Implementasi Text-Mining untuk Analisis Sentimen pada Twitter dengan Algoritma Support Vector Machine

Authors

  • Aditiya Hermawan Universitas Buddhi Dharma
  • Indrico Jowensen Universitas Buddhi Dharma
  • Junaedi Junaedi Universitas Buddhi Dharma
  • Edy Universitas Buddhi Dharma

DOI:

https://doi.org/10.23887/jstundiksha.v12i1.52358

Keywords:

Sentiment Analysis, Support Vector Machine, Twitter

Abstract

Setiap tahun, jumlah orang yang menggunakan media sosial bertambah seiring dengan jumlah orang yang menggunakan internet. Peningkatan tersebut diiringi dengan meningkatnya informasi pada internet yang tentunya informasi tersebut mempunyai nilai jika dilakukan analisa. Untuk menganalisa data dalam jumlah besar dapat menggunakan teknik text mining. Text mining mampu memproses untuk memperoleh informasi berkualitas tinggi dari teks. Text mining juga dapat digunakan untuk menganalisa informasi seperti sentimen dari sebuah kalimat dengan sangat cepat untuk memudahkan dalam mendapatkan informasi yang berkualitas. Informasi diproses berasal dari media sosial berbasis text yaitu twitter yang mana pengambilan data dilakukan dengan bantuan Application Programming Interface dan menggunakan kata kunci berupa sebuah kata atau hashtag. Kalimat tersebut akan dilakukan proses text mining dengan menggunakan algoritma Support Vector machine untuk menghasilkan klasifikasi dari sentimen suatu kalimat ke dalam sentiment positif, netral atau negatif. Tingkat akurasi yang dihasilkan oleh proses ini adalah sebesar 73% berdasarkan data sentimen yang dimiliki. Tingkat akurasi dalam melakukan text mining sangat dipengarui pada proses Pre-Processing karena terdapat banyak kata perlu dilakukan pengelolahan lebih lanjut.

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Published

2023-04-18

How to Cite

Hermawan, A., Jowensen, I., Junaedi, J., & Edy. (2023). Implementasi Text-Mining untuk Analisis Sentimen pada Twitter dengan Algoritma Support Vector Machine. JST (Jurnal Sains Dan Teknologi), 12(1), 129–137. https://doi.org/10.23887/jstundiksha.v12i1.52358

Issue

Vol. 12 No. 1 (2023): April

Section

Articles

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Copyright (c) 2023 Aditiya Hermawan, Indrico Jowensen, Junaedi Junaedi; Edy

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