Analisis Sentimen Wisatawan terhadap Kualitas Layanan Hotel dan Resort di Lombok Menggunakan SERVQUAL dan CRISP-DM
-
Yerik Afrianto Singgalen
*
Atma Jaya Catholic University of Indonesia, Jakarta,
Indonesia
(*) Corresponding Author
Abstract
The era of digital transformation has sparked innovations in product and service marketing strategies in various sectors, one of which is the tourism sector. In the hospitality industry context, product marketing using website-based digital media allows consumers as hotel guests to review the products and services received. The Tripadvisor website is a digital marketing platform that provides review features for app users, especially consumers, to give ratings and reviews. This study aims to analyze the quality of hotel services using the Service Quality (SERVQUAL) framework based on the results of the classification of hotel guest sentiment data using the Naïve Bayes Classifier (NBC) and Support Vector Machine (SVM) algorithm by the stages of the Cross-Industry Standard Process for Data Mining (CRISP-DM). The CRISP-DM framework consists of six stages, namely: business understanding stage, data understanding stage, data preparation stage, modeling stage, evaluation stage, and deployment stage. The SERVQUAL framework consists of several dimensions: reliability dimension; responsiveness; assurance; empathy; tangibles. The review data that will be processed is the consumer review data of The Oberoi Beach Resort Lombok; Sheraton Senggigi Beach Resort; Sudamala Resort Sengiggi; Holiday Resort Lombok; Aston Sunset Beach Resort. The results of this study show that the SVM algorithm performs better than NBC, where the accuracy value is 98.57%, the precision value is 100%, the recall value is 97.14%, and the f-measure value is 98.54%. The AUC value is 100%, and the t-Test value is 98.6%. Unlike the case with the results of SVM's algorithm performance evaluation without using the SMOTE Upgrading Operator, where the accuracy value is 95.71%, the precision value is 95.71%, the recall value is 100%, and the f-measure value is 97.81%. In addition, the AUC value is 91.1%, and the t-Test value is 95.7%.
Downloads
References
D. Firmansyah, D. Saepuloh, and Dede, “Daya Saing : Literasi Digital dan Transformasi Digital,” J. Financ. Bus. Digit., vol. 1, no. 3, pp. 237–250, 2022, doi: 10.55927/jfbd.v1i3.1348.
R. A. Purnama, A. A. Rifai, I. Firmansyah, and R. Rijanto, “Model Kinerja Kewriausahaan di Industri 4. 0 : Kompetensi Inti dan Transformasi Digital,” SEIKO J. Manag. Bus., vol. 5, no. 2, pp. 121–137, 2022, doi: 10.37531/sejaman.v5i2.3010.
S. Asrol, R. Lidyah, T. Hartini, and Muhammadinah, “Peran Percepatan Transformasi Digital untuk Keberlanjutan Bisnis dan Pemulihan Ekonomi Pelaku UMKM di Kota Palembang Pascapandemi,” J. Intelekt. Keislaman, Sos. dan Sains, vol. 11, no. 2, pp. 242–246, 2022, doi: 10.19109/intelektualita.v11i2.14685.
N. Ainun, V. Sihombing, and Masrizal, “Application of the Serqual Method to Analyzed the Satisfaction Level of Hotel Services and Facilities,” Sink. J. dan Penelit. Tek. Inform., vol. 5, no. 2, pp. 239–245, 2021, doi: 10.33395/sinkron.v5i2.10862.
Y. Prananda, D. R. Lucitasari, and M. S. A. Khannan, “Penerapan Metode Service Quality (SERVQUAL) Untuk Peningkatan Kualitas Pelayanan Pelanggan,” Opsi, vol. 12, no. 1, pp. 1–11, 2019, doi: 10.31315/opsi.v12i1.2827.
A. Sibua, A. Umar, W. Sibua, and D. Weka, “Analysis of Service Quality using Service Quality and SWOT Method in Border Area (Case Study : PT. JABABEKA Morotai),” J. Ind. Eng. Manag., vol. 7, no. 3, pp. 250–260, 2022.
Y. A. Singgalen, “Analisis Sentimen Wisatawan Melalui Data Ulasan Candi Borobudur di Tripadvisor Menggunakan Algoritma Naïve Bayes Classifier,” Build. Informatics, Technol. Sci., vol. 4, no. 3, p. 1343−1352, 2022, doi: 10.47065/bits.v4i3.2486.
