Analisis Sentiment pada Sosial Media Twitter Menggunakan NaÑ—ve Bayes Classifier dengan Feature Selection Particle Swarm Optimization dan Term Frequency

Authors

  • Yono Cahyono Universitas Pamulang

DOI:

https://doi.org/10.32493/informatika.v2i1.1500

Keywords:

Analisis Sentiment, Twitter, NBC, Term frequency, PSO

Abstract

Pengguna media sosial saat ini sangat besar; dimana setiap orang mengungkapkan pendapat; komentar; kritik dan lain-lain. Data tersebut memberikan informasi yang berharga untuk dapat membantu orang atau organisasi dalam pengambilan keputusan. Jumlah data yang sangat besar tidak mungkin bagi manusia untuk membaca dan menganalisis secara manual. Ansalisis Sentiment merupakan proses dalam menganalisis; memahami; dan mengklasifikasi pendapat; evaluasi; penilaian; sikap; dan emosi terhadap suatu entitas tertentu seperti produk; jasa; organisasi; individu; peristiwa; topik; guna mendapatkan informasi. Penelitian ini bertujuan untuk memisahkan tweets berbahasa Indonesia pada media sosial twitter kedalam kategori positif; negatif dan netral. Metode naїve bayes Classifier (NBC) dengan feature selection Particle Swarm Optimization (PSO) diterapkan pada dataset untuk mengurangi atribut yang kurang relevan pada saat proses klasifikasi. Hasil pengujian menunjukan bahwa algoritma Naïve Bayes Classifier dengan feature selection Particle Swarm Optimization (PSO) menggunakan parameter term frequency (TF) dengan akurasi 97;48%.
Jurnal Informatika Universitas Pamulang Vol. 2 No. 1 Maret 2017

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Published

2017-03-25

Issue

Vol. 2 No. 1 (2017): JURNAL INFORMATIKA UNIVERSITAS PAMULANG

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Article

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