Analisis Sentimen Kepuasan Pengguna OYO DiPlaystore Dengan Multinoial Naive Bayes dan Chi-square
DOI:
https://doi.org/10.37859/jf.v14i1.6943
Abstract
ABSTRACK
Opinions play a crucial role in everyday life, significantly influencing human behavior and decisions. Especially in the context of business and organizations, consumer opinions about products and services are highly valuable. This study focuses on analyzing the sentiment of OYO application reviews on the Google Play Store, with the goal of classifying reviews as either positive or negative. OYO Hotels & Homes, a startup company in the accommodation sector originating from India, has achieved remarkable success with revenues reaching US$951 million in fiscal year 2019. The primary classification method used is Multinomial Naïve Bayes, which is an approach in supervised learning, along with Chi-Square feature selection to explore correlations between factors influencing user satisfaction. The research process includes data collection of reviews, preprocessing, labeling, and data splitting. Subsequently, TF-IDF weighting and Chi-Square feature selection are performed. The results of sentiment analysis indicate a dominance of positive reviews, reflecting user satisfaction with OYO services. The classification process uses the Multinomial Naïve Bayes algorithm, with an accuracy rate of 85.5% without feature selection, increasing to 87.00% with Chi-Square feature selection. These results demonstrate the effectiveness of the Multinomial Naïve Bayes algorithm and the importance of feature selection in sentiment analysis. Through a deeper understanding of user sentiment, companies can enhance service quality and respond to feedback more effectively, ensuring optimal customer satisfaction. This research has broad implications for sentiment analysis and the use of statistical methods to address complex issues in the technology industry.
Keywords: Sentiment Analysis, OYO Application, Google Playstore, Multinomial Naïve Bayes, Chi-Square Feature Selection.
AbstrakOpini memainkan peran krusial dalam kehidupan sehari-hari, memengaruhi perilaku dan keputusan manusia secara signifikan. Terutama dalam konteks bisnis dan organisasi, pendapat konsumen tentang produk dan layanan sangatlah berharga. Penelitian ini berfokus pada analisis sentimen ulasan aplikasi OYO di Google Playstore, dengan tujuan mengklasifikasikan ulasan menjadi positif atau negatif. OYO Hotels & Homes, sebuah perusahaan startup di sektor akomodasi yang berasal dari India, telah mencapai kesuksesan luar biasa dengan pendapatan mencapai US$951 juta pada tahun fiskal 2019. Metode klasifikasi utama yang digunakan adalah Multinomial Naïve Bayes, yang merupakan pendekatan dalam pembelajaran terawasi dan seleksi fitur Chi-Square untuk mengeksplorasi korelasi antara faktor-faktor yang memengaruhi kepuasan pengguna. Proses penelitian meliputi pengumpulan data ulasan, preprocessing, labeling, dan pembagian data. Selajutnya dilakukan pembobotan TF-IDF dan seleksi fitur Chi-Square. Hasil analisis sentimen memperlihatkan dominasi ulasan positif, menunjukkan kepuasan pengguna terhadap layanan OYO. Proses klasifikasi menggunakan algoritma Multinomial Naïve Bayes, dengan hasil akurasi model tanpa seleksi fitur sebesar 85.5%, meningkat menjadi 87.00% dengan seleksi fitur Chi-Square. Hasil ini menunjukkan efektivitas algoritma Multinomial Naïve Bayes dan pentingnya seleksi fitur dalam analisis sentimen. Melalui pemahaman yang lebih dalam terhadap sentimen pengguna, perusahaan dapat meningkatkan kualitas layanan dan merespons umpan balik dengan lebih baik, memastikan kepuasan pelanggan yang optimal. Penelitian ini memiliki implikasi luas dalam analisis sentimen dan penggunaan metode statistik untuk mengatasi masalah kompleks dalam industri teknologi.
Downloads
References
S. Bhatia, M. Sharma, dan K. K. Bhatia, “Sentiment Analysis and Mining of Opinions,” Stud. Big Data, vol. 30, no. May, hal. 503–523, 2018, doi: 10.1007/978-3-319-60435-0_20.
Z. Zaenal dan I. R. I. Astutik, “Sentiment Analysis of OYO App Reviews Using the Support Vector Machine Algorithm,” Procedia Eng. Life Sci., vol. 3, no. December, 2023, doi: 10.21070/pels.v3i0.1338.
