Diterbitkan Oleh:
Lembaga Penelitian Pengabdian Masyarakat Universitas Nusa Mandiri
Ciptaan disebarluaskan di bawah Lisensi Creative Commons Atribusi-NonKomersial 4.0 Internasional.
IMPLEMENTATION OF SUPPORT VECTOR REGRESSION IN THE PREDICTION OF THE NUMBER OF TOURIST VISITS TO THE PROVINCE WEST NUSA TENGGARA (NTB)
Abstract
Abstract — Indonesia has a variety of interesting tourist destinations to visit in each region. One area that is used as a favorite tourist destination is the Province of West Nusa Tenggara (NTB). Data The number of tourists visiting the NTB province from 2014 to 2020 tends to change based on data obtained from the Website of the NTB Provincial Tourism Office. The data on the number of visitors will continue to change, even if there is a possibility that it will increase. This can lead to the unpreparedness of the government and other tourism actors in providing the facilities and infrastructure needed by visitors when there is an increase in the number of tourist visits coming to NTB. Therefore, it is necessary to predict the number of tourist visits to NTB with accurate results. In this study, predictions of the number of tourist visits to the Province of NTB were made using the support vector regression method. This research resulted in an application to predict the number of tourist visits to NTB based on Event, Month, and Year. so that it can provide predictive results that are close to the actual value under normal conditions. The data used in this study is data on the number of tourist visits in 2017-2021 and events held in 2017-2021.
Downloads
References
Kincowati, T., Furqon, M. T., & Rahayudi, B. (2019). Prediksi Jumlah Kunjungan Wisatawan Mancanegara Ke Indonesia Menggunakan Metode Average-Based Fuzzy Time Series Models. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 3(6), 5250–5256.
Lestari, K. T. N., Albar, M. A., & Afwani, R. (2019). Penerapan Metode Backpropagation Dalam Memprediksi Jumlah Kunjungan Wisatawan Ke Provinsi Nusa Tenggara Barat (NTB). Journal of Computer Science and Informatics Engineering (J-Cosine), 3(1), 39–48. https://doi.org/10.29303/jcosine.v3i1.236
Maulana, N. D., Setiawan, B. D., & Dewi, C. (2019). Implementasi Metode Support Vector Regression (SVR) Dalam Peramalan Penjualan Roti (Studi Kasus : Harum Bakery). Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 3(3), 2986–2995.
Meimela, A. (2021). Prediksi Jumlah Kunjungan Wisatawan Mancanegara ke Indonesia. Media Wisata, 19(1), 34–41. https://doi.org/10.36276/mws.v19i1.64
Prakoso, B. H. (2019). Implementasi Support Vector Regression pada Prediksi Inflasi Indeks Harga Konsumen. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 19(1), 155–162. https://doi.org/10.30812/matrik.v19i1.511
Putri, W. L., Naja, F., Pratama, T., & Widodo, E. (2021). Prediksi Kunjungan Wisatawan Mancanegara Ke Dki Jakarta Pada Masa Covid-19 Menggunakan Metode Des Holt. Journal of Mathematics Education and Science, 4(2), 81–90. https://doi.org/10.32665/james.v4i2.243
Putriwijaya, Novi Nur & Wijayaningrum, V. N. (2019). Support Vector Regression Untuk Memprediksi Jumlah Kunjungan Wisatawan Mancanegara di Pulau Bali. Machine Learning: Methods and Applications to Brain Disorders, 11(January), 123–140.
Rizal, A. A., & Hartati, S. (2017). Prediksi Kunjungan Wisatawan di Pulau Lombok dengan Menerapkan Recurrent Neural Network dengan Algoritma Training Extended Kalman Filter. Jurnal Ilmiah ILMU KOMPUTER, X(1), 7–18.
Surtiningsih, L., Furqon, M. T., & Adinugroho, S. (2018). Prediksi Jumlah Kunjungan Wisatawan Mancanegara Ke Bali Menggunakan Support Vector Regression dengan Algoritma Genetika. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 2(8), 2578–2586. Retrieved from http://j-ptiik.ub.ac.id
http://www.disbudpar.ntbprov.go.i/
Abstract viewed = 192 times
PDF downloaded = 140 times
Copyright (c) 2022 Zaeniah Zaeniah
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
An author who publishes in the Pilar Nusa Mandiri: Journal of Computing and Information System 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-NonCommercial 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-NonCommercial 4.0 Licence here: https://creativecommons.org/licenses/by-nc/4.0/.