Perbandingan Tingkat Akurasi Algoritma Naïve Bayes dan Support Vector Machine Dalam Analisis Sentimen Pengguna Aplikasi ShopeePay Pada Google Play Store

Hilmi Ammar; Fadli Al Gani; Muhammad Rifansyah; Firman Noor Hasan

doi:10.22236/teknoka.v9i1.17549

Authors

Hilmi Ammar Universitas Muhammadiyah Prof. DR. HAMKA
Fadli Al Gani Universitas Muhammadiyah Prof. DR. HAMKA
Muhammad Rifansyah Universitas Muhammadiyah Prof. DR. HAMKA
Firman Noor Hasan Universitas Muhammadiyah Prof. DR. HAMKA

DOI:

https://doi.org/10.22236/teknoka.v9i1.17549

Keywords:

Analisis Sentimen, Shopeepay, Support Vector Machine, Naïve Bayes, Google Play Store

Abstract

This research aims to analyze user sentiment towards the ShopeePay application using the Naïve Bayes and SVM algorithms with data obtained through web scraping. Of the 1500 data obtained through scraping, 63 empty data were removed in the cleaning process, leaving 1437 data. This data was then divided into a training set (1149 data) and a test set (288 data). The results showed that the Naïve Bayes algorithm achieved an accuracy of 84.38%, a precision of 79.73%, a recall of 88.72%, and an F1-score of 83.99%, while the Support Vector Machine (SVM) algorithm achieved an accuracy of 80.56%, a precision of 84.07%, a recall of 71.43%, and an F1-score of 77.24%. Overall, Naïve Bayes performed better than Support Vector Machine, especially Naïve Bayes was superior in detecting positive sentiment, while SVM was better in detecting negative sentiment. Data visualization shows that out of 1437 users, around 52.7% gave positive reviews and 47.3% negative reviews, with a diverse rating distribution from users. Based on this distribution, the ShopeePay application user experience can be categorized as predominantly positive, with a difference of 5.4% indicating the difference between 52.7% positive reviews and 47.3% negative reviews from ShopeePay application users.

Downloads

Download data is not yet available.

References

A. Simanungkalit, J. P. P. Naibaho, and A. De Kweldju, “Analisis Sentimen Berbasis Aspek Pada Ulasan Aplikasi Shopee Menggunakan Algoritma Naïve Bayes,” Jutisi J. Ilm. Tek. Inform. dan Sist. Inf., vol. 13, no. 1, p. 659, May 2024, doi: 10.35889/jutisi.v13i1.1826.

T. Fadiyah Basar, D. E. Ratnawati, and I. Arwani, “Analisis Sentimen Pengguna Twitter terhadap Pembayaran Cashless menggunakan Shopeepay dengan Algoritma Random Forest,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 6, no. 3, pp. 1426–1433, 2022, [Online]. Available: http://j-ptiik.ub.ac.id

L. Marlina, A. Mundzir, and H. Pratama, “CASHLESS DAN CARDLESS SEBAGAI PERILAKU TRANSAKSI DI ERA DIGITAL: SUATU TINJAUAN TEORETIS DAN EMPIRIS,” J. Co Manag., vol. 3, no. 2, pp. 533–542, Apr. 2021, doi: 10.32670/comanagement.v3i2.424.

Y. H. Afrizal, S. Dienan Yahya, and F. Zakiyabarsi, “Menelaah Adopsi Konsumen Pada Cashless Payment Pasca Pandemi Sebagai Upaya Menuju Cashless Society,” J. Ilm. Akunt. Perad., vol. 9, no. 2, pp. 313–328, Dec. 2023, doi: 10.24252/jiap.v9i2.41293.

