinion classification is necessary. Previous research only focused on model evaluation, while this study aims to implement the best model on a website. The objective of this research is to develop a system for monitoring the Twitter-based public opinion classification of the potential presidential candidates for Indonesia in 2024 within specific time frames. The training process utilizes the LSTM method, resulting in a model with an accuracy of 76%. Parameters such as batch size, dropout, and learning rate were tested. The data used in this study was obtained by crawling Twitter using the keywords Ganjar Pranowo, Anies Baswedan, and Prabowo Subianto. The LSTM model was then implemented in a website-based system that generates a dashboard with features such as a color-coded map displaying the highest levels of positive sentiment for each candidate in each province, the overall classification count for each candidate, and filters for sentiment classification based on province and specific time frames. \",\"PeriodicalId\":33598,\"journal\":{\"name\":\"Infotekmesin Media Komunikasi Ilmiah Politeknik Cilacap\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-07-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Infotekmesin Media Komunikasi Ilmiah Politeknik Cilacap\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.35970/infotekmesin.v14i2.1908\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Infotekmesin Media Komunikasi Ilmiah Politeknik Cilacap","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.35970/infotekmesin.v14i2.1908","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Klasifikasi Opini Publik di Twitter Terhadap Bakal Calon Presiden Indonesia Tahun 2024 Menggunakan LSTM Secara Realtime Berbasis Website
The analysis of public opinions from Indonesian netizens regarding the potential presidential candidates for Indonesia in 2024 on Twitter is challenging. Human-based classification of the candidates on Twitter has limitations as it requires expertise and a considerable amount of time to process the data. Therefore, a system that provides realtime visualization of public opinion classification is necessary. Previous research only focused on model evaluation, while this study aims to implement the best model on a website. The objective of this research is to develop a system for monitoring the Twitter-based public opinion classification of the potential presidential candidates for Indonesia in 2024 within specific time frames. The training process utilizes the LSTM method, resulting in a model with an accuracy of 76%. Parameters such as batch size, dropout, and learning rate were tested. The data used in this study was obtained by crawling Twitter using the keywords Ganjar Pranowo, Anies Baswedan, and Prabowo Subianto. The LSTM model was then implemented in a website-based system that generates a dashboard with features such as a color-coded map displaying the highest levels of positive sentiment for each candidate in each province, the overall classification count for each candidate, and filters for sentiment classification based on province and specific time frames.