A. Kusumawardani, “Pendapatan Meroket, OYO Tetap Merugi US$335 Juta,” Bisnis.com, 2020.
Rufman Iman Akbar, “Manajemen Hubungan Pelanggan (CRM),” hal. 44, 2021.
ANDHIKA, “OYO Melakukan Pembatalan Sepihak Atas Pesanan Kamar Hotel yang Telah Dibayar,” Kompas.com, 2023.
N. B. Sidauruk dan N. Riza, “SENTIMEN ANALISIS DATA PENGGUNA TERHADAP KAI ACCESS Systematic Literature Review,” vol. 7, no. 2, hal. 1297–1303, 2023.
A. E. Putra, “Pengaruh Seleksi Fitur Chi-Square Terhadap Kinerja Algoritma Naïve Bayes Classifier,” hal. 16–17, 2018.
F. A. Wenando et al., “Jurnal Computer Science and Information Technology ( CoSciTech ) Analyzing Public Sentiment on the Brigadier J Shooting Case Using the Naïve Bayes Classifier Algorithm,” vol. 4, no. 2, hal. 484–490, 2023.
Sriani, Suhardi, dan Irsan Frianda Gultom, “Analisis Sentimen Kebijakan Pemberian Subsidi Motor Listrik Menggunakan Metode Support Vector Machine,” Fasilkom, vol. 13, no. 3, hal. 511–517, 2023.
Fajri Koto dan Gemala Y. Rahmaningtyas, “InSet Lexicon: Evaluation of a Word List for Indonesian Sentiment Analysis in Microblogs,” 2017 Int. Conf. Asian Lang. Process., hal. 391–394, 20M.
D. Mualfah, Ramadhoni, R. Gunawan, dan D. Mulyadipa Suratno, “Analisis Sentimen Komentar YouTube TvOne Tentang Ustadz Abdul Somad Dideportasi Dari Singapura Menggunakan Algoritma SVM,” J. Fasilkom, vol. 13, no. 01, hal. 72–80, 2023, doi: 10.37859/jf.v13i01.4920.
M. Nurjannah dan I. F. Astuti, “PENERAPAN ALGORITMA TERM FREQUENCY-INVERSE DOCUMENT FREQUENCY ( TF-IDF ) UNTUK TEXT MINING,” vol. 8, no. 3, hal. 110–113, 2013.
T. Ernayanti, M. Mustafid, A. Rusgiyono, dan A. R. Hakim, “Penggunaan Seleksi Fitur Chi-Square Dan Algoritma Multinomial Naïve Bayes Untuk Analisis Sentimen Pelangggan Tokopedia,” J. Gaussian, vol. 11, no. 4, hal. 562–571, 2023, doi: 10.14710/j.gauss.11.4.562-571.
H. Februariyanti, M. Firmansyah, J. S. Wibowo, dan M. S. Utomo, “Analisis Sentimen Tanggapan Terhadap Aplikasi Layanan Informasi Penginapan Menggunakan Metode Klasifikasi Naive Bayes,” semanTIK, vol. 6, no. 2, hal. 115–124, 2020.
I. Saputra et al., “Analisis Sentimen Pengguna Marketplace Bukalapak dan Tokopedia di Twitter Menggunakan Machine Learning,” Fakt. Exacta, vol. 13, no. 4, hal. 200, 2021, doi: 10.30998/faktorexacta.v13i4.7074.
D. Mualfah, A. Prihatin, dan R. Firdaus, “Analisis Sentimen Masyarakat Terhadap Kasus Pembobolan Data Nasabah Bank BSI Pada Twitter Menggunakan Metode Random Forest Dan Naïve Bayes,” vol. 13, no. 3, hal. 614–620, 2023.
Downloads
Published
Issue
Section
License
Copyright Notice
An author who publishes in the Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) agrees to the following terms:
- Author retains the copyright and grants the journal the right of first publication of the work simultaneously licensed under the Creative Commons Attribution-ShareAlike 4.0 License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal
- Author is able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book) with the acknowledgement of its initial publication in this journal.
- Author is permitted and encouraged to post his/her work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of the published work (See The Effect of Open Access).
Read more about the Creative Commons Attribution-ShareAlike 4.0 Licence here: https://creativecommons.org/licenses/by-sa/4.0/.
_(1).png)
