Analisis Sentimen Terhadap Layanan Indihome Berdasarkan Twitter Dengan Metode Klasifikasi Support Vector Machine (SVM)

 Rian Tineges (Universitas Nasional, Jakarta, Indonesia)
 (*)Agung Triayudi Mail (Universitas Nasional, Jakarta, Indonesia)
 Ira Diana Sholihati (Universitas Nasional, Jakarta, Indonesia)

(*) Corresponding Author

DOI: http://dx.doi.org/10.30865/mib.v4i3.2181

Abstract

In the year 2018, 18.9% of the population in Indonesia mentioned that the main reason for their use of the Internet is social media. One of the social media with an active user of 6.43 million users is Twitter. Based on the surge of information published via Twitter, it is possible that such information may contain the user's opinions on an object, such objects may be events around the community such as a product or service. This makes the company use Twitter as a medium to disseminate information. An example is an Internet Service Provider (ISP) such as Indihome. Through Twitter, users can discuss each other's complaints or satisfaction with Indihome's services. It takes a method of sentiment analysis to understand whether the textual data includes negative opinions or positive opinions. Thus, the authors use the Support Vector Machine (SVM) method in sentiment analysis on the opinions of the Indihome service user on Twitter, with the aim of obtaining a sentiment classification model using SVM, and to know how much accuracy the SVM method generates, which is applied to sentiment analysis, and to see how satisfied the Indihome service users are based on Twitter. After testing with SVM method The result is accuracy 87%, precision 86%, recall 95%, error rate 13%, and F1-score 90%

Keywords


Analysis, Sentiment, Opinions, Twitter, Indihome, SVM

Full Text:

PDF


Article Metrics

Abstract view : 269 times
PDF - 137 times

References

Asosiasi Penyelenggara Jasa Internet Indonesia [APJII], “Penetrasi & Profil Perilaku Pengguna Internet Indonesia: Asosiasi Penyelenggara Jasa Internet Indonesia,” 2018.

H. – W. A. Social, “Social Media Advertising Audiances.” 2019.

I. M. B. S. Darma, R. S. Perdana, and Indriati, “Penerapan Sentimen Analisis Acara Televisi Pada Twitter Menggunakan Support Vector Machine dan Algoritma Genetika sebagai Metode Seleksi Fitur,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 3, pp. 998–1007, 2018, [Online]. Available: http://j-ptiik.ub.ac.id.

I. P. Windasari, F. N. Uzzi, and K. I. Satoto, “Sentiment Analysis on Twitter Posts: An analysis of Positive or Negative Opinion on GoJek,” Int. Conf. Inf. Tech, Comput. Electr. Eng., pp. 266–269, 2017.

M. Ahmad, S. Aflab, and I. Ali, “Sentiment Analysis of Tweets using SVM,” vol. 177, no. 5, pp. 25–29, 2017.

I. D. Sholihati, Irmawati, and D. Glory, “Aplikasi Data Mining Berbasis Web Menggunakan Algoritma Apriori untuk Data Penjualan di Apotek,” Semin. Nas. Teknol. Informasi, Komun. dan Apl., vol. 4, no. 2, pp. 121–126, 2019.

M. Cindo, D. P. Rini, and Ermatita, “Studi Komparatif Metode Ekstraksi Fitur pada Analisis Sentimen Maskapai Penerbangan Menggunakan Support Vector Machine dan Maximum Entropy,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 3, no. 3, pp. 402–407, 2019.

U. Rofiqoh, R. S. Perdana, and M. A. Fauzi, “Analisis Sentimen Tingkat Kepuasan Pengguna Penyedia Layanan Telekomunikasi Seluler Indonesia Pada Twitter Dengan Metode Support Vector Machine dan Lexicon Based Features,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 1, no. 12, pp. 1725–1732, 2017.

A. Triayudi, “Convolutional Neural Network For Test Classification On Twitter,” J. Softw. Eng. Intellident Syst., vol. 4, no. 3, pp. 123–131, 2019.

I. Santoso, W. Gata, and A. B. Paryanti, “Penggunaan Feature Selection di Algoritma Support Vector Machine untuk Senitimen Analisis Komisi Pemilihan Umum,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 1, no. 1, pp. 364–370, 2019.

S. Y. Pangestu, Y. Astuti, and L. D. Farida, “Algoritma Support Vector Machine Untuk Klasifikasi Sikap Politik Terhadap Partai Politik Indonesia,” J. Mantik Penusa, vol. 3, no. 1, pp. 236–241, 2019.

A. M. Pravina, I. Cholissodin, and P. P. Adikara, “Analisis Sentimen Tentang Opini Maskapai Penerbangan pada Dokumen Twitter Menggunakan Algoritme Support Vector Machine (SVM),” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 3, no. 3, pp. 2789–2797, 2019.

F. Alvianda and P. P. Adikara, “Analisis Sentimen Konten Radikal Di Media Sosial Twitter Menggunakan Metode Support Vector Machine ( SVM ),” J. Pengemb. Teknol. Inf. dan Ilmu Komput. Univ. Brawijaya, vol. 3, no. 1, pp. 241–246, 2019.

H. S. Utama, D. Rosiyadi, B. S. Prakoso, and D. Ariadarma, “Analisis Sentimen Sistem Ganjil Genap di Tol Bekasi Menggunakan,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 1, no. 10, pp. 2–8, 2019.

B. W. Sari and F. F. Haranto, “Implementasi Support Vector Machine Untuk Analisis Sentimen Pengguna Twitter Terhadap Pelayanan Telkom Dan Biznet,” J. Pilar Nusa Mandiri, vol. 15, no. 2, pp. 171–176, 2019, doi: 10.33480/pilar.v15i2.699.

A. Rahmansyah, O. Dewi, P. Andini, T. Hastuti, P. Ningrum, and M. E. Suryana, “Membandingkan Pengaruh Feature Selection Terhadap Algoritma Naïve Bayes dan Support Vector Machine,” Semin. Nas. Apl. Teknol. Inf., pp. 1–7, 2018.

Y. Al-Amrani, M. Lazaar, and K. E. El Kadiri, “Sentiment Analysis Using Hybrid Method Of Support Vector Machine And Decision Tree,” J. Theor. adn Appl. Inf. Technol., vol. 96, no. 7, pp. 1886–1895, 2018.

K. A, Support Vector Machines Succinctly. 2017.

S. Rani and J. Singh, “Sentiment Analysis Of Tweets Using Support Vector Machine,” Int. J. Comput. Sci. Mob. Appl., vol. 5, no. 10, pp. 83–91, 2017.

Bila bermanfaat silahkan share artikel ini

Berikan Komentar Anda terhadap artikel Analisis Sentimen Terhadap Layanan Indihome Berdasarkan Twitter Dengan Metode Klasifikasi Support Vector Machine (SVM)

Refbacks

  • There are currently no refbacks.


Copyright (c) 2020 JURNAL MEDIA INFORMATIKA BUDIDARMA

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.



JURNAL MEDIA INFORMATIKA BUDIDARMA
STMIK Budi Darma
Sekretariat : Jln. Sisingamangaraja No. 338 Telp 061-7875998
email : mib.stmikbd@gmail.com


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.