COMPARISON OF K-NEAREST NEIGHBOR AND NEURAL NETWORK FOR PREDICTION INTERNATIONAL VISITOR IN EAST JAVA
Abstract
Tourism is one of the government's priority sectors for economic growth. East Java is one of Indonesia's provinces and is attractive to international visitors. International visitors will appreciate the natural beauty and multiculturalism offered by East Java. In this study, predictions of international visitor visits in East Java from the entrance of Juanda International Airport were carried out using k-NN (k-Nearest Neighbor) and a neural network. The dataset used is based on BPS statistics of Jawa Timur Province in the form of the number of international visitor arrivals from January 2000 to February 2024. The datasets were distributed by dividing the data into 70% for training data and 30% for testing data. The creation of the k-NN model is carried out using k-values 2 to 7. The creation of a modern neural network using hidden layers 1 to 3. The prediction results that were made using k-NN obtained optimal RMSE at k-values 2, resulting in an RMSE of 1594,674 or an error of 3,98%. Meanwhile, the prediction results that have been made using neural networks obtained optimal RMSE at two hidden layers, which resulted in an RMSE of 1873, 355 or an error of 4,68%. So, it is recommended that the k-NN algorithm be used to predict the number of international visitors in East Java. The results of this study can be used to provide quantitative information for the government and stakeholders in adjusting the program to the development of international visitors visiting East Java.
Downloads
References
Badan Pusat Statistik Provinsi Jawa Timur, Statistik Pariwisata Provinsi Jawa Timur 2022. BPS Provinsi Jawa Timur, 2023.
A. Mun’im, “Improvement on the Measurement of Tourism Contribution: An Alternative to Accelerating Indonesia’s Economic Growth,” Jurnal Keprawisataan Indonesia, vol. 16, no. 1, 2022.
BPS-Statistics of Jawa Timur Province, Jawa Timur Province in Figures 2023. BPS-Statistics of Jawa Timur Province, 2023.
BPS Provinsi Jawa Timur, Statistik Daerah Provinsi Jawa Timur 2023. BPS Provinsi Jawa Timur, 2023.
T. A. Rihaksa and H. Susanti, “Penyuluhan Pentingnya Peran Infrastruktur Dalam Permintaan Pariwisata Internasional Indonesia,” Jurnal Abdimas Bina Bangsa, vol. 4, no. 1, pp. 731–744, 2023.
Republik Indonesia, Lampiran Peraturan Presiden Republik Indonesia Nomor 18 Tahun 2020 Tentang Rencana Pembangunan Jangka Menengah Nasional 2020-2024. 2020.
Badan Pusat Statistik Provinsi Jawa Timur, “Perkembangan Pariwisata Provinsi Jawa Timur Februari 2024,” Apr. 2024.
R. A. Pratama, Suhud, and A. D. Jubaedi, “Rancang Bangun Sistem Ramalan Penjualan Menggunakan Metode Regresi Linear,” ProTekInfo(Pengembangan Riset dan Observasi Teknik Informatika), vol. 7, pp. 11–16, Sep. 2020, doi: 10.30656/protekinfo.v8i1.5017.
H. Nisyak, N. Fithriyah, and Fatimatuzzahra, “Optimasi Neural Network Menggunakan Algoritma Genetika Untuk Memprediksi Jumlah Wisatawan Berdasarkan Hunian Hotel,” JoMI: Journal of Millennial Informatics, vol. 2, no. 1, pp. 23–30, Jan. 2024.
F. Sabry, K Nearest Neighbor Algorithm: Fundamentals and Applications. One Billion Knowledgeable, 2022.
M. Arhami and M. Nasir, Data Mining - Algoritma dan Implementasi. Penerbit Andi, 2020.
J. Indriyanto, Algoritma K-Nearest Neighbor untuk Prediksi Nasabah Asuransi. NEM, 2021.
S. Chakraverty and S. K. Jeswal, Applied Artificial Neural Network Methods for Engineers and Scientists. WORLD SCIENTIFIC, 2021.
M. Y. Anshori et al., “Estimation of closed hotels and restaurants in Jakarta as impact of corona virus disease spread using adaptive neuro fuzzy inference system,” IAES International Journal of Artificial Intelligence (IJ-AI), vol. 11, no. 2, pp. 462–472, Jun. 2022.
F. S. Rini, T. D. Wulan, and T. Herlambang, “Forecasting the Number of Demam Berdarah Dengue (DBD) Patients Using the Fuzzy Method at the Siwalankerto Public Health Center,” in AIP Conference Proceedings, American Institute of Physics Inc., Jan. 2023.
A. Rahim, A. D. Rahajoe, and M. Mahaputra, “Prediksi Jumlah Pengunjung Perperiode Terhadap Tempat Wisata Pantai Menggunakan Triple Exponential Smoothing (Studi Kasus Pantai Gili Labak Sumenep),” JIFTI-Jurnal Ilmiah Teknologi Informasi dan Robotika, vol. 3, no. 2, pp. 39–43, Dec. 2021.
M. Y. Anshori, D. Rahmalia, and T. Herlambang, “Comparison Backpropagation (BP) and Learning Vector Quantification (LVQ) on classifying price range of smartphone in market,” J Phys Conf Ser, vol. 1836, no. 1, p. 012040, Mar. 2021.
