Analisis Sentimen Customer Feedback Tokopedia Menggunakan Algoritma Naïve Bayes

 (*)Aldian Umbu Tamu Ama Mail (Politeknik Bhakti Semesta, Salatiga, Indonesia)
 Deva Nita Mulya (Politeknik Bhakti Semesta, Salatiga, Indonesia)
 Yashinta Putri D Astuti (Politeknik Bhakti Semesta, Salatiga, Indonesia)
 Ignatius Bias Galih Prasadhya (Politeknik Bhakti Semesta, Salatiga, Indonesia)

(*) Corresponding Author

Submitted: September 2, 2022; Published: September 30, 2022

Abstract

Products and customers have a close relationship, therefore UMKM need to build good relationships with customers. The most common way that companies or UMKM do is to look at the reviews given, this is called customer feedback. The results of customer feedback to companies or UMKM can improve service and product quality. The problem that arises is how to process the many reviews given, especially reviews from marketplaces like Tokopedia. Therefore, a method is needed to see user reviews of the products being sold, whether positive or negative. The method that will be used is sentiment analysis. Sentiment analysis is the process of understanding and extracting and automatically processing text data and can produce sentiments that are displayed in a sentence. The steps taken were taking House of Smith customer review data at Tokopedia, manual labeling to get positive and negative data reviews, data preprocessing, TF-IDF weighting and classification using the Naïve Bayes algorithm. The results of sentiment testing using the Naïve Bayes algorithm with TF-IDF weighting quality accuracy of 83% with visualization of the distribution of words that appear the most are the words 'good', 'comfortable' and 'use' for positive reviews. The most frequent negative reviews were 'material' and 'thin' which indicated that some buyers felt that the product had a thin material.

Keywords


Naive Bayes; Sentiment Analysis; Customer Feedback; UMKM

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References

Katadata Insight Center, “Status Literasi Digital di Indonesia Ringkasan Eksekutif,” 2021, [Online]. Available: https://katadata.co.id/StatusLiterasiDigital

tSurvey.id, “tSurvey.id Rilis Hasil Survei Pemanfaatan eCommerce Paling Dipercaya dan Diandalkan oleh UMKM Indonesia 2022,” 2022. https://www.telkomsel.com/about-us/news/tsurveyid-rilis-hasil-survei-pemanfaatan-ecommerce-paling-dipercaya-dan-diandalkan

D. Hariyanti, “Mayoritas Masyarakat Indonesia Pernah Belanja Online,” 2021. [Online]. Available: https://databoks.katadata.co.id/datapublish/2022/04/27/mayoritas-masyarakat-indonesia-pernah-belanja-online

D. Mourtzis et al., “Customer feedback gathering and management tools for product-service system design,” Procedia CIRP, vol. 67, pp. 577–582, 2018, doi: 10.1016/j.procir.2017.12.264.

E. Park, Y. Jang, J. Kim, N. J. Jeong, K. Bae, and A. P. del Pobil, “Determinants of customer satisfaction with airline services: An analysis of customer feedback big data,” J. Retail. Consum. Serv., vol. 51, no. June, pp. 186–190, 2019, doi: 10.1016/j.jretconser.2019.06.009.

M. Schuckert, S. Liang, R. Law, and W. Sun, “How do domestic and international high-end hotel brands receive and manage customer feedback?,” Int. J. Hosp. Manag., vol. 77, no. February, pp. 528–537, 2019, doi: 10.1016/j.ijhm.2018.08.017.

F. Fathoni, E. Afrianti, and R. I. Heroza, “Klasifikasi Teks dengan Naïve Bayes Classifier (NBC) untuk Pengelompokan Keterangan Laporan dan Durasi Recovery Time Laporan Gangguan Listrik PT. PLN (Persero) WS2JB Area Palembang,” JSI J. Sist. Inf., vol. 12, no. 1, pp. 1955–1961, 2020, doi: 10.36706/jsi.v12i1.9586.

A. P. Natasuwarna, “Analisis Sentimen Keputusan Pemindahan Ibukota Negara Menggunakan Klasifikasi Naive Bayes,” Semin. Nas. Sist. Inf. dan Tek. Inform. SENSITIF, pp. 47–53, 2019.

R. Risnantoyo, A. Nugroho, and K. Mandara, “Sentiment Analysis on Corona Virus Pandemic Using Machine Learning Algorithm,” J. Informatics Telecommun. Eng., vol. 4, no. 1, pp. 86–96, 2020, doi: 10.31289/jite.v4i1.3798.

F. A. D. Aji Prasetya Wibawa, Muhammad Guntur Aji Purnama, Muhammad Fathony Akbar, “Metode-metode Klasifikasi,” Pros. Semin. Ilmu Komput. dan Teknol. Inf., vol. 3, no. 1, p. 134, 2018.

D. Tuhenay, “Perbandingan Klasifikasi Bahasa Menggunakan Metode Naïve Bayes Classifier (NBC) Dan Support Vector Machine (SVM),” JIKO (Jurnal Inform. dan Komputer), vol. 4, no. 2, pp. 105–111, 2021, doi: 10.33387/jiko.v4i2.2958.

F. S. Jumeilah, “Penerapan Support Vector Machine (SVM) untuk Pengkategorian Penelitian,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 1, no. 1, pp. 19–25, 2017, doi: 10.29207/resti.v1i1.11.

M. J. S. Keenan, Advanced Positioning, Flow, and Sentiment Analysis in Commodity Markets. 2019. doi: 10.1002/9781119603849.ch1.

R. Dale, Classical approaches to natural language processing. 2010.

N. K. Widyasanti, I. K. G. Darma Putra, and N. K. Dwi Rusjayanthi, “Seleksi Fitur Bobot Kata dengan Metode TFIDF untuk Ringkasan Bahasa Indonesia,” J. Ilm. Merpati (Menara Penelit. Akad. Teknol. Informasi), vol. 6, no. 2, p. 119, 2018, doi: 10.24843/jim.2018.v06.i02.p06.

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