Analisis Perilaku Wisatawan Berdasarkan Data Ulasan di Website Tripadvisor Menggunakan CRISP-DM: Wisata Minat Khusus Pendakian Gunung Rinjani dan Gunung Bromo


  • Yerik Afrianto Singgalen * Mail Universitas Katolik Indonesia Atma Jaya, Jakarta, Indonesia
  • (*) Corresponding Author
Keywords: Tourist Behavior; Special Interest; Sentiment Analysis; Bromo; Rinjani

Abstract

Traveller behavior needs to be comprehensively identified and analyzed to determine changes in tourism preference in Indonesia.  One relevant approach to identifying traveler behavior is sentiment analysis through review data on the Tripadvisor website by text mining approach. This study aims to recommend a sentiment analysis model that is useful for managers of particular interest climbing tourist destinations on Mount Rinjani and Mount Bromo based on the type of visit alone (solo), with couples (couple), with friends, and with family (family). The research method used is Cross Industry Standard Process for Data Mining (CRISP-DM), with an algorithm adapted to managing tourist destinations for Mount Rinjani and Mount Bromo, namely attractions, roads and modes of transportation (accessibility), and accommodation. To overcome the problem of data balance in datasets, the calculation process of the Decision Tree (DT) algorithm and the Support Vector Machine (SVM) is connected to the Synthetic Minority Over-sampling Technique (SMOTE) operator in the Rapidminer application. The results of this study showed that the SVM algorithm showed better performance with an accuracy value of 97.67%, precision of 100%, recall of 95.34, and f-measure of 97.61% in the classification of 1075 text data of Mount Bromo and 326 review data of Mount Rinjani. In addition, in the context of Mount Rinjani, the top five words that often appear in tourist review data on Mount Rinjani are as follows: summit (272), Rinjani (259), trek (201), hike (170), mountain (159). On the other hand, the top five words that often appear in tourist review data on Mount Bromo are as follows: Bromo (1864), sunrise (1124), view (854), crater (758), and mount (577). Thus, it can be seen that tourists with the type of visit alone (solo), with couples (couple), with friends (friends), and with family (family) have a preference for the types of attractions in the form of summits, craters, natural beauty of mountains, hiking trails, types of transportation modes as well as supporting accommodation that needs to be prepared to keep the sustainability of tourism.

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References

S. A. Azzahra and A. Wibowo, “Analisis Sentimen Multi-Aspek Berbasis Konversi Ikon Emosi dengan Algoritme Naïve Bayes untuk Ulasan Wisata Kuliner Pada Web Tripadvisor,” J. Teknol. Inf. dan Ilmu Komput., vol. 7, no. 4, pp. 737–744, 2020, doi: 10.25126/jtiik.2020731907.

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.

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.

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.

A. M. M. Fattah, A. Voutama, N. Heryana, and N. Sulistiyowati, “Pengembangan Model Machine Learning Regresi sebagai Web Service untuk Prediksi Harga Pembelian Mobil dengan Metode CRISP-DM,” JURIKOM J. Ris. Komput., vol. 9, no. 5, pp. 1669–1678, 2022, doi: 10.30865/jurikom.v9i5.5021.

C. Wulandari, Y. Ansori, and K. Fahmi, “CRISP-DM Method On Indonesian Micro Industries (UMKM) Using K-Means Clustering Algorithm,” MATICS J. Ilmu Komput. dan Teknol. Inf. (Journal Comput. Sci. Inf. Technol., vol. 14, no. 2, pp. 35–40, 2022, doi: 10.18860/mat.v14i2.13760.

Y. Christian and K. O. Y. R. Qi, “Penerapan K-Means pada Segmentasi Pasar untuk Riset Pemasaran pada Startup Early Stage dengan Menggunakan CRISP-DM,” JURIKOM J. Ris. Komput., vol. 9, no. 4, pp. 966–973, 2022, doi: 10.30865/jurikom.v9i4.4486.

D. A. Munawwaroh and A. H. Primandari, “Implementasi Crisp-Dm Model Menggunakan Metode Decision Tree Dengan Algoritma Cart Untuk Prediksi Lila Ibu Hamil Berpotensi Gizi Kurang,” J. Ilm. Pendidik. Mat., vol. 10, no. 2, pp. 367–380, 2022, [Online]. Available: http://dx.doi.org/10.31941/delta.v10i2.2172

C. Cahyaningtyas, Y. Nataliani, and I. R. Widiasari, “Analisis Sentimen Pada Rating Aplikasi Shopee Menggunakan Metode Decision Tree Berbasis SMOTE,” Aiti, vol. 18, no. 2, pp. 173–184, 2021, doi: 10.24246/aiti.v18i2.173-184.

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.

Y. A. Singgalen, “Pemilihan Metode dan Algoritma dalam Analisis Sentimen di Media Sosial : Sistematic Literature Review,” J. Inf. Syst. Informatics, vol. 3, no. 2, pp. 278–302, 2021.

