Analisis Sentimen Ulasan Hotel Zuri Express Lippo Cikarang Menggunakan Algoritma Naive Bayes
Abstract
The hospitality industry relies heavily on customer satisfaction, which is often reflected through reviews on online travel applications. Hotel Zuri Express Lippo Cikarang requires an automated sentiment monitoring system to enhance its service quality. This study aims to implement the Naive Bayes Classifier algorithm to classify customer review sentiments into positive and negative categories. The research dataset consists of 1,637 reviews obtained via web scraping from the google maps. The research methodology includes text preprocessing, Term Frequency-Inverse Document Frequency (TF-IDF) weighting, and N-Gram (Bigram) feature modeling. The experimental results demonstrate that the model achieves an accuracy rate of 91.16%, with a precision of 0.91 and a recall of 0.99 for the majority class. Keyword analysis using Word clouds identified cleanliness and staff service as the primary factors driving customer satisfaction. Despite challenges in detecting negative sentiments within an imbalanced dataset, the Naive Bayes algorithm proved to be reliable and efficient for automated sentiment analysis, providing a robust tool to support fast and accurate management decision-making.
Keywords: Sentiment Analysis; Customer Reviews; Hotel Zuri Express; Naive Bayes; Text mining.
Abstrak
Industri perhotelan sangat bergantung pada kepuasan pelanggan yang tercermin melalui ulasan aplikasi travel online. Hotel Zuri Express Lippo Cikarang memerlukan sistem otomatis untuk memantau sentimen guna meningkatkan kualitas layanan. Penelitian ini bertujuan mengimplementasikan algoritma Naive Bayes Classifier dalam mengklasifikasikan sentimen ulasan pelanggan ke kategori positif dan negatif. Data penelitian mencakup 1.637 ulasan hasil web scraping dari google maps. Tahapan penelitian meliputi preprocessing teks, pembobotan kata Term Frequency-Inverse Document Frequency (TF-IDF), serta pemodelan fitur N-Gram (Bigram). Hasil pengujian menunjukkan model menghasilkan tingkat akurasi sebesar 91,16%, dengan nilai presisi 0,91 dan recall 0,99 pada kelas mayoritas. Analisis kata kunci melalui Word cloud mengidentifikasi aspek kebersihan dan pelayanan staf sebagai faktor utama kepuasan pelanggan. Meskipun terdapat tantangan dalam mendeteksi sentimen negatif pada dataset tidak seimbang, algoritma Naive Bayes terbukti handal dan efisien dalam melakukan analisis sentimen otomatis untuk mendukung pengambilan keputusan manajemen hotel secara cepat dan akurat.
Kata kunci: Analisis Sentimen; Ulasan Pelanggan; Hotel Zuri Express; Naive Bayes; Text mining.
References
S. Munir and E. A. A. D. I. A, "Analisis Sentimen Pada Review Hotel Menggunakan Metode Pembobolan dan Klasifikasi," JNANALOKA, vol. 2, no. 1, pp. 33-38, 2022.
T. Rachman and R. M. Hasan, "Digitalisasi Layanan Pelanggan di Industri Perhotelan Indonesia: Studi Kasus Hotel Berbintang," Jurnal Sistem Informasi dan Pariwisata, vol. 4, no. 10, pp. 41-50, 2022.
R. A. M. Suryadi, "Analisis Sentimen Review Hotel Menggunakan Algoritma Naive Bayes Classifier," TECHSI - Jurnal Teknik Informatika, vol. 11, no. 13, pp. 90-95, 2021.
L. Maharani and T. W. D, "Analisis Brand Positioning Resort Bintang Lima Kabupaten Malang Di Platform Traveloka," JAMBURA, vol. 6, no. 7, pp. 50-55, 2024.
R. F. H. Syahputra and A. D, "Pengaruh Review dan Rating Terhadap Loyalitas Pelanggan Hotel Berbasis Aplikasi Online," Jurnal Sistem Informasi dan Manajemen Bisnis Digital, vol. 2, no. 6, pp. 33-41, 2023.
A. Marwan and G. A, "Analisis Reputasi Digital Hotel Berbasis Review Online Menggunakan Text mining dan Sentiment Analysis," Jurnal Teknologi Informasi dan Komunikasi, vol. 7, no. 9, pp. 91-98, 2023.
