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Lembaga Penelitian Pengabdian Masyarakat Universitas Nusa Mandiri
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KOMPARASI ALGORITMA KLASIFIKASI PADA ANALISIS REVIEW HOTEL
Keywords:
Naïve Bayes, Support Vector Machine, K-Nearest Neighbor, K-NN, SVM, Hotel
Abstract
Pada saat ini kebebasan menyampaikan pendapat dalam bentuk lisan maupun tulisan tentang segala hal sudah sangat mudah. Kegiatan ini dapat dimanfaatkan untuk mengambil keputusan oleh sebagian pelaku bisnis. Khususnya oleh penyedia jasa, seperti hotel. Hal itu akan sangat bermanfaat dalam perkembangan bisnis hotel itu sendiri. Namun data review tersebut harus diolah menggunakan algoritma yang tepat. Maka penelitian ini dilakukan untuk mengetahui algoritma yang lebih layak digunakan untuk mendapatkan akurasi yang paling tinggi. Adapun metode yang digunakan adalah Naïve Bayes (NB), Support Vector Machine (SVM), dan k-Nearest Neighbor (k-NN). Dari proses yang telah dilakukan didapatkan hasil akurasi Naïve Bayes adalah 71,50% dengan nilai AUC adalah 0,500, Support Vector Machine adalah 72,50% dengan nilai AUC adalah 0,936 dan hasil akurasi jika menggunakan algoritma k-Nearest Neighbor adalah 75,00% dengan nilai AUC adalah 0,500. Penggunaan algoritma k-Nearest Neighbor dapat membantu dalam pengambilan keputusan yang lebih tepat untuk review hotel pada saat ini.
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References
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Chandani, V. (2015). Komparasi Algoritma Klasifikasi Machine Learning Dan Feature Selection pada Analisis Sentimen Review Film, 1(1), 55–59.
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
Dirsehan, T. (2016). Text Mining in the Hospitality Sector To Extend the Motivation Theory, (March). Retrieved from https://www.researchgate.net/profile/Taskin_Dirsehan/publication/298070052_Text_Mining_in_the_Hospitality_Sector_to_Extend_the_Motivation_Theory/links/56e5dde408ae68afa112b709.pdf
Elango, V., & Narayan, G. (2011). Sentiment Analysis for Hotel Reviews. Proceedings of the Computational Linguistics-Applications Conference, 231527(March), 45–52. https://doi.org/10.1051/matecconf/20167503002
Liu, B. (2012). Sentiment Analysis and Opinion Mining, (May).
Muthia, D. A. (2015). SNIPTEK 2015 OPINION MINING PADA REVIEW HOTEL MENGGUNAKAN ALGORITMA Seminar Nasional Ilmu Pengetahuan dan Teknologi Komputer Nusa Mandiri Seminar Nasional Ilmu Pengetahuan dan Teknologi Komputer Nusa Mandiri, 735–740.
Sipayung, E. M., Maharani, H., & Zefanya, I. (2016). Perancangan Sistem Analisis Sentimn Komentar Pelanggan Menggunakan Metde Naive Bayes Classifer. Jurnal Sistem Informasi (JSI), 8(1), 958–965.
Sodanil, M. (2016). Multi-Language Sentiment Analysis for Hotel Reviews. MATEC Web of Conferences, 75, 3002. https://doi.org/10.1051/matecconf/20167503002
Tian, X., Tao, R., He, W., & Akula, V. (2016). Mining online hotel reviews: A case study from hotels in China. AMCIS 2016: Surfing the IT Innovation Wave - 22nd Americas Conference on Information Systems, (2011), 1–8.
Yordanova, S., & Kabakchieva, D. (2017). Sentiment Classification of Hotel Reviews in Social Media with Decision Tree Learning. International Journal of Computer Applications, 158(August), 7. https://doi.org/10.5120/ijca2017912806
Published
2018-09-15
How to Cite
Utami, L., Rachmi, H., & Nurlaela, D. (2018). KOMPARASI ALGORITMA KLASIFIKASI PADA ANALISIS REVIEW HOTEL. Jurnal Pilar Nusa Mandiri, 14(2), 261-266. https://doi.org/10.33480/pilar.v14i2.77
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