R. Despica and E. Zuriyani, “Tourism Awareness and Sapta Pesona Training for the Community of the Purus Beach Tourism Area , Padang City,” Asian J. Community Serv., vol. 1, no. 6, pp. 305–314, 2022.

L. K. M. Putri, P. A. A. Damayanti, and N. L. P. E. Diarthini, “Pengetahuan, Sikap dan Perilaku Terhadap Demam Berdarah Dengue Pada Wisatawan di Kecamatan Ubud, Gianyar Bali,” Hurnal Med. Udayana, vol. 11, no. 3, pp. 10–17, 2022.

R. Sarudin and A. Ismail, “Analisis Online Review Tripadvisor.com Terhadap Minat Pembelian Produk Jasa Akomodasi Di Hotel Manhattan,” J. Hosp. dan Pariwisata, vol. 7, no. 4, pp. 33–43, 2021.

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.

C. Pomantow, F. M. Langi, and C. N. Waworuntu, “Analisis Perilaku Wisatawan Dalam Memilih Objek Wisata di Kota Manado,” Humanlight J. Psychol., vol. 3, no. 2, pp. 102–113, 2022.

R. Lubis, A. Munang, and H. Q. Karima, “Analisis Faktor yang Mempengaruhi Keputusan Berkunjung Pada Waterpark Top 100 Batu Aji,” JIEOM, vol. 5, no. 2, pp. 178–189, 2022.

R. Kosasih and A. Alberto, “Analisis Sentimen Produk Permainan Menggunakan Metode TF-IDF Dan Algoritma K-Nearest Neighbor,” InfoTekJar J. Nas. Inform. dan Teknol. Jar., vol. 6, no. 1, pp. 134–139, 2021.

Y. E. Kurniawati, “Class Imbalanced Learning Menggunakan Algoritma Synthetic Minority Over-sampling Technique – Nominal (SMOTE-N) pada Dataset Tuberculosis Anak,” J. Buana Inform., vol. 10, no. 2, pp. 134–143, 2019, doi: 10.24002/jbi.v10i2.2441.

M. F. Asshiddiqi and K. M. Lhaksmana, “Perbandingan Metode Decision Tree dan Support Vector Machine untuk Analisis Sentimen pada Instagram Mengenai Kinerja PSSI,” in e-Proceeding of Engineering, 2020, vol. 7, no. 3, pp. 9936–9948.

R. Puspita and A. Widodo, “Perbandingan Metode KNN, Decision Tree, dan Naïve Bayes Terhadap Analisis Sentimen Pengguna Layanan BPJS,” J. Inform. Univ. Pamulang, vol. 5, no. 4, pp. 646–654, 2021, doi: 10.32493/informatika.v5i4.7622.

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.

Sapardi, E. Martius, and Erwin, “Analisis Dampak Pengelolaan Wisata Minat Khusus Terhadap Ekonomi dan Sosial Budaya Masyarakat Nagari Air Batumbuk Kabupaten Solok (Studi Kasus Objek Pendakian Gunung Talang Melalui Jalur Air Batumbuk),” J. Ilm. MEA (Manajemen, Ekon. dan Akuntansi), vol. 5, no. 1, pp. 484–501, 2021.

P. N. Sadikin, S. Mulatsih, B. Pramudya, and H. S. Arifin, “Analisis Willingness-To-Pay Pada Ekowisata Taman Nasional Gunung Rinjani,” J. Anal. Kebijak. Kehutan., vol. 14, no. 1, pp. 31–46, 2017, doi: 10.20886/jakk.2017.14.1.31-46.

A. Munir, E. P. Atika, and A. D. Indraswari, “Analisis Sentimen pada review hotel menggunakan metode pembobotan dan klasifikasi,” Jnanaloka, vol. 3, no. 1, pp. 33–38, 2022, doi: 10.36802/jnanaloka.2022.v3-no1-33-38.

I. S. K. Idris, Y. A. Mustofa, and I. A. Salihi, “Analisis Sentimen Terhadap Penggunaan Aplikasi Shopee Mengunakan Algoritma Support Vector Machine ( SVM ),” Jambura J. Electr. Electron. Eng., vol. 5, no. 1, pp. 32–35, 2023.

A. T. Zy, “Comparison Algorithm Classification Naive Bayes, Decision Tree, and Neural Network for Analysis Sentiment,” J. Pelita Teknol., vol. 12, no. 1, pp. 1–14, 2017.

J. A. Syahid and D. Mahdiana, “Perbandingan algoritma untuk klasifikasi analisis sentimen terhadap Genose pada media sosial Twitter,” semanTIK, vol. 7, no. 1, pp. 9–16, 2021, doi: 10.5281/zenodo.5034916.

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.


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Submitted: 2023-01-29
Published: 2023-02-25
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