Analisis Sentimen Terhadap Ulasan Hotel Melalui Platform Google Review Menggunakan Metode Stacking
Abstract
In the digital era, the internet has become the main source of information for the public, including when searching for hotel information. One platform frequently used for finding hotel information is Google Review, which allows hotel guests to share their experiences. This study aims to analyze sentiments towards hotel reviews, specifically for hotels in Jakarta, through Google Review to help customers choose hotels that meet their needs. The research method uses Serp API in Python to gather review data from Google Review, followed by data preprocessing and labeling with VADER. Sentiment classification is conducted using a stacking ensemble method. The stacking ensemble method in this study employs Naive Bayes, Random Forest, Logistic Regression, Support Vector Machine, K-Nearest Neighbor, and Extra Trees Classifier as base algorithms. The sentiment classification results are then evaluated using precision, F1-score and accuracy metrics. The research findings show that the stacking ensemble method with Naive Bayes, Random Forest, Logistic Regression, Support Vector Machine, K-Nearest Neighbor, and Extra Trees Classifier algorithms performs better than individual models. The precision, F1-Score and accuracy of the stacking ensemble method reach 91%, 97%, 98%, respectively.
Downloads
References
B. P. Statistik, “Tingkat Hunian Kamar Hotel Di Jakarta Pada Tahun 2022,” Badan Pusat Statistik Provinsi DKI Jakarta, 2022. https://jakarta.bps.go.id/publication/2023/09/15/0f4fa4442b1acfb5bca4a0f4/statistik-hotel-dan-tingkat-penghunian-kamar-hotel-provinsi-dki-jakarta-2022.html
B. pusat Statistik, “Jumlah Tamu Indonesia Pada Hotel Bintang (Ribu Orang), 2021-2022,” Badan Pusat Statistik, 2023. https://www.bps.go.id/id/statistics-table/2/MzI4IzI=/jumlah-tamu-indonesia-pada-hotel-bintang--ribu-orang-.html
UNWTO World Tourism Organization, “International tourism expected to decline over 70% in 2020, back to levels of 30 years ago,” UNWTO World Tour. Barom., vol. 18, no. 7, pp. 1–36, 2020, [Online]. Available: https://www.e-unwto.org/doi/epdf/10.18111/wtobarometereng.2020.18.1.7
M. Rizky and R. Andarsyah, “Klasifikasi MIT-BIH Arrhythmia Database Metode Random Forest dan CNN dengan Model ResNet-50: A Systematic Literature Review,” J. Teknol. Dan Sist. Inf. Bisnis, vol. 5, no. 3, pp. 190–196, 2023, doi: 10.47233/jteksis.v5i3.825.
F. V. Sari, “ANALISIS SENTIMEN PELANGGAN TOKO ONLINE JD . ID MENGGUNAKAN METODE NAÏVE BAYES CLASSIFIER BERBASIS KONVERSI IKON EMOSI,” vol. 10, no. 2, pp. 681–686, 2019.
M. R. Prasetyo and A. Fahrurozi, “Analisa Sentimen Pada Ulasan Google Untuk Hotel Gran Mahakam Jakarta Menggunakan Pendekatan Machine Learning,” J. Ilm. Inform. Komput., vol. 28, no. 3, pp. 203–217, 2023, doi: 10.35760/ik.2023.v28i3.9761.
Muyassar Akmal Iftikar and Yuliant Sibaroni, “Analisis Sentimen Twitter: Penanganan Pandemi Covid-19 Menggunakan Metode Hybrid Naïve Bayes, Decision Tree, dan Support Vector Machine,” e-Proceeding Eng., vol. 9, no. 3, pp. 1809–1826, 2022.
R. A. Firsttama, A. A. Arifiyanti, and D. S. Y. Kartika, “Analisis Sentimen Komentar Youtube Konferensi Tingkat Tinggi G20 Menggunakan Metode Naive Bayes,” J. Teknol. Dan Sist. Inf. Bisnis, vol. 6, no. 2, pp. 282–285, 2024, doi: 10.47233/jteksis.v6i2.1263.
