Muhammad Fadilah Dzukaidah, . (2022) ANALISIS SENTIMEN PROGRAM BANTUAN SOSIAL TUNAI PADA SOSIAL MEDIA TWITTER MENGGUNAKAN ALGORTIMA SUPPORT VECTOR MACHINE. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.
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Abstract
At this time the growth of online media as a communication tool has been very rapid, the various kinds of information and content available are the main attraction for its users. In addition to various information, online media can also be a means used to convey issues, criticisms, suggestions, and public opinions. One of the online media is Twitter. In this application, tweets are characterized into two classes of sentiment, which is a class of positive sentiment and negative sentiment. The calculation in this opinion is Support Vector Machine (SVM), this calculation is used to process the order of feelings in tweets. Information is obtained by utilizing the Application Programming Interface (API) provided by Twitter. The data obtained are 237 tweets as training data and 60 as test data in order to get test results with the yahoo Support Vector Machine of 88.33% accuracy, 91.37% precision and 96.36% recall value. The magnitude of accuracy indicates that the Support Vector Mahine algorithm can be used in the classification of the Cash Social Assistance Program. Kata Kunci : Classification, Support Vector Machine, Tweet
| Item Type: | Thesis (Skripsi) |
|---|---|
| Additional Information: | [No.Panggil: 1810511051] [Pembimbing 1: Ermatita] [Pembimbing 2: Desta Sandya Prasvita] [Penguji 1: Jayanta] [Penguji 2: Bambang Triwahyono] |
| Uncontrolled Keywords: | At this time the growth of online media as a communication tool has been very rapid, the various kinds of information and content available are the main attraction for its users. In addition to various information, online media can also be a means used to convey issues, criticisms, suggestions, and public opinions. One of the online media is Twitter. In this application, tweets are characterized into two classes of sentiment, which is a class of positive sentiment and negative sentiment. The calculation in this opinion is Support Vector Machine (SVM), this calculation is used to process the order of feelings in tweets. Information is obtained by utilizing Twitter's Application Programming Interface (API). The data obtained are 237 tweets as training data and 60 as test data to get test results with the yahoo Support Vector Machine of 88.33% accuracy, 91.37% precision, and 96.36% recall value. The magnitude of accuracy indicates that the Support Vector Machine algorithm can be used in the classification of to classy the Cash Social Assistance Program. Kata Kunci: Classification, Support Vector Machine, Tweet |
| Subjects: | Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Fakultas Ilmu Komputer > Program Studi Informatika (S1) |
| Depositing User: | Muhammad Fadilah Dzukaidah |
| Date Deposited: | 04 Aug 2022 03:14 |
| Last Modified: | 04 Aug 2022 03:14 |
| URI: | http://repository.upnvj.ac.id/id/eprint/19846 |
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