Klasifikasi Sentiment Review Aplikasi MyPertamina Menggunakan Word Embedding FastText dan SVM (Support Vector Machine)

 Mustasaruddin Mustasaruddin (Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia)
 (*)Elvia Budianita Mail (Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia)
 M Fikry (Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia)
 Febi Yanto (Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia)

(*) Corresponding Author

Submitted: February 7, 2023; Published: March 31, 2023

Abstract

The MyPertamina application is a requirement for buying subsidized fuel oil (BBM), namely pertalite and diesel, the goal is that subsidized (BBM) purchases are right on target. The MyPertamina application has received many ratings and comments from the public, both positive and negative, with these comments and ratings expected to help the government as a benchmark in implementing a program. Therefore, this research aims to assess the MyPertamina application by grouping sentiment classes 90:10, 80:20 and 70:30. In this study, the method used is Fasttext and Support Vector Machine (SVM) to review the MyPertamina application. This research uses 8000 data, the data is grouped into three portions of data, with portions of 90:10, 80:20 and 70:30. The best SVM model was obtained with a data portion of 90:10 with a total of 7200 training data and 800 testing data, obtained 80% accuracy, 50% recall and 84% precision without undersampling. Meanwhile, if the amount of data is balanced (undersampling) with the number of positive data 1325, neutral 1325 and negative 1325, that is, with the benchmark of the lowest data value from the sentiment class, an accuracy of 67% is obtained, recall is 69% and precision is 57%. The highest number of sentiment classes from the 90:10 portion of the data is negative, namely 4300, neutral 1575 and positive 1325, because many users found reviews of the MyPertamina application, namely "after updating the MyPertamina application the bugs are getting worse".

Keywords


Play Store App; MyPertamina; SVM; FastText; Sentiment Analysis

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References

M. Andriani, “Analisis Pengaruh Kualitas Pelayanan Terhadap keputusan Pembelian pada PT.Pertamina (Studi Pada PT.Pertamina Cemara Asri, Medan),” J. Stindo Prof., vol. VI, no. September, pp. 3–7, 2020.

D. Yuliani, S. Saryono, D. Apriani, Maghfiroh, and M. Ro, “Dampak Kenaikan Harga Bahan Bakar Minyak (BBM) Terhadap Sembilan Bahan Pokok (Sembako) Di Kecamatan Tambun Selatan Dalam Masa Pandemi,” J. Citizsh. Virtues, vol. 2, no. 2, pp. 320–326, 2022.

G. Rozy Hrp, N. Aslami, and P. Studi Manajemen Fakultas Ekonomi Bisnis Islam, “Analisis Damfak Kebijakan Perubahan Publik Harga BBM terhadap Perekonomian Rakyat Indonesia,” J. Ilmu Komputer, Ekon. dan Manaj. , vol. 2, no. 1, pp. 1464–1474, 2022.

Y. S. N. Lutfi, Ahmad, Syamsir, Aulia Annisa Fitriani, Ira Ramadani, Nabilah Azahra Putri, “Efekvitas Penggunaan Aplikasi My Pertamina di Era Kenaikan BBM Bersubsidi,” J. Pros. Mateandrau, vol. 1, no. 2, 2022.

A. Surahmat, “RANCANG BANGUN APLIKASI SISTEM PENJUALAN PADA PERCETAKAN CUBIC ART,” Junal JATI (Jurnal Mhs. Tek. Inform., vol. 7, no. 1, pp. 81–86, 2023.

E. O. Safitri, Y. T. Musityo, and N. H. Wardhani, “Analisis Perilaku Penggunaan Mobile Payment Aplikasi OVO menggunakan Technology Acceptance Model (TAM) Termodifikasi,” Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 3, no. 8, pp. 8184–8189, 2019, [Online]. Available: http://j-ptiik.ub.ac.id

A. A. Sinurat, C. Hendriyani, F. Damayanti, A. Sekretari, and M. Taruna, “Aplikasi MyPertamina Untuk Meningkatkan Keterlibatan Konsumen,” J. Int. Tinj. BISNIS, vol. 5, no. 1, pp. 65–73, 2022.

N. K. Hikmawati, “Analisis Kualitas Layanan My Pertamina Menggunakan Pendekatan e-GovQual pada Beberapa Kota Percobaan MyPertamina Service Quality Analysis Using E-GovQual Approach in Several Trial Cities,” J. Manaj. Inform., vol. 12, pp. 100–111, 2022.

“play.google.com,” 2022, [Online]. Available: https://play.google.com/store/apps/details?id=com.dafturn.mypertamina&hl=in&gl=US

V. K. S. Que, A. Iriani, and H. D. Purnomo, “Analisis Sentimen Transportasi Online Menggunakan Support Vector Machine Berbasis Particle Swarm Optimization,” J. Nas. Tek. Elektro dan Teknol. Inf., vol. 9, no. 2, pp. 162–170, 2020, doi: 10.22146/jnteti.v9i2.102.

N. A. F. T. Ardianne luthfika Fairuz, Rima Dias Ramadhani, “Analisis Sentimen Masyarakat Terhadap COVID-19 Pada Media Sosial,” J. Data Sci. IOT, Mach. Learn. Artif. Intell., vol. 1, no. 1, pp. 10–12, 2021.

D. Musfiroh, U. Khaira, P. E. P. Utomo, and T. Suratno, “Analisis Sentimen terhadap Perkuliahan Daring di Indonesia dari Twitter Dataset Menggunakan InSet Lexicon: Sentiment Analysis of Online Lectures in Indonesia from Twitter Dataset Using InSet Lexicon,” MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 1, no. 1, pp. 24–33, 2021.

R. Mahendrajaya, G. A. Buntoro, and M. B. Setyawan, “Analisis Sentimen Cyberbullying pada Komentar Instagram dengan Metode SVM,” J. Tek. Univ. Muhammadiyah Ponorogo, vol. 3, no. 2, pp. 52–63, 2019.

Z. A. A. Nurdin, Bernadus Anggo Seno Aji, Anugrayai Bustamin, “Perbandingan Kinerja Word Embedding Word2vec , Glove ,” J. TEKNOKOMPAK, vol. 14, no. 2, 2020.

T. S. Sabrila, V. R. Sari, and A. E. Minarno, “Analisis Sentimen Pada Tweet Tentang Penanganan Covid - 19 Menggunakan Word Embedding Pada Algoritma Support Vector Machine Dan K - Nearest Neighbor,” Fountain Informatics J., vol. 6, no. 2, 2021.

M. Rizky, Analisis Sentimen Masyarakat Terhadap Vaksin Covid-19 Menggunakan Metode Support Vector Machine Pada Media Sosial Twitter. 2021.

P. A. Sumitro, D. I. Mulyana, and W. Saputro, “Analisis Sentimen Terhadapat Vaksin Covid-19 di Indonesia pada Twitter Menggunakan Metode Lexicon Based,” J. Inform. dan Teknol. Komput., vol. 02, no. 02, pp. 50–56, 2021.

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.

E. Lim, T. I. Istts, E. I. Setiawan, and T. I. Istts, “Stance Classification Post Kesehatan di Media Sosial Dengan FastText Embedding dan Deep Learning,” J. Intell. Syst. Comput. 66, pp. 65–73.

V. S. Ginting, K. Kusrini, and E. Taufiq, “Implementasi Algoritma C4.5 untuk Memprediksi Keterlambatan Pembayaran Sumbangan Pembangunan Pendidikan Sekolah Menggunakan Python,” Inspir. J. Teknol. Inf. dan Komun., vol. 10, no. 1, pp. 36–44, 2020.

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