Perbandingan Algoritma Naive Bayes Dan SVM Dalam Sentimen Analisis Marketplace Pada Twitter

Indra Kurniawan; April Lia Hananto; Shofa Shofia Hilabi; Agustia Hananto; Bayu Priyatna; Aviv Yuniar Rahman

doi:10.35957/jatisi.v10i1.3582

Indra Kurniawan Universitas Buana Perjuangan Karawang
April Lia Hananto Universitas Buana Perjuangan Karawang
Shofa Shofia Hilabi Universitas Buana Perjuangan
Agustia Hananto Universitas Buana Perjuangan
Bayu Priyatna Universitas Buana Perjuangan
Aviv Yuniar Rahman Universitas Widyagama

DOI: https://doi.org/10.35957/jatisi.v10i1.3582

Keywords: Sentiment Analysis, Marketplace, Naive Bayes, Support Vector Machine, SMOTE

Abstract

Online buying and selling transactions are increasing in Indonesia due to the ease of using marketplace platforms, and online shopping saves more time than offline shopping. Each marketplace has advantages and disadvantages, this affects customer sentiment who have made transactions on the marketplace platform. This research uses customer opinion from tweet data based on positive or negative sentiments to compare the Naive Bayes (NB) and Support Vector Machine (SVM) classification algorithms with the aim of finding out the best classification algorithm based on the accuracy value for sentiment analysis using the marketplace platform. The tweet data in this study was taken from October 18 to November 11, 2022. To test the performance of the NB and SVM classification algorithms using the Cross Validation method and from the results of the comparison test that the SVM algorithm has the best accuracy value compared to the NB algorithm. Where the accuracy value of Tokopedia uses the NB algorithm is 85.34%, and the accuracy value uses SVM 86.82%, the accuracy value for Shopee uses NB is 80.04%, and the accuracy value uses SVM 80.91%. and Lazada which uses the NB algorithm has an accuracy value of 83.52%, while the accuracy value uses SVM 88.93%, which means that the use of the SVM algorithm has the best level of accuracy.