A. Ramadhanu, M. R. Zaky, M. Isra, N. S. W. Nengsih, and S. Sularno, “Penerapan Machine Learning Untuk Menentukan Tingkat Kepuasan Tamu Hotel Dymens Menggunakan Metode Vader,” J. Teknol. Dan Sist. Inf. Bisnis, vol. 5, no. 3, pp. 337–343, 2023, doi: 10.47233/jteksis.v5i3.866.
C. J. Hutto and E. Gilbert, “VADER : A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text,” pp. 216–225.
B. Sunarko et al., “Penerapan Stacking Ensemble Learning untuk Klasifikasi Efek Kesehatan Akibat Pencemaran Udara,” Edu Komputika J., vol. 10, no. 1, pp. 55–63, 2023, doi: 10.15294/edukomputika.v10i1.72080.
W. Purbaratri, H. D. Purnomo, D. Manongga, I. Setyawan, and H. Hendry, “Sentiment Analysis of e-Government Service Using the Naive Bayes Algorithm,” MATRIK J. Manajemen, Tek. Inform. dan Rekayasa Komput., vol. 23, no. 2, pp. 441–452, 2024, doi: 10.30812/matrik.v23i2.3272.
F. Akbar, H. W. Saputra, and A. K. Maulaya, “Implementation of Decision Tree Algorithm C4 . 5 and Support Vector Regression for Stroke Disease Prediction Implementasi Algoritma Decision Tree C4 . 5 dan Support Vector Regression untuk Prediksi Penyakit Stroke,” vol. 2, no. October, pp. 61–67, 2022.
M. K. Anam, Y. Irawan, and M. Jamaris, “Comparison of support vector machine and XGBSVM in analyzing public opinion on Covid-19 vaccination,” vol. 14, no. 1, pp. 32–38, 2022.
D. Darwis, N. Siskawati, and Z. Abidin, “Penerapan Algoritma Naive Bayes untuk Analisis Sentimen Review Data Twitter BMKG Nasional,” vol. 15, no. 1, pp. 131–145.
Siti Khomsah, Rima Dias Ramadhani, and Sena Wijaya, “The Accuracy Comparison Between Word2Vec and FastText On Sentiment Analysis of Hotel Reviews,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 6, no. 3, pp. 352–358, 2022, doi: 10.29207/resti.v6i3.3711.
A. Q. Surbakti, R. Hayami, and J. Al-amien, “Jurnal Computer Science and Information Technology ( CoSciTech ),” vol. 2, no. 2, pp. 91–97, 2021.
A. W. Pradana and M. Hayaty, “The Effect of Stemming and Removal of Stopwords on the Accuracy of Sentiment Analysis on Indonesian-language Texts,” Kinet. Game Technol. Inf. Syst. Comput. Network, Comput. Electron. Control, vol. 4, no. 3, pp. 375–380, 2019, doi: 10.22219/kinetik.v4i4.912.
I. Irawanto, C. Widodo, A. Hasanah, P. A. Dharma Kusumah, K. Kusrini, and K. Kusnawi, “Sentiment Analysis and Classification of Forest Fires in Indonesia,” Ilk. J. Ilm., vol. 15, no. 1, pp. 175–185, 2023, doi: 10.33096/ilkom.v15i1.1337.175-185.
K. S. Nugroho, “Confusion Matrix Untuk Evaluasi Model Pada Supervised Learning,” Medium.com, 2019. https://ksnugroho.medium.com/confusion-matrix-untuk-evaluasi-model-pada-unsupervised-machine-learning-bc4b1ae9ae3f
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under an Attribution 4.0 International (CC BY 4.0) that allows others to share — copy and redistribute the material in any medium or format and adapt — remix, transform, and build upon the material for any purpose, even commercially with an acknowledgment of the work's authorship and initial publication in this journal.
