OPINION MINING ABOUT PARFUM ON E-COMMERCE BUKALAPAK.COM USING THE NAÏVE BAYES ALGORITHM
Abstract
Information plays a very important role in the rapid development of the world. Many people use online media to search for information, one of which is to find out information about the negative or positive of a product in e-commerce based on the comments that exist. To find out the classification of all comments-comet takes quite a long time in reading it. So, to make it easier than that all made a classification system to determine the classification of comments. In this classification process, the Naive Bayes algorithm is used as a solution to the problem. The process with the Naïve Bayes algorithm requires training data which is used as learning material from the system. The training data used is taken from one e-commerce site, Bukalapak.com regarding perfume products. Taking comments from Buakalapak.com used crawling techniques to retrieve comments from the whole product. The training data needed in this system is 1000 comments consisting of 500 positive training comments and 500 negative training comments. To get the accuracy value, it requires 300 test comments consisting of 150 positive test comments and 150 negative test comments. From the results of testing with Naive Bayes, the accuracy rate can be quite good, namely with a precision value of 96.44%, 96.34% recall, and an accuracy of 96.33%.
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References
F. N. Zuhri and A. Alamsyah, “Menggunakan Naive Bayes Classifier Di Forum Kaskus Public Sentiment Analysis of Smartfren Brand Using Naive Bayes Classifier on Kaskus Forum,” e-Proceeding Manag., vol. 4, no. 1, pp. 242–251, 2017.
S. Prasetyo and T. Widodo, “Anteseden Kepercayaan Pengguna Pada Penawaran E-Commerce Dan Konsekuensinya Terhadap Niat Beli ( Studi Pengguna E- Commerce Provinsi Dki Jakarta ) Antecedent of Trust Users on Offering E-Commerce and Its Consequences To the Purchase Intention ( Study User,” e-Proceeding Manag., vol. 4, no. 2, pp. 1429–1436, 2017.
Y. Hapsari, M. F. Hidayattullah, and M. Khambali, “Opinion Mining Terhadap Toko Online Di Media Sosial Menggunakan Algoritma Naïve Bayes,” J. Inform. Pengemb. IT, vol. 03, no. 02, pp. 233–236, 2018.
S. Ernawati, “Penerapan Particle Swarm Optimization Untuk Seleksi Fitur Pada Analisis Sentimen Review Perusahaan Penjualan Online Menggunakan Naïve Bayes,” J. Evolusi, vol. 4, no. 1, pp. 45–54, 2016.
D. Pakpahan and H. Widyastuti, “Aplikasi Opinion Mining dengan Algoritma Naïve Bayes untuk Menilai Berita Online,” J. Integr., vol. 6, no. 1, pp. 1–10, 2014.
D. A. Kristiyanti, “Analisis Sentimen Review Produk Kosmetik Menggunakan Algoritma Support Vector Machine dan Particle Swarm Optimization Sebagai Metode Seleksi Fitur,” Semin. Nas. Inov. dan Tren, pp. 134–141, 2015.
A. W. Attabi, L. Muflikhah, and M. A. Fauzi, “Penerapan Analisis Sentimen untuk Menilai Suatu Produk pada Twitter Berbahasa Indonesia dengan Metode Naïve Bayes Classifier dan Information Gain,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 11, pp. 4548–4554, 2018.
Muljono, D. P. Artanti, A. Syukur, A. Prihandono, and D. R. I. M. Setiadi, “Analisa Sentimen Untuk Penilaian Pelayanan Situs Belanja Online Menggunakan Algoritma Naïve Bayes,” Konf. Nas. Sist. Inf., pp. 8–9, 2018.
R. T. Adek and S. Nasution, “Tweet Clustering in Indonesian Language Twitter Social Media using Naive Bayes Classifier Method,” Eurasian J. Anal. Chem., vol. 13, no. 6, pp. 277–284, 2018.
S. Hanggara, T. M. Akhriza, and M. Husni, “Aplikasi Web Untuk Analisis Sentimen Pada Opini Produk dengan Metode Naive Bayes,” Semin. Nas. Inov. Dan Apl. Teknol. Di Ind., pp. 1–6, 2017.
B. Gunawan, H. S. Pratiwi, and E. E. Pratama, “Sistem Analisis Sentimen pada Ulasan Produk Menggunakan Metode Naive Bayes,” J. Edukasi dan Penelit. Inform., vol. 4, no. 2, p. 118, Dec. 2018.
E. M. Sipayung, H. Maharani, and I. Zefanya, “Perancangan Sistem Analisis Sentimen Komentar Pelanggan Menggunakan Metode Naive Bayes Classifier,” J. Sist. Inf., vol. 8, no. 1, pp. 958–965, 2016.
D. Dwi and J. Santoso, “Multinomial Naïve Bayes Classifier Untuk Menentukan Review Positif Atau Negatif Pelanggan Website Penjualan,” Semin. Nas. “Inovasi dalam Desain dan Teknol., pp. 117–122, 2015.
Mihuandayani, E. Feriyanto, Syarham, and Kusrini, “Opinion Mining Pada Komentar Twitter e-KTP Menggunaan Naive Bayes Classifier,” Semin. Nas. Teknol. Inf. dan Multimed, p. 6, 2018.
Y. T. Arifin, “Komparasi Fitur Seleksi Pada Algoritma Support Vector Machine Untuk Analisis Sentimen Review,” J. Inform., vol. 3, no. 2, pp. 191–199, 2016.
A. R. C and Y. Lukito, “Klasifikasi Sentimen Komentar Politik dari Facebook Page Menggunakan Naive Bayes,” JUISI, vol. 02, no. 02, pp. 26–34, 2016.
R. Wati, “PENERAPAN ALGORITMA NAIVE BAYES DAN PARTICLE SWARM OPTIMIZATION UNTUK KLASIFIKASI BERITA HOAX PADA MEDIA SOSIAL,” JITK (Jurnal Ilmu Pengetah. dan Teknol. Komputer), 2020.
R. K. Dinata, Fajriana, Zulfa, and N. Hasdyna, “Klasifikasi Sekolah Menengah Pertama/Sederajat Wilayah Bireuen Menggunakan Algoritma K-Nearest Neighbors Berbasis Web,” CESS (Journal Comput. Eng. Syst. Sci., vol. 5, no. February, pp. 33–37, 2020.
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