Sentiment analysis on myindihome user reviews using support vector machine and naive bayes classifier method
DOI:
https://doi.org/10.12928/ijio.v2i2.4437Keywords:
Sentiment Analysis, myIndiHome, Support Vector Machine, Naïve Bayes ClassifierAbstract
In the era of globalization, the internet has become a human need in doing various things. Many internet users are an opportunity for internet service providers, PT Telekomunikasi Indonesia (Telkom). One of PT Telkom's products is IndiHome. As the only state-owned enterprise engaged in telecommunications, PT Telkom is expected to meet the needs of the Indonesian people. However, based on the rating obtained by IndiHome products through the myIndiHome application on Google Play, it is 3.5 out of 87,000 more reviews. The reviews focus on how important the effect of word-of-mouth is on choosing and using internet provider products. The review data was collected on November 1, 2020 to December 15, 2020, with a total of 2,539 reviews as a sample.  The sentiment analysis process that has been carried out shows that the number of reviews included in the negative sentiment class was 1.160 reviews, and the positive class was 1.374 reviews out of a total of 2,539 reviews. The results indicate that service errors in IndiHome services are still quite high, reaching 46.7% as indicated by the number of negative reviews. The classification results show that the average value of the total accuracy of the Support Vector Machine (SVM) method is 86.54% greater than Naïve Bayes Classifier (NBC) method which has an average total accuracy of 84.69%.  Based on fishbone diagram analysis, there are 12nd problems on negative reviews that classify problems 5P factors: Price, People, Process, Place, and Product.References
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Pratmanto, D., Rousyati, R., Wati, F., Widodo, A. E., Suleman, S., & Wijianto, R. (2020). App Review Sentiment Analysis Shopee Application In Google Play Store Using Naive Bayes Algorithm. Journal of Physics: Conference Series, 1641.
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Redhu, S., Srivastava, S., Bansal, B., & Gupta, G. (2018). Sentiment analysis using text mining: a review. International Journal on Data Science and Technology.
Sari, S. & Kalender, M. (2020). Sentiment Analysis and Opinion Mining Using Deep Learning for the Reviews on Google Play. The Proceedings of the Third International Conference on Smart City Applications. 4, 126-137.
Silaen, S. (2018). Metodologi Penelitian Sosial Untuk Penulisan Skripsi dan Tesis . Bandung: In Media.
Sugiyono. (2015). Metode Penelitian Kombinasi (Mix Methods). Bandung: Alfabeta.
Al-Smadi, M. (2018). Deep Recurrent Neural Network vs. Support Vector Machine for Aspect-based Sentiment Analysis of Arabic Hotels’ Reviews. Journal of Computational Science, 27, 386-393.
Alwasi'a, A. (2020). Analisis Sentimen Pada Review Aplikasi Berita Online Menggunakan Metode Maximum Entropy (Studi Kasus: Review Detik.com pada Google Play 2019) [Skripsi]. Yogyakarta: Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Islam Indonesia.
Ardiansyah, A., Aji, S., Pratmanto, D., Kuryanti, S. J., Putra, O. P., & Adiwihardja, C. (2020). The Analysis of Digital Provider Sentiment of ‘by.u’ on Google Play Which Uses The Support Vector Machine (SVM) Method. Journal of Physics: Conference Series. 1641, 1-6.
Arrofiqoh, E. N., & Harintaka. (2018). Implementasi Metode Convolutional Neural Network Untuk Klasifikasi Tanaman Pada Citra Resolusi Tinggi. Jurnal Ilmiah Geomatika, 24(2), 61-68.
Azhar, Y. (2017). Metode Lexicon-Learning Based Untuk Identifikasi Tweet Opini Berbahasa Indonesia. Jurnal Nasional Pendidikan Teknik Informatika, 6(3), 237-243.
Baid, P., Gupta, A., & Chaplot, N. (2017). Sentiment analysis of movie reviews using machine learning techniques. International Journal of Computer Applications. 7(1), 383-385.
Buntoro, G. A. (2017). Analisis Sentimen Calon Gubernur DKI Jakarta 2017 di Twitter. INTEGER: Journal of Information Technology, 2(1), 32-41.
Chauhan C., S. S. (2017). Sentiment Analysis On Product Reviews. International Conference on Computing, Communication and Automation (ICCCA). 26-31.
Dadgar, S. M. H., Araghi, M. S., & Farahani, M. M. (2016). A Novel Text Mining Approach Based on TF-IDF and Support Vector Machine for News Classification. IEEE International Conference on Engineering and Technology (ICETECH). 112-116.
Diba, S. F. (2020). Implementation of Naive Bayes Classification Method for Sentiment Analysis on Community Opinion to Indonesian Criminal Code Draft. Proceedings of the 2nd International Seminar on Science and Technology (ISSTEC 2019). 474, 186-192.
