ANALISIS SENTIMEN ULASAN E-COMMERCE SHOPEE PADA GOOGLE PLAY STORE MENGGUNAKAN MACHINE LEARNING
DOI:
https://doi.org/10.31949/jensitec.v10i02.9071Abstract
With increasingly advanced technology nowadays, it become easier for people to shop. Previously we had to shop in person, now thanks to technology we can shop online with E-Commerce. E-Commerce provides an opportunity for producers to promote their products to be more accessible and makes it easier for consumers to share activities such as shopping and providing reviews. Reviews play an important role in a product, especially in building consumer confidence in choosing the desired product. Therefore, sentiment analysis is needed to help choose an E-Commerce application. Here the author will carry out Sentiment Analysis using Machine Learning. The purpose of this research is to conclude and identify whether a review is a positive review or a negative review. The algorithm used in this research is Naïve Bayes and Decision Tree using TF-IDF. The research results obtained are that the Naïve Bayes algorithm has higher accuracy with an accuracy of 0.88 or around 88%, and the negative label has a precision prediction result of 0.87, recall has 1.00, fi-score has 0.93, and finally with support 26, while the positive label with precision prediction results has 1.00, recall has 0.33, fi-score has 0.50, and the last one has support 6.
Keywords:
E-Commerce, Google Play Store, Machine Learning, Naïve Bayes, Decision Tree.Downloads
References
A. Juhria, N. Meinitasari, F. I. Fauzi, and A. Yusuf, “Pengaruh e-service quality terhadap kepuasan pelanggan di aplikasi e-commerce shopee,” J. Manaj., vol. 13, no. 1, pp. 55–62, 2021.
A. K. Putra, R. D. Nyoto, and H. S. Pratiwi, “17991-53021-4-Pb (1),” J. Sist. dan Teknol. Inf., vol. 5, no. 1, pp. 22–26, 2017.
IPrice, “Peta E_Commerce Indonesia.” https://iprice.co.id/insights/mapofecommerce/. Diakses tanggal 6 Maret 2024
M. R. Firdaus, F. M. Rizki, F. M. Gaus, and I. K. Susanto, “Analisis Sentimen Dan Topic Modelling Dalam Aplikasi Ruangguru,” J-SAKTI (Jurnal Sains Komput. dan Inform., vol. 4, no. 1, p. 66, 2020, doi: 10.30645/j-sakti.v4i1.188.
I. Habib Kusuma and N. Cahyono, “Analisis Sentimen Masyarakat Terhadap Penggunaan E-Commerce Menggunakan Algoritma K-Nearest Neighbor,” J. Inform. J. Pengemb. IT, vol. 8, no. 3, pp. 302–307, 2023.
E. Laia and M. Yamin, “Penerapan Algoritma Naïve Bayes dalam Menganalisis Sentimen pada Review Pengguna E-Commerce,” Media Online), vol. 4, no. 1, pp. 305–316, 2023, doi: 10.30865/klik.v4i1.1186.
A. Oktian Permana and Sudin Saepudin, “Perbandingan algoritma k-nearst neighbor dan naïve bayes pada aplikasi shopee,” J. CoSciTech (Computer Sci. Inf. Technol., vol. 4, no. 1, pp. 25–32, 2023, doi: 10.37859/coscitech.v4i1.4474.
T. J. et al James W, Elston D, “済無No Title No Title No Title,” Andrew’s Dis. Ski. Clin. Dermatology., pp. 10–29, 20AD.
A. D. Adhi Putra, “Analisis Sentimen pada Ulasan pengguna Aplikasi Bibit Dan Bareksa dengan Algoritma KNN,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 8, no. 2, pp. 636–646, 2021, doi: 10.35957/jatisi.v8i2.962.
N. L. P. C. Savitri, R. A. Rahman, R. Venyutzky, and N. A. Rakhmawati, “Analisis Klasifikasi Sentimen Terhadap Sekolah Daring pada Twitter Menggunakan Supervised Machine Learning,” J. Tek. Inform. dan Sist. Inf., vol. 7, no. 1, pp. 47–58, 2021, doi: 10.28932/jutisi.v7i1.3216.
C. Chazar and M. H. Rafsanjani, “Penerapan Teachable Machine Pada Klasifikasi Machine Learning Untuk Identifikasi Bibit Tanaman,” Pros. Semin. Nas. Inov. dan Adopsi Teknol., vol. 2, no. 1, pp. 32–40, 2022, doi: 10.35969/inotek.v2i1.207.
L. Ardiani, H. Sujaini, and T. Tursina, “Implementasi Sentiment Analysis Tanggapan Masyarakat Terhadap Pembangunan di Kota Pontianak,” J. Sist. dan Teknol. Inf., vol. 8, no. 2, p. 183, 2020, doi: 10.26418/justin.v8i2.36776.
S. N. J. Fitriyyah, N. Safriadi, and E. E. Pratama, “Analisis Sentimen Calon Presiden Indonesia 2019 dari Media Sosial Twitter Menggunakan Metode Naive Bayes,” J. Edukasi dan Penelit. Inform., vol. 5, no. 3, p. 279, 2019, doi: 10.26418/jp.v5i3.34368.
J. Sihombing, “Klasifikasi Data Antroprometri Individu Menggunakan Algoritma Naïve Bayes Classifier,” BIOS J. Teknol. Inf. dan Rekayasa Komput., vol. 2, no. 1, pp. 1–10, 2021, doi: 10.37148/bios.v2i1.15.
D. N. I. Huda, C. Prianto, and R. M. Awangga, “Analisis Sentimen Perbandingan Layanan Jasa Pengiriman Kurir Pada Ulasan Play Store Menggunakan Metode Decision Tree Dan Random Forest,” J. Ilm. Inform., vol. 11, no. 02, pp. 150–158, 2023, doi: 10.33884/jif.v11i02.7952.
Syahril Dwi Prasetyo, Shofa Shofiah Hilabi, and Fitri Nurapriani, “Analisis Sentimen Relokasi Ibukota Nusantara Menggunakan Algoritma Naïve Bayes dan KNN,” J. KomtekInfo, vol. 10, pp. 1–7, 2023, doi: 10.35134/komtekinfo.v10i1.330.
A. R. Rizqullah, A. Wedhasmara, R. I. Heroza, A. Putra, F. Fathoni, and P. Putra, “Analisis Masalah Pada Data Review Aplikasi Terhadap Layanan E-Commerce Menggunakan Metode Text Classification,” J. Tekno Kompak, vol. 16, no. 1, p. 186, 2022, doi: 10.33365/jtk.v16i1.1448.
A. Muhammadin and I. A. Sobari, “Analisis Sentimen Pada Ulasan Aplikasi Kredivo Dengan Algoritma Svm Dan Nbc,” Reputasi J. Rekayasa Perangkat Lunak, vol. 2, no. 2, pp. 85–91, 2021, doi: 10.31294/reputasi.v2i2.785.
I. Zulfahmi, J. Williem Iskandar, P. V Medan, S. Tuan, and D. Serdang, “Analisis Sentimen Aplikasi PLN Mobile Menggunakan Metode Decission Tree,” J. Penelit. Rumpun Ilmu Tek., vol. 3, no. 1, pp. 11–21, 2024.
E. Sera, H. Hazriani, M. Mirfan, and Y. Yuyun, “Analisis Sentimen Ulasan Produk di E-Commerce Bukalapak Menggunakan Natural Language Processing,” Pros. SISFOTEK, pp. 237–243, 2023, [Online].
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