Implementation of the c4.5 decision tree learning algorithm for sentiment analysis in e-commerce application reviews on google play store
DOI:
https://doi.org/10.31315/cip.v1i1.6128Abstract
Objective: Forknowing the level of accuracy of the C4.5 Decision Tree Learning Algorithm in sentiment analysis of reviews of e-commerce applications on the google play store.
Design/method/approach: Using C4.5 AlgorithmDecision Tree Learning.
Results: This study uses a confusion matrix test with a comparison of 80% for training data and 20% for test data, where 750 is used for training data and 190 is used for test data. This test obtained an average accuracy of 92.63%, precision 69.58%, and recall 69.99%.
Authenticity/state of the art: In this study using the C4.5 Algorithm Decision Tree Learningto conduct sentiment analysis of e-commerce reviews, which use the gain value to perform feature selection. There are four categories, namely display, service, access, and product. The data in this study were obtained from the google play store.
References
Y. Jahja, “Pengaruh service performance value, emotional value, monetary value, social value terhadap customer loyalty melalui perceived value dan customer satisfaction pada konsumen zalora di Surabaya,” pp. 1–13, 2018.
Ilmawan, “Aplikasi Mobile untuk Analisis Sentimen pada Google Play,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 9, no. 1, pp. 53–64, 2015.
A. Kumar and Y. K. Kim, “The store-as-a-brand strategy: The effect of store environment on customer responses,” J. Retail. Consum. Serv., vol. 21, no. 5, pp. 685–695, 2014.
Murphy, Rosie. 2020. “Local Consumer Review Survey: How Customer Reviews Affect Behavior", https://www.brightlocal.com/Research/Local-Consumer-Review-Survey/ .
F. Gunawan, M. A. Fauzi, and P. P. Adikara, “Analisis Sentimen Pada Ulasan Aplikasi Mobile Menggunakan Naive Bayes dan Normalisasi Kata Berbasis Levenshtein Distance (Studi Kasus Aplikasi BCA Mobile),” Syst. Inf. Syst. Informatics J., vol. 3, no. 2, pp. 1–6, 2017.
H. Zulfa, “ANALISIS PENGARUH PERSEPSI RISIKO, KUALITAS SITUS WEB, DAN KEPERCAYAAN KONSUMEN TERHADAP KEPUTUSAN PEMBELIAN KONSUMEN E-COMMERCE SHOPEE DI KOTA SEMARANG,” JSHP J. Sos. Hum. dan Pendidik., vol. 4, no. 2, pp. 1–11, 2020.
A. Rakhman and M. R. Tsani, “Analisis Sentimen Review Media Massa,” Smart Comp, vol. 8, no. 2, pp. 78–82, 2019.
Z. A. Gumilang, “IMPLEMENTASI NAÏVE BAYES CLASSIFIER DAN ASOSIASI UNTUK ANALISIS SENTIMEN DATA ULASAN APLIKASI E-COMMERCE SHOPEE PADA SITUS GOOGLE PLAY TUGAS,” 經濟研究, 2018.
M. Lailiyah, “Sentiment Analysis Menggunakan Rule Based Method Pada Data Pengaduan Publik Berbasis Lexical Resources,” 2017.
And M. A. F. P. Antinasari, R. S. Perdana, “‘Analisis Sentimen Tentang Opini Film Pada Dokumen Twitter Berbahasa Indonesia Menggunakan Naive Bayes Dengan Perbaikan Kata Tidak Baku,’” מים והשקייה, vol. 549, no. 12, pp. 40–42, 2017.
Y. Kustiyahningsih and E. Rahmanita, “Aplikasi Sistem Pendukung Keputusan Menggunakan Algoritma C4.5. untuk Penjurusan SMA,” J. Semantec, vol. 5, no. 2, pp. 101–108, 2016.
Mulholland, “Application of the C4 . 5 classifier to building an expert system for ionchromatography, 27, 95–104,” עלון הנוטע, vol. 66, no. September, pp. 37–39, 1995.
And P. V. C. P. N. Patil, P. R. Lathi, “‘Customer Card Classification Based on C5 . 0 & CART Algorithms,’” עלון הנוטע, vol. 66, no. 3, pp. 37–39, 2012.
J. Han, J., Kamber, M., & Pei, “Data Mining Concepts and Techniques Third Edition. Waltham: Elsevier Inc.,” p. 2011, 2011.
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