Fransiska, S., Rianto, R., & Gufroni, A. I. (2020). Sentiment Analysis Provider by. U on Google Play Store Reviews with TF-IDF and Support Vector Machine (SVM) Method. Scientific Journal of Informatics. 7(2), 203-212.
Garcia, K., & Berton, L. (2020). Topic detection and sentiment analysis in Twitter content related to COVID-19 from Brazil and the USA. Applied Soft Computing Journal. 101, 1-25.
Gumilang, Z. (2018). Implementasi Naive-Bayes Classifier dan Asosiasi Untuk Analisis Sentimen Data Ulasan Aplikasi E-commerce Shopee pada Situs Google Play [Skripsi]. Yogyakarta: Program Studi Statistika FMIPA Universitas Islam Indonesia.
Handani, S. W., & Saputra, D. I. (2019). Sentiment Analysis for Go-Jek on Google Play Store. Journal of Physics: Conference Series. 1196.
Indonesia Top Brand Award. (2020). Tob Brand Index. Retrieved Desember 20, 2020, from https://www.topbrand-award.com/en/top-brand-index-int/?tbi_find=IndiHome
Indonesian Internet Service Providers Association . (2020, Juni). Laporan Survey Internet APJII 2019-2020 (Q2). Retrieved Desember 12, 2020, from https://apjii.or.id/survei2019x/download/BRTdltugLoJEG1wWiSrKZxeb0HDCQ5
Indriati, I. & Ridok, A. (2016). Sentiment Analysis For Review Mobile Applications Using Neighbor Method Weighted K-Nearest Neighbor (Nwknn). Journal of Environmental Engineering and Sustainable Technology, 3(1). 23-32.
Kuntoro, A. Y., Asra, T., Pratama, E. B., Effendi, L., & Ocanitra, R. (2020). Gojek and Grab User Sentiment Analysis on Google Play Using Naive Bayes Algorithm And Support Vector Machine Based Smote Technique. Journal of Physics: Conference Series, 1641.
Luoa, T., Wu, C., & Duan, L. (2017). Fishbone Diagram and Risk Matrix Analysis Method and Ats Application Fishbone Diagram and Risk Matrix Analysis Method and Its Application. Journal of Cleaner Production, 174, 296-304.
Muhammad, I., & Yan, Z. (2015). Supervised Machine Learning Approach: A Survey. International Journal of Soft Computing, 5(3), 946-952.
Oktaviani, V., Warsito, B., Yasin, H., & Santoso, R. (2021). Sentiment analysis of e-commerce application in Traveloka data review on Google Play site using Naïve Bayes classifier and association method. Journal of Physics: Conference Series, 1943.
Patel, R., & Passi, K. (2020). Sentiment Analysis on Twitter Data of World Cup Soccer Tournament Using Machine Learning. Internet of Things Journal, 1(2), 218-239.
Pratmanto, D., Rousyati, R., Wati, F., Widodo, A. E., Suleman, S., & Wijianto, R. (2020). App Review Sentiment Analysis Shopee Application In Google Play Store Using Naive Bayes Algorithm. Journal of Physics: Conference Series, 1641.
Putri, B. A. (2019). Analisis Sentimen Data Ulasan Pengguna Grab Menggunakan Metode Support Vector Machine dan Maximum Entropy. [Skripsi]. Yogyakarta: Fakultas Teknologi Industri Universitas Islam Indonesia.
Putri, B. A., Khasanah, A. U., & ‘Azzam, A. (2019). Sentiment Analysis on Grab User Reviews Using Support Vector Machine and Maximum Entropy Methods. International Conference on Information and Communications Technology, 468-473.
Redhu, S., Srivastava, S., Bansal, B., & Gupta, G. (2018). Sentiment analysis using text mining: a review. International Journal on Data Science and Technology.
Sari, S. & Kalender, M. (2020). Sentiment Analysis and Opinion Mining Using Deep Learning for the Reviews on Google Play. The Proceedings of the Third International Conference on Smart City Applications. 4, 126-137.
Silaen, S. (2018). Metodologi Penelitian Sosial Untuk Penulisan Skripsi dan Tesis . Bandung: In Media.
Sugiyono. (2015). Metode Penelitian Kombinasi (Mix Methods). Bandung: Alfabeta.
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2021-09-01
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Hakim, S. N., Putra, A. J., & Khasanah, A. U. (2021). Sentiment analysis on myindihome user reviews using support vector machine and naive bayes classifier method. International Journal of Industrial Optimization, 2(2), 151–164. https://doi.org/10.12928/ijio.v2i2.4437
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