References

[1] D. Darmawan and A. R. Putra, “Pengalaman Pengguna, Keamanan Transaksi, Kemudahan Penggunaan, Kenyamanan, Dan Pengaruhnya Terhadap Perilaku Pembelian Online Secara Impulsif,” Relasi J. Ekon., vol. 18, no. 1, pp. 26–45, 2022, doi: 10.31967/relasi.v18i1.523.
[2] “Simak 10 Marketplace di Indonesia yang Terlaris dan Paling Banyak Dikunjungi di Kuartal Pertama 2022.” https://www.idxchannel.com/milenomic/simak-10-marketplace-di-indonesia-yang-terlaris-dan-paling-banyak-dikunjungi-di-kuartal-pertama-2022 (accessed Oct. 28, 2022).
[3] “Kementerian Komunikasi dan Informatika.” https://www.kominfo.go.id/index.php/content/detail/3415/Kominfo+%3A+Pengguna+Internet+di+Indonesia+63+Juta+Orang/0/berita_satker (accessed Oct. 28, 2022).
[4] M. Azhar, N. Hafidz, B. Rudianto, and W. Gata, “Marketplace Sentiment Analysis Using Naive Bayes And Support Vector Machine,” PIKSEL Penelit. Ilmu Komput. Sist. Embed. Log., vol. 8, no. 2, pp. 91–100, 2020, doi: 10.33558/piksel.v8i2.2272.
[5] H. Irsyad and M. R. Pribadi, “Klasifikasi Opini Terhadap Pertanian Sawit (Palm Oil) Indonesia Menggunakan Naïve Bayes,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 6, no. 2, pp. 230–239, 2020, doi: 10.35957/jatisi.v6i2.182.
[6] M. R. Pribadi, H. D. Purnomo, Hendry, K. D. Hartomo, I. Sembiring, and A. Iriani, “Improving the Accuracy of Text Classification Using the over Sampling Technique in the Case of Sinovac Vaccine,” Int. Conf. Electr. Eng. Comput. Sci. Informatics, vol. 2022-October, pp. 106–110, 2022, doi: 10.23919/EECSI56542.2022.9946508.
[7] M. Intan Pratiwi Hant and Hendry, “Data Mining Technique Using Naïve Bayes Algorithm To Predict Shopee Consumer Satisfaction Among Millennial Generation,” J. Tek. Inform., vol. 3, no. 4, pp. 829–838, 2022, [Online]. Available: https://doi.org/10.20884/1.jutif.2022.3.4.295.
[8] R. A. Permana and S. Sahara, “Review Analisis Produk Marketplace Online pada Algoritma Support Vector Machine,” J. Ilm. Inform., vol. 6, no. 1, pp. 50–58, 2021, doi: 10.35316/JIMI.V6I1.1227.
[9] A. Andreyestha and Q. N. Azizah, “Analisa Sentimen Kicauan Twitter Tokopedia Dengan Optimalisasi Data Tidak Seimbang Menggunakan Algoritma SMOTE,” Infotek J. Inform. dan Teknol., vol. 5, no. 1, pp. 108–116, 2022, doi: 10.29408/jit.v5i1.4581.
[10] S. Samsir, A. Ambiyar, U. Verawardina, F. Edi, and R. Watrianthos, “Analisis Sentimen Pembelajaran Daring Pada Twitter di Masa Pandemi COVID-19 Menggunakan Metode Naïve Bayes,” J. Media Inform. Budidarma, vol. 5, no. 1, pp. 157–163, 2021, doi: http://dx.doi.org/10.30865/mib.v5i1.2580.
[11] D. A. Agustina, S. Subanti, and E. Zukhronah, “Implementasi Text Mining Pada Analisis Sentimen Pengguna Twitter Terhadap Marketplace di Indonesia Menggunakan Algoritma Support Vector Machine,” Indones. J. Appl. Stat., vol. 3, no. 2, p. 109, 2021, doi: 10.13057/ijas.v3i2.44337.
[12] R. Rachman and R. N. Handayani, “Klasifikasi Algoritma Naive Bayes Dalam Memprediksi Tingkat Kelancaran Pembayaran Sewa Teras UMKM,” J. Inform., vol. 8, no. 2, pp. 111–122, 2021, doi: 10.31294/ji.v8i2.10494.
[13] R. W. Pratiwi, S. F. H, D. Dairoh, D. I. Af’idah, Q. R. A, and A. G. F, “Analisis Sentimen Pada Review Skincare Female Daily Menggunakan Metode Support Vector Machine (SVM),” J. Informatics, Inf. Syst. Softw. Eng. Appl., vol. 4, no. 1, pp. 40–46, 2021, doi: 10.20895/inista.v4i1.387.
[14] E. Fitri, Y. Yuliani, S. Rosyida, and W. Gata, “Analisis Sentimen Terhadap Aplikasi Ruangguru Menggunakan Algoritma Naive Bayes, Random Forest Dan Support Vector Machine,” J. Transform., vol. 18, no. 1, pp. 71–80, 2020, doi: 10.26623/transformatika.v18i1.2317.
[15] A. L. Hananto, B. Priyatna, A. Fauzi, A. Yuniar Rahman, Y. Pangestika, and Tukino, “Analysis of the Best Employee Selection Decision Support System Using Analytical Hierarchy Process (AHP),” J. Phys. Conf. Ser., vol. 1908, no. 1, 2021, doi: 10.1088/1742-6596/1908/1/012023.
[16] H. S. Utama, D. Rosiyadi, B. S. Prakoso, and D. Ariadarma, “Analisis Sentimen Sistem Ganjil Genap di Tol Bekasi Menggunakan Algoritma Support Vector Machine,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 3, no. 2, pp. 243–250, 2019, doi: 10.29207/resti.v3i2.1050.