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Sentiment Analysis of JNE User Perception using Naïve Bayes Classifier Algorithm
Abstract
The logistics industry is growing very rapidly. One of big industry in Indonesia is PT. Tiki Line Nugraha Ekakurir (JNE), which has been established for 29 years. This company has an extensive network in all cities in Indonesia, with service points of 1,500 locations. JNE has an application called my JNE on Google Play, which received more than 86,000 reviews and since December 2019 only got a rating of 2.4 stars out of a total rating of 5 stars. This study is obtained to analysis JNE user review data from Google Play. The reviews used in this study totaled 1,876 classified into positive and negative sentiment classes using the Naïve Bayes Classifier algorithm and word associations were also implemented. Classification with naïve bayes classifier with 90% training data and 10% test data had the best accuracy of 85.87%. Furthermore, for the text association, information is obtained that JNE users are talking about "send", "package", "courier", "good", "application", "fast", "service", "receive", "help", and "star". Whereas in the class of negative sentiment users often talk about "send", "package", "courier", "disappointed", "service", "service", "bad", "application", "severe", and "slow".
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Aditya. (2015). Penggunaan Web Crawler Untuk Menghimpun Tweet Dengan Metode Pre-Processing Text Mining. Jurnal Infotel, 7.
APJII. (2018). Hasil Survei Penetrasi dan Perilaku Pengguna Internet Indonesia 2018. Retrieved December 10, 2019, from https://www.apjii.or.id/content/read/39/4 10/Hasil-Survei-Penetrasi-dan-PerilakuPengguna-Internet-Indonesia-2018
Award, T. B. (2020). Top Brand Index. Retrieved from https://www.topbrandaward.com/top-brand
Azalia, F. Y., Bijaksana, M. A., & Huda, A. F. (2019). Name Indexing in Indonesian Translation of Hadith using Named Entity Recognition with Naïve Bayes Classifier. Procedia Computer Science, 157, 142–149. https://doi.org/10.1016/J.PROCS.2019.08.151
Barfian, E., Iswanto, B. H., & Isa, S. M. (2017). Twitter Pornography Multilingual Content Identification Based on Machine Learning. Procedia Computer Science, 116, 129–136. https://doi.org/10.1016/J.PROCS.2017.10.024
Dey, L., Chakraborty, S., Biswas, A., Bose, B., & Tiwari, S. (2016). Sentiment Analysis of Review Datasets Using Naïve Bayes‘ and K-NN Classifier. International Journal of Information Engineering and Electronic Business, 8(4), 54–62. https://doi.org/10.5815/ijieeb.2016.04.07
El-Masri, M., Altrabsheh, N., Mansour, H., & Ramsay, A. (2017). A web-based tool for Arabic sentiment analysis. Procedia Computer Science, 117, 38–45. https://doi.org/10.1016/J.PROCS.2017.10.092
Fanani. (2017). Klasifikasi Review Software Pada Google Play Menggunakan Pendekatan Analisis Sentimen. Universitas Gadjah Mada.
Ibrohim, M. O., & Budi, I. (2018). A Dataset and Preliminaries Study for Abusive Language Detection in Indonesian Social Media. Procedia Computer Science, 135, 222–229. https://doi.org/10.1016/j.procs.2018.08.169
Josi, A., Abdillah, L., & Suryayusra. (2014). Penerapan Teknik Web Scraping Pada Mesin Pencari Artikel Ilmiah. Jurnal Sistem Informasi (SISFO), 05.
Jumeilah, F. S. (2018). Klasifikasi Opini Masyarakat Terhadap Jasa Ekspedisi JNE dengan Naïve Bayes. Jurnal Sistem Informasi Bisnis, 8(1), 92. https://doi.org/10.21456/vol8iss1pp92-98
Kristiyanti, D. A. (2015). Analisis sentimen review produk kosmetik melalui komparasi feature selection. Konferensi Nasional Ilmu Pengetahuan Dan Teknologi (KNIT), 2(2), 74–81.
Kunal, S., Saha, A., Varma, A., & Tiwari, V. (2018). Textual Dissection of Live Twitter Reviews using Naive Bayes. Procedia Computer Science, 132, 307–313. https://doi.org/10.1016/J.PROCS.2018.05.182
Liu, J., Tian, Z., Liu, P., Jiang, J., & Li, Z. (2016). An approach of semantic web service classification based on naive bayes. Proceedings - 2016 IEEE International Conference on Services Computing, SCC 2016, 356–362. https://doi.org/10.1109/SCC.2016.53
Mahadzir, N. H., Omar, M. F., & Nawi, M. N. M. (2018). A Sentiment Analysis Visualization System for the Property Industry. International Journal of Technology, 9(8), 1609. https://doi.org/10.14716/ijtech.v9i8.2753
Mukherjee, S., & Bala, P. K. (2017). Sarcasm detection in microblogs using Naïve Bayes and fuzzy clustering. Technology in Society, 48, 19–27. https://doi.org/10.1016/j.techsoc.2016.10.003
Putri, D. (2016). Implementasi Inferensi Fuzzy Mamdani Untuk Keperluan Sistem Rekomendasi Berita Berbasis Konten. Universitas Gadjah Mada.
Raksanagara, R., Chrisnanto, Y. H., & Hadiana, A. I. (2016). Analisis Sentimen Jasa Ekspedisi Barang Menggunakan Metode Naïve Bayes. Analisis Sentimen Jasa Ekspedisi Barang Menggunakan Metode Naive Bayes, 19–24.
Rozi, I. F., Pramono, S. H., & Dahlan, E. A. (2012). Implementasi Opinion Mining ( Analisis Sentimen ) untuk Ekstraksi Data Opini Publik pada Perguruan Tinggi. Electrical Power, Electronics, Communications, Controls, and Informatics Seminar (EECCIS), 6(1), 37– 43.
Sánchez-Franco, M. J., Navarro-García, A., & Rondán-Cataluña, F. J. (2019). A naive Bayes strategy for classifying customer satisfaction: A study based on online reviews of hospitality services. Journal of Business Research, 101, 499–506. https://doi.org/10.1016/J.JBUSRES.2018.12.051
Wati, R. (2016). Penerapan Algoritma Genetika Untuk Seleksi Fitur Pada Analisis Sentimen Review Jasa Maskapai Penerbangan Menggunakan Naive Bayes Risa. Jurnal Evolusi Volume, 4. https://doi.org/10.1017/CBO9781107415 324.004
DOI: https://doi.org/10.31315/opsi.v15i1.7179
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