A. Arfan and M. M, "Analisis pengaruh Ulasan Pelanggan Terhadap Keputusan Menginap di Hotel berbasis E-Commerce," Jurnal Teknologi Informasi dan Terapan, vol. 8, no. 2, pp. 115-121, 2021.
E. Wibowo and P. I, "Analisis Sentimen Terhadap Ulasan Hotel Melalui Platform Google Review Menggunakan Metode Stacking," Jurnal Teknologi dan Sistem Informasi Bisnis, vol. 6, no. 3, pp. 774-784, 2024.
D. Yuliani and H. D, "Pengolahan Data Teks Ulasan Konsumen Menggunakan Text mining dan Analisis Sentimen," Jurnal Komputer dan Informatika, vol. 8, no. 18, pp. 55-61, 2020.
M. Azizah and P. A, "Pengaruh Preprocessing Teks terhadap Akurasi Analisis Sentimen Ulasan Produk Berbahasa Indonesia," Jurnal Informatika dan Sistem Informasi, vol. 3, no. 10, pp. 17-25, 2022.
CNN Indonesia, "Jumlah Wisman ke Indonesia Januari-September 2024 Capai 10.37 juta," CNN Indonesia, vol. 1, no. 1, pp. 1-2, 4 November 2024.
A. Pramana and R. Yusuf, "Implementasi Naive Bayes pada Analisis Sentimen Ulasan Hotel Berbahasa Indonesia," Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 10, no. 2, pp. 120-128, 2023.
D. Pradana and F. Setiawan, "Perbandingan Algoritma Machine learning pada Analisis Sentimen Review Hotel," Jurnal Media Informatika Budidarma, vol. 7, no. 4, pp. 1880-1888, 2023.
R. Kurniawan, A. Wijaya, and M. Saputra, "Evaluasi Kinerja Naive Bayes, KNN dan SVM pada Sentiment Analysis Pariwisata," Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 8, no. 1, pp. 55-63, 2024.
J. Rennie, L. Shih, J. Teevan, and D. Karger, "Tackling the Poor Assumptions of Naive Bayes Text Classifiers," Proceedings of the 20th International Conference on Machine learning (ICML), vol. 20, no. 1, pp. 616-623, 2003.
D. N. Prayogi and S. A, "Implementasi Naive Bayes Classifier dan Confusion matrix pada Analisis Sentimen Berbasis Teks pada Twitter," Jurnal Informatika, vol. 5, no. 17, pp. 345-355, 2021.
G. F. N. Duei Putri and W. E. S. D, "Analisis Sentimen Kinerja Dewan Perwakilan Rakyat pada Twitter Menggunakan Metode Naive Bayes Classifier," Jurnal Informatika dan Teknik Elektro Terapan, vol. 10, no. 3, pp. 275-285, 2022.
F. A. M. Ramadani and M. S, "Klasifikasi Sentimen Ulasan Pengguna Marketplace Tokopedia Menggunakan Naive Bayes dan TF-IDF," Jurnal Teknik Informatika, vol. 4, no. 9, pp. 101-109, 2023.
M. Rahman, I. Firmansyah, dan D. Putra, "Sentiment Classification on Hotel Reviews using Probabilistic Approach," Journal of Data Science and Information Systems, vol. 5, no. 2, pp. 77-86, 2023.
S. Putri dan R. Wijaya, "Pengaruh TF-IDF terhadap Akurasi Analisis Sentimen Bahasa Indonesia," Jurnal Informatika, vol. 11, no. 1, pp. 31-39, 2024.
A. Saputra, F. Ramadhan, dan L. Hakim, "Implementasi N-Gram pada Analisis Sentimen Ulasan Pelanggan," Jurnal Teknologi Komputer, vol. 9, no. 3, pp. 201-209, 2024.
N. Hidayat dan B. Nugroho, "Challenges of Sarcasm Detection in Indonesian Sentiment Analysis," Journal of Artificial Intelligence Research, vol. 6, no. 1, pp. 15-24, 2023.
How To Cite This :
Refbacks
- There are currently no refbacks.