I. H. P. F. K. A. A. Wendy Liana, FINANCIAL TECHNOLOGY (FinTech): Pengantar dan Inovasi Teknologi Keuangan. books.google.com, 2023. [Online]. Available: https://books.google.com/books?hl=en&lr=&id=S10QEQAAQBAJ&oi=fnd&pg=PA1&dq=financial+technology+fintech++pengantar+dan+inovasi+teknologi+keuangan&ots=zROBh4gbxx&sig=i_1wvpxQpMXemz-W0ENUQxsBWV4

Shopee, “Tentang Shopeepay.” Accessed: Nov. 13, 2024. [Online]. Available: https://shopeepay.co.id/

N. P. Husain, S. Sukirman, and S. SAJIAH, “Analisis Sentimen Ulasan Pengguna Tiktok pada Google Play Store Berbasis TF-IDF dan Support Vector Machine,” J. Syst. Comput. Eng., vol. 5, no. 1, pp. 91–102, Jan. 2024, doi: 10.61628/jsce.v5i1.1105.

N. M. S. Hadna, I. S. Paulus, and W. Winarno, “Studi Literatur Tentang Perbandingan Metode Untuk Proses Analisis Sentimen Di Twitter,” 2016, researchgate.net. [Online]. Available: https://www.researchgate.net/profile/Nurrun-Muchammad-Hadna/publication/292831965_Studi_Literatur_Tentang_Perbandingan_Metode_Untuk_Proses_Analisis_Sentimen_di_Twitter/links/56b182ec08ae5ec4ed4895b1/Studi-Literatur-Tentang-Perbandingan-Metode-Untuk-Proses

D. Surya Sayogo, B. Irawan, and A. Bahtiar, “ANALISIS SENTIMEN ULASAN INSTAGRAM DI GOOGLE PLAY STORE MENGGUNAKAN ALGORITMA NAÏVE BAYES,” JATI (Jurnal Mhs. Tek. Inform., vol. 7, no. 6, pp. 3314–3319, Jan. 2024, doi: 10.36040/jati.v7i6.8178.

A. S. Rahayu, A. Fauzi, and R. Rahmat, “Komparasi Algoritma Naïve Bayes Dan Support Vector Machine (SVM) Pada Analisis Sentimen Spotify,” J. Sist. Komput. dan Inform., vol. 4, no. 2, p. 349, Dec. 2022, doi: 10.30865/json.v4i2.5398.

D. Normawati and S. A. Prayogi, “Implementasi Naïve Bayes Classifier Dan Confusion Matrix Pada Analisis Sentimen Berbasis Teks Pada Twitter,” J. Sains Komput. Inform., vol. 5, no. 2, pp. 697–711, 2021, doi: http://dx.doi.org/10.30645/j-sakti.v5i2.369.

Azka Fikri, “PENGARUH PENGGUNAAN SHOPEEPAY SEBAGAI DOMPET DIGITAL TERHADAP PERILAKU KONSUMTIF MAHASISWA FEB USU,” KomunikA, vol. 17, no. 2, pp. 1–8, Nov. 2021, doi: 10.32734/komunika.v17i2.7556.

B. R. Aditya, “Penggunaan Web Crawler Untuk Menghimpun Tweets dengan Metode Pre-Processing Text Mining,” J. INFOTEL - Inform. Telekomun. Elektron., vol. 7, no. 2, p. 93, Nov. 2015, doi: 10.20895/infotel.v7i2.35.

A. R. Abdillah and F. N. Hasan, “Analisis Sentimen Terhadap Kandidat Calon Presiden Berdasarkan Tweets Di Sosial Media Menggunakan Naive Bayes Classifier,” SMATIKA J., vol. 13, no. 01, pp. 117–130, Jul. 2023, doi: 10.32664/smatika.v13i01.750.

T. T. Widowati and M. Sadikin, “Analisis Sentimen Twitter terhadap Tokoh Publik dengan Algoritma Naive Bayes dan Support Vector Machine,” Simetris J. Tek. Mesin, Elektro dan Ilmu Komput., vol. 11, no. 2, pp. 626–636, Oct. 2021, doi: 10.24176/simet.v11i2.4568.

