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All Journal J-SAKTI (Jurnal Sains Komputer dan Informatika) J-SAKTI (Jurnal Sains Komputer dan Informatika)
Sunanto, Anto
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Energy

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Optimasi Sentimen Analisis Informatif dan Tidak Informatif dari Tweet di BMKG Menggunakan Algoritma Naive Bayes dan Metode Teknik Pengambilan Sampel Minoritas Sintetis Hidayatulloh, Muhammad Yusuf; Sunanto, Anto; Armansyah, A; Gevin, Muhammad Farrell Afelino; Saputra, Dedi Dwi
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 1 (2023): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i1.565

Abstract

The emergence of computer-based and mobile-based social networks seems to have received high attention from the public. Evidenced by the increasing number of social networks that appear. Friendster, Facebook, Twitter, Linkd In and many others. Twitter is one of the social media used to find information, Twitter users generally report every activity. They are even more helped by the existence of increasingly sophisticated cellphones. The system created in this study to optimize the analysis of informative and uninformative sentiment using a rapid miner application with the Naïve Bayes, Naïve Bayes + Adaboost, SVM, and SVM PSO methods using data taken from twitter @infoBMKG. The research method used is the collection of tweet data from twitter taken by the Crawling method. The data taken is tweets in Indonesian with a total of 1,000 tweets from the @infoBMKG twitter account. The results of the nave Bayes algorithm test carried out in this study were to measure the performance of accuracy, precision, recall, AUC from the results of the training and submission of datasets that had gone through the data preprocessing process. From the results of the research that has been done, it is proven that the optimization of informative and uninformative sentiment analysis from tweets on BMKG's twitter gets good results using the Support Machine Vector method with higher Accuracy, Recall, and AUC values than other methods.
Optimasi Sentimen Analisis Informatif dan Tidak Informatif dari Tweet di BMKG Menggunakan Algoritma Naive Bayes dan Metode Teknik Pengambilan Sampel Minoritas Sintetis Hidayatulloh, Muhammad Yusuf; Sunanto, Anto; Armansyah, A; Gevin, Muhammad Farrell Afelino; Saputra, Dedi Dwi
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 1 (2023): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i1.565

Abstract

The emergence of computer-based and mobile-based social networks seems to have received high attention from the public. Evidenced by the increasing number of social networks that appear. Friendster, Facebook, Twitter, Linkd In and many others. Twitter is one of the social media used to find information, Twitter users generally report every activity. They are even more helped by the existence of increasingly sophisticated cellphones. The system created in this study to optimize the analysis of informative and uninformative sentiment using a rapid miner application with the Naïve Bayes, Naïve Bayes + Adaboost, SVM, and SVM PSO methods using data taken from twitter @infoBMKG. The research method used is the collection of tweet data from twitter taken by the Crawling method. The data taken is tweets in Indonesian with a total of 1,000 tweets from the @infoBMKG twitter account. The results of the nave Bayes algorithm test carried out in this study were to measure the performance of accuracy, precision, recall, AUC from the results of the training and submission of datasets that had gone through the data preprocessing process. From the results of the research that has been done, it is proven that the optimization of informative and uninformative sentiment analysis from tweets on BMKG's twitter gets good results using the Support Machine Vector method with higher Accuracy, Recall, and AUC values than other methods.
Co-Authors Armansyah, A Gevin, Muhammad Farrell Afelino Hidayatulloh, Muhammad Yusuf Saputra, Dedi Dwi