The Shopee Application User Reviews Sentiment Analysis Employing Naïve Bayes Algorithm
Article Sidebar
Main Article Content
Abstract
With the significant growth of internet use in Indonesia, there has been a surge in online business activity. The convenience offered by online platforms is increasingly in demand because it allows consumers to shop without being bound by a certain time or location. Before making a purchase, consumers tend to look for information first through various sources such as reviews on blogs, Instagram, TikTok, or reviews on the YouTube platform which is integrated in the application. This research adopted a method that included planning, literature study, data collection, and data processing using a dataset from the Play Store application which was taken using the Python library with an initial amount of 5000 data. After a manual filtering process which involved removing slang words, eliminating duplications, and normalizing punctuation marks, the remaining data was 3946. The application of the Naïve Bayes algorithm in this research uses probability methods to classify and predict 3141 training data and 805 test data, with Python library help. The accuracy calculation results show satisfactory performance, with an accuracy of 86.00%, precision of 80.74%, recall of 78.13%, and f1-score of 79.00% on a dataset of 3946. Analysis from this research shows the dominance of positive sentiment in 2050 data, while sentiment negative amounted to 1199 data. The amount and quality of training data plays an important role in system predictions, where high data quality provides better accuracy in predicting sentiment classes
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
References
Nugroho, D.G., Chrisnanto, Y.H. and Wahana, A., 2016, September. Analisis Sentiment Pada Jasa Ojek Online Menggunakan Metode Naive Bayes. In Prosiding Seminar Sains Nasional dan Teknologi. 1(1), pp. 156-161. DOI: http://dx.doi.org/10.36499/psnst.v1i1.1526.
Firmansyach, W.A., Hayati, U. and Wijaya, Y.A., 2023. Analisa Terjadinya Overfitting Dan Underfitting Pada Algoritma Naive Bayes Dan Decision Tree Dengan Teknik Cross Validation. JATI (Jurnal Mahasiswa Teknik Informatika), 7(1), pp.262-269. DOI: https://doi.org/10.36040/jati.v7i1.6329.
Ahmad, A.Z., Asril, E., Sadar, M. and Turnandes, Y., 2023. Analisis Sentiment Opini Terhadap Vaksin Covid-19 Pada Media Sosial Twitter Menggunakan Naïve Bayes Dan Decision Tree. ZONAsi: Jurnal Sistem Informasi, 5(1), pp.100-110. DOI: https://doi.org/10.31849/zn.v5i1.5553
Apandi, T.H. and Sugianto, C.A., 2019. Algoritma Naive Bayes untuk Prediksi Kepuasan Pelayanan Perekaman e-KTP. Juita, 7(2), pp.125-128. DOI: https://doi.org/10.30595/juita.v7i2.3608
Aulia, Z.N., Jati, G.K. and Santoso, I., 2023. ANALISIS SENTIMENT TANGGAPANPUBLIC MENGENAI E-TILANG MELALUI MEDIA SOSIAL YOUTUBE MENGGUNAKAN ALGORITMA NAIVE BAYES. IKRA-ITH Informatika: Jurnal Komputer dan Informatika, 7(2), pp.150-156.
Budiman, B., 2021. Perbandingan Algoritma Klasifikasi Data Mining untuk Penelusuran Minat Calon Mahasiswa Baru. NUANSA INFORMATIKA, 15(2), pp.37-52. DOI: https://doi.org/10.25134/nuansa.v15i2.4162
Cahyaningtyas, C., Nataliani, Y. and Widiasari, I.R., 2021. Analisis Sentiment pada rating aplikasi Shopee menggunakan metode Decision Tree berbasis SMOTE. AITI, 18(2), pp.173-184. DOI: https://doi.org/10.24246/aiti.v18i2.173-184
Depari, D.H., Widiastiwi, Y. and Santoni, M.M., 2022. Perbandingan Model Decision Tree, Naive Bayes dan Random Forest untuk Prediksi Klasifikasi Penyakit Jantung. Informatik: Jurnal Ilmu Komputer, 18(3), pp.239-248. DOI: https://doi.org/10.52958/iftk.v18i3.4694
Shabrilianti, S.S., Triayudi, A. and Lantana, D.A., 2023. Analisis Klasifikasi Perfomance KPI Salesman Menggunakan Metode Decision Tree Dan Naïve Bayes. JURIKOM (Jurnal Riset Komputer), 10(1), pp.182-191. DOI: http://dx.doi.org/10.30865/jurikom.v10i1.5628.
Situmorang, R.N., 2021. Klasifikasi Kesegaran Ikan Berdasarkan Ekstraksi Fitur Menggunakan Metode K-Nearest Neighbor Dan Hue Saturation Value (Thesis, Universitas Islam Negeri Sumatera Utara Medan)..
Zukhoiriyah, D., 2022. Analisis Sentiment Pada Review Pengguna ECommerce Menggunakan Algoritma Naïve Bayes (Studi Kasus: Shopee) (Thesis, Universitas Islam Negeri Sumatera Utara).
Firdaus, A.L.I., WIDODO, S., Sutrisman, A.D.I., GADING, S. and MARDIANA, R., 2019. Rancang bangun sistem informasi perpustakaan menggunakan web service pada jurusan teknik komputer polsri. INFORMANIKA, 5(2).
Ginting, V.S., Kusrini, K. and Taufiq, E., 2020. Implementasi Algoritma C4. 5 untuk Memprediksi Keterlambatan Pembayaran Sumbangan Pembangunan Pendidikan Sekolah Menggunakan Python. Inspiration: Jurnal Teknologi Informasi dan Komunikasi, 10(1), pp.36-44. DOI: https://doi.org/10.35585/inspir.v10i1.2535
Kamil, M. and Cholil, W., 2020. Analisis Perbandingan Algoritma C4. 5 dan Naive Bayes pada Lulusan Tepat Waktu Mahasiswa di Universitas Islam Negeri Raden Fatah Palembang. Jurnal Informatika, 7(2), pp.97-106. DOI: https://doi.org/10.31294/ji.v7i2.7723.
Nuraeni, R., Sudiarjo, A. and Rizal, R., 2021. Perbandingan Algoritma Naïve Bayes Classifier dan Algoritma Decision Tree untuk Analisa Sistem Klasifikasi Judul Skripsi. Innovation in Research of Informatics (INNOVATICS), 3(1), pp. 26-31. DOI: https://doi.org/10.37058/innovatics.v3i1.2976
Oktafia, D. and Pardede, D.L., 2010. Perbandingan Kinerja Algoritma Decision Tree dan Naïve Bayes dalam Prediksi Kebangkrutan. Universitas Gunadarma.
Permana, J.N., Goejantoro, R. and Prangga, S., 2023. Perbandingan Algoritma C4. 5 Dan Naïve Bayes Untuk Prediksi Ketepatan Waktu Studi Mahasiswa. EKSPONENSIAL, 13(2), pp.161-170. DOI: https://doi.org/10.30872/eksponensial.v13i2.947.
Putri, T.A.Q., Triayudi, A. and Aldisa, R.T., 2023. Implementasi Algoritma Decision Tree dan Naïve Bayes Untuk Klasifikasi Sentiment Terhadap Kepuasan Pelanggan Starbucks. Journal of Information System Research (JOSH), 4(2), pp.641-649. DOI: https://doi.org/10.47065/josh.v4i2.2949
Supriyadi, A., 2023. Perbandingan Algoritma Naive Bayes dan Decision Tree (C4. 5) dalam Klasifikasi Dosen Berprestasi. Generation Journal, 7(1), pp.39-49. DOI: https://doi.org/10.29407/gj.v7i1.19797.