Social Media Sentiment Analysis to Measure Community Response in the Millennial Road Safety Festival Program Using TF-IDF and Support Vector Machine

Analisis Sentimen Media Sosial untuk Mengukur Respon Masyarakat pada Program Millennial Road Safety Festival Menggunakan TF-IDF dan Support Vector Machine

  • Saiful Bukhori Universitas Jember
  • Sonya Sulistyono Universitas Jember
  • Antonius Cahya Prihandoko Universitas Jember
  • Januar Adi Putra Universitas Jember
  • Windi Eka Yulia Retnani Universitas Jember
  • Umroh Makhmudah Universitas Jember
  • Muhammad Noor Dwi Eldianto Universitas Jember

Abstract

This Sentiment Analysis is a combination of data mining and text mining. Sentiment Analysis itself is used to process various opinions that the public or experts have given through a variety of existing media. The argument is given to a product, service, or agency. Sentiment Analysis has three types of opinions: negative opinions, positive opinions, and neutral opinions. Based on the test results, the resulting model achieves the highest accuracy of 83.33% when using 80:20 scenario data, while the lowest accuracy of 80.00% is achieved when using the 60:40 scenario data. The higher the precision that will be obtained, whereas using less training data will be slightly unstable.


ABSTRAK


Sentiment Analysis merupakan perpaduan dari data mining dan teks mining, dimana Sentiment Analysis sendiri digunakan untuk mengolah berbagai macam opini yang telah diberikan oleh masyarakat atau para pakar melalui berbagai media yang ada, opini tersebut diberikan untuk sebuah produk, jasa maupun sebuah instansi. Pada Sentiment Analysis terdapat 3 jenis opini, yaitu opini negatif, opini positif dan opini netral. Berdasarkan hasil pengujian, model yang dihasilkan mencapai akurasi tertinggi yaitu 83,33% saat menggunakan data skenario 80:20, sedangkan akurasi terendah 80,00% dicapai ketika menggunakan skenario data 60:40  Skenario data dapat memengaruhi tingkat akurasi semakin banyak jumlah data pelatihan yang diberikan, semakin tinggi akurasi yang akan diperoleh, sedangkan jika  menggunakan lebih sedikit data pelatihan, hasilnya akan sedikit tidak stabil.

Author Biographies

Saiful Bukhori, Universitas Jember

Technology Information Department
Faculty of Computer Science, Universitas Jember
Jl. Kalimantan 37 Jember, East Java

Sonya Sulistyono, Universitas Jember

Civil Engineering Department
Faculty of Engineering, Universitas Jember
Jl. Kalimantan 37 Jember, East Java

Antonius Cahya Prihandoko, Universitas Jember

Technology Information Department
Faculty of Computer Science, Universitas Jember
Jl. Kalimantan 37 Jember, East Java

Januar Adi Putra, Universitas Jember

Technology Information Department
Faculty of Computer Science, Universitas Jember
Jl. Kalimantan 37 Jember, East Java

Windi Eka Yulia Retnani, Universitas Jember

Informatics Department
Faculty of Computer Science, Universitas Jember
Jl. Kalimantan 37 Jember, East Java

Umroh Makhmudah, Universitas Jember

System Information Department
Faculty of Computer Science, Universitas Jember
Jl. Kalimantan 37 Jember, East Java

Muhammad Noor Dwi Eldianto, Universitas Jember

System Information Department
Faculty of Computer Science, Universitas Jember
Jl. Kalimantan 37 Jember, East Java

Published
2021-04-30
How to Cite
BUKHORI, Saiful et al. Social Media Sentiment Analysis to Measure Community Response in the Millennial Road Safety Festival Program Using TF-IDF and Support Vector Machine. Journal of Indonesia Road Safety, [S.l.], v. 3, n. 2, p. 69-82, apr. 2021. ISSN 2654-9794. Available at: <https://jurnal.unej.ac.id/index.php/KORLANTAS-JIRS/article/view/16768>. Date accessed: 08 july 2022. doi: https://doi.org/10.19184/korlantas-jirs.v3i2.16768.