Y. A. Singgalen, “Analisis Sentimen Pengunjung Pulau Komodo dan Pulau Rinca di Website Tripadvisor Berbasis CRISP-DM,” J. Inf. Syst. Res., vol. 4, no. 2, pp. 614–625, 2023, doi: 10.47065/josh.v4i2.2999.
Y. A. Singgalen, “Analisis Performa Algoritma NBC , DT , SVM dalam Klasifikasi Data Ulasan Pengunjung Candi Borobudur Berbasis CRISP-DM,” Build. Informatics, Technol. Sci., vol. 4, no. 3, pp. 1634–1646, 2022, doi: 10.47065/bits.v4i3.2766.
A. Fadli, M. I. Zulfa, and Y. Ramadhani, “Performance Comparison of Data Mining Classification Algorithms for Early Warning System of Students Graduation Timeliness,” J. Teknol. dan Sist. Komput., vol. 6, no. 4, pp. 158–163, 2018, doi: 10.14710/jtsiskom.6.4.2018.158-163.
M. A. Burhanuddin, R. Ismail, N. Izzaimah, A. A.-J. Mohammed, and N. Zainol, “Analysis of Mobile Service Providers Performance Using Naive Bayes Data Mining Technique,” Int. J. Electr. Comput. Eng., vol. 8, no. 6, p. 5153, 2018, doi: 10.11591/ijece.v8i6.pp5153-5161.
A. Aakash and A. Gupta Aggarwal, “Assessment of Hotel Performance and Guest Satisfaction through eWOM: Big Data for Better Insights,” Int. J. Hosp. Tour. Adm., vol. 23, no. 2, pp. 317–346, 2022, doi: 10.1080/15256480.2020.1746218.
A. Firmansyah and Eriswandi, “Analisis Sentimen Terhadap Operator Seluler Telkomsel Menggunakan Algoritma Naive Bayes,” SIGMA – J. Teknol. Pelita Bangsa, vol. 10, no. 4, pp. 108–116, 2020.
H. J. Christanto and Y. A. Singgalen, “Sentiment Analysis on Customer Perception towards Products and Services of Restaurant in Labuan Bajo,” J. Inf. Syst. Informatics, vol. 4, no. 3, pp. 511–523, 2022, doi: 10.51519/journalisi.v4i3.276.
F. Abadi and Herwin, “Pengaruh Kualitas Pelayanan Terhadap Kepuasan Wisatawan Berdampak Kepada Perilaku Masa Depan Wisatawan Domestik,” Manag. Account. Expo., vol. 3, no. 2, pp. 134–142, 2020, doi: 10.36441/mae.v3i2.210.
A. Darmawan, N. Kustian, and W. Rahayu, “Implementasi Data Mining Menggunakan Model SVM untuk Prediksi Kepuasan Pengunjung Taman Tabebuya,” STRING (Satuan Tulisan Ris. dan Inov. Teknol., vol. 2, no. 3, p. 299, 2018, doi: 10.30998/string.v2i3.2439.
N. P. A. Aryawati, “Digital Tourism: Strategi Menarik Wisatawan Milenial Ke NTB,” J. Ilm. Hosp., vol. 9, no. 1, pp. 87–92, 2020, [Online]. Available: http://stp-mataram.e-journal.id/JHI
R. Kurniansah and J. J. Purnama, “Komponen-Komponen Pendukung Pariwisata Kuta Lombok Provinsi Nusa Tenggara Barat,” J. Hosp. dan Pariwisata, vol. 6, no. 1, pp. 1–6, 2020, doi: 10.30813/jhp.v6i1.2029.
M. O. Tumbade, K. D. Hartomo, and M. N. N. Sitokdana, “Sistem Informasi Dalam Mendukung Kematangan Planning Smart City E-Government Untuk Peningkatan Kapabilitas Pelayanan Pemerintah Lombok Timur Terhadap Masyarakat,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 9, no. 3, pp. 2137–2152, 2022, doi: 10.35957/jatisi.v9i3.2225.
A. P. Haster and K. D. Hartomo, “Analisis Tingkat Kematangan Smart City Kabupaten Lombok Utara Menggunakan COBIT 2019,” J. Media Inform. Budidarma, vol. 6, no. 3, p. 1459, 2022, doi: 10.30865/mib.v6i3.4344.
K. M. Sukiakhy and C. V. R. Jummi, “Klasifikasi Dan Visualisasi Pariwisata Aceh Dengan Menggunakan Data Pada Twitter,” JUSIM (Jurnal Sist. Inf. Musirawas), vol. 5, no. 2, pp. 148–157, 2020, doi: 10.32767/jusim.v5i02.1062.