N. A. Rakhmawati, R. A. Zuhroh, Q. N. Muna, and V. R. Dianutami, “Klasterisasi Keyword Terkait Pornografi pada Media Sosial Twitter Menggunakan Latent Dirichlet Allocation,” J. Inf. Eng. Educ. Technol., vol. 6, no. 2, pp. 66–72, 2022, doi: 10.26740/jieet.v6n2.p66-72.

N. R. Jayanti, “Analisis Sentimen Review Aplikasi Identitas Kependudukan Digital Menggunakan Algoritma Support Vector Machine,” Glob. J. Lentera BITEP, vol. 2, no. 04, pp. 132–138, 2024, doi: https://doi.org/10.59422/global.v2i04.460.

S. Sahilla, F. Amalia, and ..., “Klasifikasi Sentimen Pengguna Terhadap Akun Twitter Official Dana Dengan Menggunakan Algortima Naïve Bayes Classifier,” JURSISTEKNI (Jurnal Sist. Inf. dan Teknol. Informasi), vol. 6, no. 3, pp. 580–591, 2024, doi: https://doi.org/10.52005/jursistekni.v6i3.369.

L. A. Andika, P. A. N. Azizah, and R. Respatiwulan, “Analisis Sentimen Masyarakat terhadap Hasil Quick Count Pemilihan Presiden Indonesia 2019 pada Media Sosial Twitter Menggunakan Metode Naive Bayes Classifier,” Indones. J. Appl. Stat., vol. 2, no. 1, p. 34, Jul. 2019, doi: 10.13057/ijas.v2i1.29998.

N. A. Rakhmawati, M. I. Aditama, R. I. Pratama, and K. H. U. Wiwaha, “Analisis Klasifikasi Sentimen Pengguna Media Sosial Twitter Terhadap Pengadaan Vaksin COVID-19,” J. Inf. Eng. Educ. Technol., vol. 4, no. 2, pp. 90–92, 2020, doi: 10.26740/jieet.v4n2.p90-92.

A. Riyani, M. Zidny Naf’an #2, and A. Burhanuddin, “Penerapan Cosine Similarity dan Pembobotan TF-IDF untuk Mendeteksi Kemiripan Dokumen,” J. Linguist. Komputasional, vol. 2, no. 1, pp. 23–27, Mar. 2019, doi: 10.26418/jlk.v2i1.17.

F. A. Adiyatma, S. Alam, and M. A. Komara, “ANALISIS SENTIMEN MASYARAKAT DI PLATFORM X TERHADAP PENGGUNAAN BANSOS UNTUK MEMENANGKAN SALAH SATU CAPRES TERTENTU DI …,” JATI (Jurnal Mhs. Tek. Inform., vol. 8, no.05, pp. 9941–9947, 2024, doi: https://doi.org/10.36040/jati.v8i5.10836

M. Tirta Nugraha, N. Nina Sulistiyowati, and U. Ultach Enri, “Analisis Sentimen Ulasan Aplikasi Satu Sehat Pada Google Play Store Menggunakan Naïve Bayes Classifier,” JATI (Jurnal Mhs. Tek. Inform., vol. 7, no. 5, pp. 3593–3601, 2024, doi: 10.36040/jati.v7i5.7753.

D. Darwis, E. S. Pratiwi, and A. F. O. Pasaribu, “Penerapan Algoritma Svm Untuk Analisis Sentimen Pada Data Twitter Komisi Pemberantasan Korupsi Republik Indonesia,” Edutic - Sci. J. Informatics Educ., vol. 7, no. 1, pp. 1–11, Nov. 2020, doi: 10.21107/edutic.v7i1.8779.

M. Galih Pradana, “Penggunaan Fitur Wordcloud Dan Document Term Matrix Dalam Text Mining,” J. Ilm. Infromatika, vol. 08, no. 01, pp. 38–43, 2020, doi: https://doi.org/10.33884/jif.v8i01.1838.