SENTIMENT ANALYSIS OF COMMENTS ON GOOGLE PLAY STORE, TWITTER AND YOUTUBE TO THE MYPERTAMINA APPLICATION WITH SUPPORT VECTOR MACHINE

Aji Nursalim; Rice Novita

doi:10.52436/1.jutif.2023.4.6.1059

Aji Nursalim Information Systems Study Program, Faculty of Science and Technology, UIN Suska Riau, Indonesia
Rice Novita Information Systems Study Program, Faculty of Science and Technology, UIN Suska Riau, Indonesia

DOI: https://doi.org/10.52436/1.jutif.2023.4.6.1059

Keywords: application, mypertamina, sentimen, SVM

Abstract

Application is an important requirement in a business because it makes work more efficient thereby increasing the results of the company, pertamina as a supplier of fuel oil (BBM) in Indonesia provides the latest innovations by launching the mypertamina application for purchasing BBM which raises public opinion, and conveys its aspirations in social media. Text mining is a way to group community comments because text mining has an analysis that focuses on analyzing a comment that is extracted into information. The purpose of this study was to determine public sentiment towards the use of mypertamina by classifying comments using the Support Vector Machine (SVM) algorithm and finding the best kernel among linear, polynomial and RBF. In this study, data was taken from three social media, namely Google Play Store with 18.000 data, Twitter with 20.000 data and YouTube with 6.400 data with a total of 44.400 data. Sentiment is carried out by giving positive and negative classes, the accuracy obtained from sentiment is carried out for Google Play Store data of 95%, Twitter 76% and YouTube 99% and it is known that the best svm kernel in this study is the RBF kernel which outperforms the linear and polynomial kernels.

Downloads

Download data is not yet available.

References

G. R. Hrp and N. Aslami, “Analisis Dampak Kebijakan Perubahan Publik Harga BBM terhadap Perekonomian Rakyat Indonesia,” J. Ilmu Komputer, Ekon. dan Manaj., vol. 2, no. 1, pp. 1464–1474, 2022.

R. I. Liperda, I. K. Hardianti, I. N. Widyah, A. Rahmadini, N. A. Fadjri, and R. R. Agustin, “Simulasi-Optimasi Sistem Transportasi Penentuan Kebutuhan Truk Tangki Pada Proses Distribusi BBM: Studi Kasus TBBM Plumpang,” JISI J. Integr. Sist. Ind., vol. 9, no. 2, p. 92, 2022, doi: 10.24853/jisi.9.2.92-102.

A. A. Writings, C. Hendriyani, and F. Damayanti, “MyPertamina Application To Increase Consumer Engagement,” Int. J. Bus. Rev. …, vol. 5, no. 1, pp. 65–73, 2022, [Online]. Available: https://ejournal.upi.edu/index.php/thejobsreview/article/view/48470

R. M. Ibrahim and N. N. K. Moeliono, “Pengaruh manfaat, kepercayaan, efikasi diri, kemudahan penggunaan, keamanan terhadap persepsi konsumen pada my pertamina (Studi pada penggunaan my pertamina kota Bandung,” J. Ilm. Mhs. Ekon. Manaj. Accredit. SINTA, vol. 4, no. 2, pp. 396–413, 2020, [Online]. Available: http:jim.unsyiah.ac.id/ekm

G. S. Heryadi, “Strategi Promosi Loyality Program My Pertamina yang Dilakukan Oleh PT Pertamina (PERSERO) Tahun 2018,” Visi Komun., vol. 17, no. 01, pp. 23–44, 2018.

A. A. Sinurat, C. Hendriyani, and F. Damayanti, “MyPertamina Application To Increase Consumer Engagement,” Int. J. Bus. Rev. (The Jobs Rev., vol. 5, no. 1, pp. 65–74, 2022, doi: 10.17509/tjr.v5i1.48470.

A. W. Syaputri, “Analisis Sentimen Pada Ulasan Hotel Grand Elite Di Website Traveloka Menggunakan Algoritma K-Nearest Neighbor,” J. Ekon. Vol. 18, Nomor 1 Maret201, vol. 2, no. 1, pp. 41–49, 2020.

C. Dreisbach, T. A. Koleck, P. E. Bourne, and S. Bakken, “A systematic review of natural language processing and text mining of symptoms from electronic patient-authored text data,” Int. J. Med. Inform., vol. 125, no. February, pp. 37–46, 2019, doi: 10.1016/j.ijmedinf.2019.02.008.

F. Ratnawati, “Implementasi Algoritma Naive Bayes Terhadap Analisis Sentimen Opini Film Pada Twitter,” INOVTEK Polbeng - Seri Inform., vol. 3, no. 1, p. 50, 2018, doi: 10.35314/isi.v3i1.335.

V. A. Flores, L. Jasa, and L. Linawati, “Analisis Sentimen untuk Mengetahui Kelemahan dan Kelebihan Pesaing Bisnis Rumah Makan Berdasarkan Komentar Positif dan Negatif di Instagram,” Maj. Ilm. Teknol. Elektro, vol. 19, no. 1, p. 49, 2020, doi: 10.24843/mite.2020.v19i01.p07.

A. M. Zuhdi, E. Utami, and S. Raharjo, “Analisis Sentiment Twitter Terhadap Capres Indonesia 2019 dengan Metode K-NN,” J. Inf. Politek. Indones. Surakarta, vol. 5, no. 2, pp. 1–7, 2019.