D. A. Muthia, “Komparasi Algoritma Klasifikasi Text Mining Untuk Analisis Sentimen Pada Review Restoran,” J. PILAR Nusa Mandiri, vol. 14, no. 1, pp. 69–74, 2018.
M. Kusmira, “Penerapan Data Mining Pengajuan Pembiayaan Perumahan (Consumen Financing) Individual Menggunakan Algoritma C4.5,” J. Khatulistiwa Inform., vol. 7, no. 1, pp. 66–71, 2019, doi: 10.31294/jki.v7i1.126.
Rousyati, W. Gata, D. Pratmanto, and N. K. Warchani, “Analisis Sentimen Financial Technology Peer to Peer Lending Pada Aplikasi Koinworks,” J. Teknol. Infor, vol. 9, no. 6, pp. 1167–1176, 2022, doi: 10.25126/jtiik.202294409.
R. W. Astuti, S. Puspitorini, and F. K. Akbar, “Implementasi RapidMiner pada Analisa Ketepatan Bidang Pekerjaan Alumni Prodi Teknik Informatika STMIK Nurdin Hamzah Jambi,” Fortech, vol. 3, no. 2, pp. 58–64, 2019.
M. W. Pertiwi, M. F. Adiwisastra, and D. Supriadi, “Analisa Komparasi Menggunakan 5 Metode Data Mining dalam Klasifikasi Persentase Wanita Sudah menikah di Usia 15-49 yang Memakai Alat KB (Keluarga Berencana),” J. Khatulistiwa Inform., vol. 7, no. 1, pp. 37–42, 2019, doi: 10.31294/jki.v7i1.5741.
D. N. Fitriana and Y. Sibaroni, “Sentiment Analysis on KAI Twitter Post Using Multiclass Support Vector Machine (SVM),” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 4, no. 5, pp. 846–853, 2020, doi: 10.29207/resti.v4i5.2231.
A. Karim, “Perbandingan Prediksi Kemiskinan di Indonesia Menggunakan Support Vector Machine (SVM) dengan Regresi Linear,” J. Sains Mat. dan Stat., vol. 6, no. 1, pp. 107–112, 2020, doi: 10.24014/jsms.v6i1.9259.
E. A. Nida, “Analisis Kinerja Algoritma Support Vector Machine (SVM) Guna Pengambilan Keputusan Beli/Jual Pada Saham PT Elnusa Tbk. (ELSA),” J. Transform., vol. 17, no. 2, pp. 160–170, 2020, doi: 10.26623/transformatika.v17i2.1649.
N. L. W. S. R. Ginantra, C. P. Yanti, G. D. Prasetya, I. B. G. Sarasvandana, and I. K. A. G. Wiguna, “Analisis Sentimen Ulasan Villa di Ubud Menggunakan Metode Naive Bayes, Decision Tree, dan k-NN,” Janapati, vol. 11, no. 3, pp. 205–216, 2022.
F. Nalle, R. P. C. Fanggidae, and P. Y. Amtiran, “Analisis Harga Terhadap Tingkat Hunian Hotel dalam Mendukung Industri Pariwisata di Kota Kupang,” GLORY J. Ekon. Ilmu Sos., vol. 3, no. 1, pp. 1–13, 2022, [Online]. Available: https://ejurnal.undana.ac.id/index.php/glory/article/view/7664
S. Wijayanto, D. A. Prabowo, D. Y. Kristiyanto, and M. Y. Fathoni, “Analisis Sentimen Berbasis Aspek pada Layanan Hotel di Wilayah Kabupaten Banyumas dengan Word2Vec dan Random Forest,” JPIT J. Pengemb. IT, vol. 8, no. 1, pp. 1–3, 2023.
W. Fauzzia, R. Rahmayani, and R. Handayani, “Peranan Standar Operasional Prosedur dalam Meningkatkan Kinerja Cook di Kitchen Department Hotel Harper Purwakarta by Aston,” J. Manaj. Perhotelan Dan Pariwisata, vol. 5, no. 2, pp. 100–104, 2022, [Online]. Available: https://ejournal.undiksha.ac.id/index.php/JMPP/article/view/46858
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Analisis Sentimen Wisatawan terhadap Kualitas Layanan Hotel dan Resort di Lombok Menggunakan SERVQUAL dan CRISP-DM
Pages: 1870−1882
Copyright (c) 2023 Yerik Afrianto Singgalen
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are 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 an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their 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 published work (Refer to The Effect of Open Access).
