Analisis Sentimen Restoran Ulasan Pelanggan Dengan Menggunakan Algoritme Naive Bayes Untuk Meningkatkan Kualitas Pelayanan Restoran “Warung Pedes Gemes”
DOI:
https://doi.org/10.29408/jit.v9i1.33688Keywords:
Naive Bayes, Restaurant, Sentiment Analysis, Customer Reviews, Service QualityAbstract
In the digital era, customer reviews play an important role in shaping a restaurant’s reputation and assessing the quality of its services. Feedback provided by customers through reviews can be utilized as a valuable source of information for service improvement. This study aims to analyze customer sentiment toward Warung Pedes Gemes restaurant using a sentiment analysis approach based on the Naive Bayes algorithm. Review data were collected through questionnaires and processed through several stages, including data preprocessing, weighting using Term Frequency–Inverse Document Frequency (TF-IDF), sentiment classification, and model evaluation using a confusion matrix. The results indicate that the majority of customer reviews were classified as positive, with 543 positive reviews and 22 negative reviews identified. The performance evaluation of the Naive Bayes model shows an accuracy, precision, recall, and F1-score of 100%. These findings demonstrate the effectiveness of the Naive Bayes algorithm in accurately classifying customer sentiment. Therefore, this study contributes to the application of data-driven sentiment analysis as a supporting tool for improving service quality in the culinary industry.
References
[1] V. A. Permadi, “Analisis Sentimen Menggunakan Algritma Naïve Bayes Terhadap Review Restoran Di Singapura 141.” [Online]. Available: Https://Www.Kaggle.Com/Hj5992/Restaurantreviews
[2] W. Parasati, F. Abdurrachman Bachtiar, And N. Y. Setiawan, “Analisis Sentimen Berbasis Aspek Pada Ulasan Pelanggan Restoran Bakso President Malang Dengan Metode Naïve Bayes Classifier,” 2020. [Online]. Available: Http://J-Ptiik.Ub.Ac.Id
[3] S. Aini Pohan And F. Hasyifah Sibarani, “Analisis Sentimen Terhadap Aplikasi Maxim Menggunakan Algoritma Random Forest,” 2024. [Online]. Available: Http://Jurnal.Goretanpena.Com/Index.Php/Jssr
[4] K. Diah Indarwati And H. Februariyanti, “Analisis Sentimen Terhadap Kualitas Pelayanan Aplikasi Go-Jek Menggunakan Metode Naive Bayes Classifier.”
[5] I. Di Estika, I. Darmawan, And O. N. Pratiwi, “Analisis Sentimen Ulasan Pengguna Untuk Peningkatan Layanan Menggunakan Algoritma Naïve Bayes (Studi Kasus : Bukalapak) Sentiment Analysis Of User Review For Service Improvement Using Naive Bayes Algorithm (Case Study: Bukalapak).”
[6] A. P. Giovani, A. Ardiansyah, T. Haryanti, L. Kurniawati, And W. Gata, “Analisis Sentimen Aplikasi Ruang Guru Di Twitter Menggunakan Algoritma Klasifikasi,” Jurnal Teknoinfo, Vol. 14, No. 2, P. 115, Jul. 2020, Doi: 10.33365/Jti.V14i2.679.
[7] N. Dimas Pratama, Y. A. Sari, And P. P. Adikara, “Analisis Sentimen Pada Review Konsumen Menggunakan Metode Naive Bayes Dengan Seleksi Fitur Chi Square Untuk Rekomendasi Lokasi Makanan Tradisional,” 2018. [Online]. Available: Http://J-Ptiik.Ub.Ac.Id
[8] T. Darmansyah, M. M. Dermawan, F. Habib, And A. R. Siregar, “Analisis Tata Cara Pengelolaan Arsip Elektronik Dalam Manajemen Persuratan,” Jurnal Pendidikan Sosial Dan Konseling, Vol. 2, No. 1, Pp. 144–151, 2024.
[9] R. W. Pratiwi, D. Yusuf, And S. Nugroho, “Prediksi Rating Film Menggunakan Metode Naïve Bayes.” [Online]. Available: Https://Www.Kaggle.Com/
[10] I. A. Mastan And Y. Toni, “Analisis Sentimen Terhadap Tempat Kuliner Ayam Gedebuk Dari Komentar Pengunjung Dengan Menggunakan Metode Naïve Bayes Classifier,” Jbase - Journal Of Business And Audit Information Systems, Vol. 3, No. 1, Mar. 2020, Doi: 10.30813/Jbase.V3i1.2062.
