DOI of the published article https://doi.org/10.21070/pels.v3i0.1338
Sentiment Analysis Of OYO App Reviews Using The Support Vector Machine Algorithm
Analisis Sentimen Terhadap Ulasan Aplikasi OYO Menggunakan Algoritma Support Vector Machine
DOI:
https://doi.org/10.21070/ups.424Keywords:
OYO, Playstore, Support Vector Machine, RBFAbstract
The rapidly growing tourism industry causes the need for hotels to increase. This has led to innovation in the form of virtual hotel operators, one of which is OYO. OYO is one of the applications with millions of users. Of course, this cannot be separated from the ratings and reviews from users. The reviews contained in the playstore itself can contain positive, neutral or even negative opinions. This research classifies reviews on the OYO application to determine user sentiment. In this study, the data used is 2,000 data which will be classified into positive,neutral and negative sentiments. The Support Vector Machine algorithm was chosen because it's capable of producing high accuracy. Based on testing, the RadialBasisFunction kernel is able to produce the highest accuracy among other kernels and by using adataset division ratio of 90:10 the accuracy obtained is 90.96%. While testing using the ConfusionMatrix produces an accuracy of 89.83%
Downloads
References
E. Gunawan, G. O. Sebastian, and A. Harianto, “ANALISA PENGARUH KUALITAS LAYANAN
TERHADAP KEPUASANPELANGGAN MENGINAP DI EMPAT VIRTUAL HOTEL OPERATOR DI
SURABAYA,” Journal of Indonesian Tourism, Hospitality and Recreation, vol. 2, no. 2, pp. 145–153, 2019.
F. Kusumawati, “TREN VIRTUAL HOTEL OPERATOR (VHO) DI YOGYAKARTA (STUDI KASUS HOTEL OYO),” Media Wisata, vol. 18, no. 8, pp. 90–100, 2020, doi: 10.36275/mws.
A. C. Najib, A. Irsyad, G. A. Qandi, and N. A. Rakhmawati, “Perbandingan Metode Lexicon-based dan SVM untuk Analisis Sentimen Berbasis Ontologi pada Kampanye Pilpres Indonesia Tahun 2019 di Twitter,” Fountain of Informatics Journal, vol. 4, no. 2, p. 41, Nov. 2019, doi: 10.21111/fij.v4i2.3573.
R. Feldman and J. Sanger, The text mining handbook : advanced approaches in analyzing unstructured data. Cambridge University Press, 2007.
S. Sanjaya and E. A. Absar, “Pengelompokan Dokumen Menggunakan Winnowing Fingerprint dengan Metode K-Nearest Neighbour,” 2015.
D. K. Barupal and O. Fiehn, “Generating the blood exposome database using a comprehensive text mining and database fusion approach,” Environ Health Perspect, vol. 127, no. 9, Sep. 2019, doi: 10.1289/EHP4713.
Jerryl Jeovano, “2D Data Visualization Tools Menggunakan Flask dan AngularJS,” 2020.
H. Leidiyana, “PENERAPAN ALGORITMA K-NEAREST NEIGHBOR UNTUK PENENTUAN
RESIKO KREDIT KEPEMILIKAN KENDARAAN BEMOTOR,” 2013.
B. Sartono and U. D. Syafitri, “METODE POHON GABUNGAN: SOLUSI PILIHAN UNTUK
MENGATASI KELEMAHAN POHON REGRESI DAN KLASIFIKASI TUNGGAL,” Forum Statistika
dan Komputasi, vol. 15, no. 1, 2010.
G. A. Buntoro, “Analisis Sentimen Calon Gubernur DKI Jakarta 2017 Di Twitter,” 2016. [Online]. Available: https://t.co/jrvaMsgBdH
M. Windarti, “PERBANDINGAN KINERJA ALGORITMA NAIVE BAYES DAN BAYESIAN
NETWORK DALAM KLASIFIKASI MASA STUDI MAHASISWA,” 2018.
E. N. Arrofiqoh and H. Harintaka, “IMPLEMENTASI METODE CONVOLUTIONAL NEURAL NETWORK UNTUK KLASIFIKASI TANAMAN PADA CITRA RESOLUSI TINGGI,” GEOMATIKA, vol. 24, no. 2, p. 61, Nov. 2018, doi: 10.24895/jig.2018.24-2.810.
Downloads
Additional Files
Posted
License
Copyright (c) 2023 UMSIDA Preprints Server
This work is licensed under a Creative Commons Attribution 4.0 International License.
