Muhamad Hanif Razka, . (2022) ANALISIS SENTIMEN TERHADAP APLIKASI PEDULI LINDUNGI PADA JEJARING SOSIAL TWITTER MENGGUNAKAN ALGORITMA NAÏVE BAYES DAN SELEKSI FITUR PARTICLE SWARM OPTIMIZATION. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.
![[img]](http://repository.upnvj.ac.id/style/images/fileicons/text.png) |
Text
ABSTRAK.pdf Download (226kB) |
|
Text
AWAL.pdf Download (829kB) |
|
Text
BAB 1.pdf Download (359kB) |
|
Text
BAB 2.pdf Restricted to Repository UPNVJ Only Download (550kB) |
|
Text
BAB 3.pdf Restricted to Repository UPNVJ Only Download (560kB) |
|
Text
BAB 4.pdf Restricted to Repository UPNVJ Only Download (1MB) |
|
Text
BAB 5.pdf Download (229kB) |
|
Text
DAFTAR PUSTAKA.pdf Download (470kB) |
|
Text
RIWAYAT HIDUP.pdf Restricted to Repository UPNVJ Only Download (234kB) |
|
Text
LAMPIRAN.pdf Restricted to Repository UPNVJ Only Download (1MB) |
|
Text
Hasil Plagiarisme.pdf Restricted to Repository staff only Download (12MB) |
|
Text
Artikel KI.pdf Restricted to Repository staff only Download (873kB) |
Abstract
The concern protecting application is an official comprehensive application in collaboration with several other ministries. The application is intended to help prevent the spread of the covid-19 virus because it is constantly linked to its user by location. The study USES the public opinion data against the use of the care application of the tweets by people using key words such as protection, hashtags # protect and user username @plprotect. Data retrieval takes place March 13 to April 11, 2022. In this study the goal of categorizing a data of a tweet to a positive and negative sentiment and using a naive bayes algorithm to assess it and then apply the usage of personalizing personization of selection features by artificially matrix to further account for the accuracy of using the selection feature for the classification algorithm. And from the test results using the naive bayes classification algorithm, I was able to maintain a fixed rate of 76.78%, recall of 78%, and a precission of 79.62%. While the use of information regarding the swarm metrics optimization on algorithm naive gets the best results on pso's iteration process 250 times with an increased value of accuracy to 80.19% then recall value to 85.71% and also for precission to 80%.
| Item Type: | Thesis (Skripsi) |
|---|---|
| Additional Information: | [No.Panggil: 1810511119] [Pembimbing 1: Theresiawati] [Pembimbing 2: Nurul Chamidah] [Penguji 1: Ermatita] [Penguji 2: Mayanda Mega Santoni] |
| Uncontrolled Keywords: | PeduliLindungi, Sentiment Analyzer, Twitter, Naïve Bayes, Particle Swarm Optimization. |
| Subjects: | Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General) |
| Divisions: | Fakultas Ilmu Komputer > Program Studi Informatika (S1) |
| Depositing User: | Muhamad Hanif Razka |
| Date Deposited: | 01 Aug 2022 03:28 |
| Last Modified: | 01 Aug 2022 03:28 |
| URI: | http://repository.upnvj.ac.id/id/eprint/19792 |
Actions (login required)
 |
View Item |