[11] M. Ridwan, R. Hariyanto, And M. Alamsyah, “Sentimen Analisis Rumah Makan Rawon Sakinah Dengan Metode Naïve Bayes Classifier,” Journal Homepage: : Journal Of Computer And Electrical Engineering, Vol. 1, No. 3, 2024, [Online]. Available: Https://Journal.Ajbnews.Com/Index.Php/Akiratech
[12] A. Nofandi, N. Y. Setiawan, And D. W. Brata, “Analisis Sentimen Ulasan Pelanggan Dengan Metode Support Vector Machine (Svm) Untuk Peningkatan Kualitas Layanan Pada Restoran Warung Wareg,” 2023. [Online]. Available: Http://J-Ptiik.Ub.Ac.Id
[13] S. Anggina, N. Y. Setiawan, And F. A. Bachtiar, “Analisis Ulasan Pelanggan Menggunakan Multinomial Naïve Bayes Classifier Dengan Lexicon-Based Dan Tf-Idf Pada Formaggio Coffee And Resto,” Is The Best Accounting Information Systems And Information Technology Business Enterprise This Is Link For Ojs Us, Vol. 7, No. 1, Pp. 76–90, Sep. 2022, Doi: 10.34010/Aisthebest.V7i1.7072.
[14] N. Azhar, P. P. Adikara, S. Adinugroho, And P. Korespondensi, “Analisis Sentimen Ulasan Kedai Kopi Menggunakan Metode Naïve Bayes Dengan Seleksi Fitur Algoritme Genetika Sentiment Analysis For Coffee Shop Reviews Using Naïve Bayes Method With Genetic Algorithm Feature Selection,” Vol. 8, No. 3, Pp. 609–618, 2021, Doi: 10.25126/Jtiik.202184436.
[15] L. Nursinggah, R. Ruuhwan, And T. Mufizar, “Analisis Sentimen Pengguna Aplikasi X Terhadap Program Makan Siang Gratis Dengan Metode Naïve Bayes Classifier,” Jurnal Informatika Dan Teknik Elektro Terapan, Vol. 12, No. 3, Aug. 2024, Doi: 10.23960/Jitet.V12i3.4336.
[16] I. Sinanto Ate, A. Nuraminah, And P. Studi, “Komparasi Algoritma Feature Selection Pada Analisis Sentimen Review Film,” Juitik, Vol. 2, No. 2, 2022, [Online]. Available: Http://Journal.Sinov.Id/Index.Php/Juitik/Indexhalamanutamajurnal:Https://Journal.Sinov.Id/Index.Php
[17] P. Hartini Et Al., “Jurnal Informatika Dan Rekayasa Perangkat Lunak Naive Bayes Classifier Untuk Analisis Sentimen Ulasan Pelanggan Pada Domo Coffee And Resto”.
[18] S. D. S. Kurniawan And A. Fauzy, “Penggunaan Naïve Bayes Classifier Dalam Analisis Sentimen Ulasan Aplikasi Mcdonald’s: Perspektif Pengguna Di Indonesia,” Jurnal Media Informatika Budidarma, Vol. 8, No. 3, P. 1545, Jul. 2024, Doi: 10.30865/Mib.V8i3.7765.
[19] E. Wahyu Sholeha, S. Yunita, R. Hammad, V. Cahya Hardita, T. Rekayasa Komputer Jaringan, And P. Tanah Laut, “Analisis Sentimen Pada Agen Perjalanan Online Menggunakan Naïve Bayes Dan K-Nearest Neighbor (Sentiment Analysis Of Online Travel Agent Using Naïve Bayes And K-Nearest Neighbor),” Vol. 3, No. 4, Pp. 203–208, 2022.
[20] D. F. Salsabillah, D. E. Ratnawati, And N. Y. Setiawan, “Analisis Sentimen Ulasan Rumah Makan Menggunakan Perbandingan Algoritma Support Vector Machine Dengan Naive Bayes (Studi Kasus: Ayam Goreng Nelongso Cabang Singosari, Malang),” Jurnal Teknologi Informasi Dan Ilmu Komputer, Vol. 11, No. 1, Pp. 107–116, Feb. 2024, Doi: 10.25126/Jtiik.20241117584.
[21] S. S. Berutu, “Text Mining Dan Klasifikasi Sentimen Berbasis Naïve Bayes Pada Opini Masyarakat Terhadap Makanan Tradisional,” Jurnal Sistem Komputer Dan Informatika (Json), Vol. 4, No. 2, P. 254, Dec. 2022, Doi: 10.30865/Json.V4i2.5138
[22] A. Sudianto, M. Wasil, and M. Mahpuz, “Penerapan Sistem Informasi Geografis dalam Pemetaan Sebaran Kasus Gizi Buruk,” Infotek : Jurnal Informatika dan Teknologi, vol. 4, no. 2, pp. 142–150, Jul. 2021, doi: 10.29408/jit.v4i2.3559.
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