Kurniawan Danil, . (2024) ANALISIS SENTIMEN OPINI PENGGUNA MEDIA SOSIAL X TERHADAP PEMILIHAN UMUM TAHUN 2024 MENGGUNAKAN METODE NAIVE BAYES DAN PARTICLE SWARM OPTIMIZATION. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.
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Abstract
The Indonesian people have carried out routine five-year activities, namely General Elections (Pemilu) which have been held in 2024. On social media X, there are many public opinions about the implementation of this activity, some are positive and negative. The collection of public opinion will then be analyzed based on its sentiment to be used in model development. This analysis aims to determine the composition of public sentiment towards the 2024 General Election and measure the model's ability to classify the data. This is useful for knowing the public's acceptance of the election and knowing the performance of the Naive Bayes model together with Particle Swarm Optimization in handling the opinion data. Before the development of the model, the data is first labeled with sentiment, preprocessing, data division, and word weighting using TF-IDF. The results of this study are the majority of people or as many as 77% of X social media users have positive sentiments towards the implementation of the General Election (Election), and data classification using the Naive Bayes model together with Particle Swarm Optimization has good performance with an accuracy value of 83.9%.
| Item Type: | Thesis (Skripsi) |
|---|---|
| Additional Information: | [No.Panggil: 2010511077] [Pembimbing: Musthofa Galih Pradana] [Penguji 1: Theresia Wati] [Penguji 2: Zatin Niqotaini] |
| Uncontrolled Keywords: | Election; Media Social; Naive 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 |
| Divisions: | Fakultas Ilmu Komputer > Program Studi Informatika (S1) |
| Depositing User: | KURNIAWAN DANIL |
| Date Deposited: | 05 Sep 2024 06:36 |
| Last Modified: | 05 Sep 2024 06:36 |
| URI: | http://repository.upnvj.ac.id/id/eprint/30785 |
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