E. Retnoningsih and R. Pramudita, “Mengenal Machine Learning Dengan Teknik Supervised Dan Unsupervised Learning Menggunakan Python,” Bina Insa. Ict J., vol. 7, no. 2, p. 156, 2020, doi: 10.51211/biict.v7i2.1422.

P. P. P. A. N. . F. I. R.H. Zer, B. H. Hayadi, and A. R. Damanik, “Pendekatan Machine Learning Menggunakan Algoritma C4.5 Berbasis Pso Dalam Analisa Pemahaman Pemrograman Website,” J. Inform. dan Tek. Elektro Terap., vol. 10, no. 3, 2022, doi: 10.23960/jitet.v10i3.2700.

D. A. Otchere, T. O. Arbi Ganat, R. Gholami, and S. Ridha, “Application of supervised machine learning paradigms in the prediction of petroleum reservoir properties: Comparative analysis of ANN and SVM models,” J. Pet. Sci. Eng., vol. 200, no. August 2020, p. 108182, 2021, doi: 10.1016/j.petrol.2020.108182.

K. A. Rokhman, B. Berlilana, and P. Arsi, “Perbandingan Metode Support Vector Machine Dan Decision Tree Untuk Analisis Sentimen Review Komentar Pada Aplikasi Transportasi Online,” J. Inf. Syst. Manag., vol. 3, no. 1, pp. 1–7, 2021, doi: 10.24076/joism.2021v3i1.341.

M. Ichwan, I. A. Dewi, and Z. M. S, “Klasifikasi Support Vector Machine (SVM) Untuk Menentukan TingkatKemanisan Mangga Berdasarkan Fitur Warna,” MIND J., vol. 3, no. 2, pp. 16–23, 2019, doi: 10.26760/mindjournal.v3i2.16-23.

D. L. Rianti, Y. Umaidah, and A. Voutama, “Tren Marketplace Berdasarkan Klasifikasi Ulasan Pelanggan Menggunakan Perbandingan Kernel Support Vector Machine,” STRING (Satuan Tulisan Ris. dan Inov. Teknol., vol. 6, no. 1, p. 98, 2021, doi: 10.30998/string.v6i1.9993.

N. R. Feta and A. R. Ginanjar, “Komparasi Fungsi Kernel Metode Support Vector Machine Untuk Pemodelan Klasifikasi Terhadap Comparison of the Kernel Function of Support Vector Machine Method for Modeling Classification of Soybean Plat Disease,” J. Ilm. Ilmu Komputer, Sains dan Teknol. Terap., vol. 1, no. 1, pp. 33–39, 2019.

A. Liani, “Analisis Perbandingan Kernel Algoritma Support Vector Machine dalam Mengklasifikasikan Skripsi Teknik Informatika berdasarkan Abstrak,” JOINS (Journal Inf. Syst., vol. 5, no. 2, pp. 240–249, 2020, doi: 10.33633/joins.v5i2.3715.

T. M. Permata Aulia, N. Arifin, and R. Mayasari, “Perbandingan Kernel Support Vector Machine (Svm) Dalam Penerapan Analisis Sentimen Vaksinisasi Covid-19,” SINTECH (Science Inf. Technol. J., vol. 4, no. 2, pp. 139–145, 2021, doi: 10.31598/sintechjournal.v4i2.762.

M. Pejic-Bach, T. Bertoncel, M. Meško, and Ž. Krstić, “Text mining of industry 4.0 job advertisements,” Int. J. Inf. Manage., vol. 50, no. July, pp. 416–431, 2020, doi: 10.1016/j.ijinfomgt.2019.07.014.

W. A. Luqyana, I. Cholissodin, and R. S. Perdana, “Analisis Sentimen Cyberbullying pada Komentar Instagram dengan Metode Klasifikasi Support Vector Machine,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 11, pp. 4704–4713, 2018, [Online]. Available: http://j-ptiik.ub.ac.id

Y. Al-amrani, M. Lazaar, and K. Eddine, “Sentiment Analysis Using Hybrid Method of,” J. Theor. Appl. Inf. Technol., vol. 96, no. 7, pp. 1886–1895, 2018, [Online]. Available: www.jatit.org

S. Sumayah, F. Sembiring, and W. Jatmiko, “Analysis of Sentiment of Indonesian Community on Metaverse Using Support Vector Machine Algorithm,” J. Tek. Inform., vol. 4, no. 1, pp. 143–150, 2023.

S. Mulyani, R. Novita, P. Studi, and S. Informasi, “IMPLEMENTATION OF THE NAIVE BAYES CLASSIFIER ALGORITHM FOR CLASSIFICATION OF COMMUNITY SENTIMENT ABOUT DEPRESSION ON IMPLEMENTASI ALGORITMA NAIVE BAYES CLASSIFIER UNTUK,” vol. 3, no. 5, 2022.

B. Herwijayanti, D. E. Ratnawati, and L. Muflikhah, “Klasifikasi Berita Online dengan menggunakan Pembobotan TF-IDF dan Cosine Similarity,” Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 1, pp. 306–312, 2018, [Online]. Available: https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/796

W. S. U. Saragih, N. A. Hasibuan, and ..., “Penerapan Text Mining Dengan Menggunakan Metode TF-IDF Untuk Menentukan Genre Dari Komik,” KOMIK (Konferensi …, vol. 4, pp. 191–199, 2020, doi: 10.30865/komik.v4i1.2679.