D. F. Karya, P. Katias, T. Herlambang, and D. Rahmalia, “Development of Unscented Kalman Filter Algorithm for stock price estimation,” J Phys Conf Ser, vol. 1211, p. 012031, Apr. 2019.
M. Y. Anshori, I. H. Santoso, T. Herlambang, D. Rahmalia, K. Oktafianto, and P. Katias, “Forecasting of Occupied Rooms in the Hotel Using Linear Support Vector Machine,” 2023.
M. Y. Anshori, T. Herlambang, P. Katias, F. A. Susanto, and R. R. Rasyid, “Profitability estimation of XYZ company using H-infinity and Ensemble Kalman Filter,” in The 5th International Conference of Combinatorics, Graph Theory, and Network Topology (ICCGANT 2021), IOP Publishing Ltd, Jan. 2021.
A. Muhith, I. H. Susanto, D. Rahmalia, D. Adzkiya, and T. Herlambang, “The Analysis of Demand and Supply of Blood in Hospital in Surabaya City Using Panel Data Regression,” Nonlinear Dynamics and Systems Theory, vol. 22, no. 5, pp. 550–560, 2022.
D. Rahmalia et al., “Comparison between Neural Network (NN) and Adaptive Neuro Fuzzy Inference System (ANFIS) on sunlight intensity prediction based on air temperature and humidity,” J Phys Conf Ser, vol. 1538, no. 1, p. 012044, May 2020.
F. A. Susanto et al., “Estimation of Closed Hotels and Restaurants in Jakarta as Impact of Corona Virus Disease (Covid-19) Spread Using Backpropagation Neural Network,” Nonlinear Dynamics and Systems Theory, vol. 22, no. 4, pp. 457–467, 2022.
F. S. Nugraha, M. J. Shidiq, and S. Rahayu, “Analisis Algoritma Klasifikasi Neural Network Untuk Diagnosis Penyakit Kanker Payudara,” Jurnal Pilar Nusa Mandiri, vol. 15, no. 2, pp. 149–156, Aug. 2019.
D. B. Magfira et al., “Electronic Nose for Classifying Civet Coffee and Non-Civet Coffee,” 2023.
P. Andrean, “Penerapan Metode K-NN Untuk Memprediksi Hasil Pertanian di Kabupaten Malang,” Jurnal Mahasiswa Teknik Informatika, vol. 3, no. 1, pp. 235–242, Mar. 2019.
V. Asy’ari, M. Y. Anshori, T. Herlambang, I. W. Farid, D. Fidita Karya, and M. Adinugroho, “Forecasting average room rate using k-nearest neighbor at Hotel S,” in 2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation (ICAMIMIA), IEEE, Nov. 2023, pp. 496–500.
M. Y. Anshori, V. Asy’Ari, T. Herlambang, and I. W. Farid, “Forecasting occupancy rate using neural network at Hotel R,” in 2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation (ICAMIMIA), IEEE, Nov. 2023, pp. 347–351.
F. V. P. Samosir, L. P. Mustamu, E. D. Anggara, A. I. Wiyogo, and A. Widjaja, “Exploratory Data Analysis terhadap Kepadatan Penumpang Kereta Rel Listrik,” Jurnal Teknik Informatika dan Sistem Informasi, vol. 7, no. 2, pp. 449–467, Aug. 2021, doi: 10.28932/jutisi.v7i2.3700.
G. Leinhardt and S. Leinhardt, “Exploratory Data Analysis: New Tools for the Analysis of Empirical Data,” Review of Research in Education, vol. 8, pp. 85–157, 1980, doi: 10.2307/1167124.
Chairani, J. Harahap, and U. Zein, “Hubungan Pola Asuh Ibu dengan Karies Gigi Anak Balita di TK Perkebunan Nusantara I Kota Langsa Tahun 2019,” Journal of Healthcare Technology and Medicine, vol. 9, no. 1, pp. 416–424, Apr. 2023, doi: 10.33143/jhtm.v9i1.2834.
U. Hidayah and A. Sifaunajah, Cara Mudah Memahami Algortima K-Nearest Neighbor Studi Kasus Visual Basic 6.0. Jombang: LPPM Universitas KH. A. Wahab Hasbullah, 2019.
F. Gorunescu, Data Mining: Concepts, Models and Techniques, vol. 12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011.
Imam Robandi, ARTIFICIAL INTELLIGENCE-Mengupas Rekayasa Kecerdasan Tiruan. Yogyakarta: Andi Offset, 2021.
D. Kriesel, A Brief Introduction to Neural Networks. Germany: ZETA2-EN, 2005. [Online]. Available: http://www.dkriesel.com/en/science/neural_networkshttp://www.dkriesel.com/en/tech/snipe
Copyright (c) 2024 Dina Novita, Teguh Herlambang, Vaizal Asy’ari, Arasy Alimudin, Hamzah Arof
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this Journal agree to the following terms:
- Author retain copyright and grant the journal right of first publication with the work simultaneously licensed under a creative commons attribution license that allow others to share the work within an acknowledgement of the work’s authorship and initial publication of this journal.
- Authors are able to enter into separate, additional contractual arrangement for the non-exclusive distribution of the journal’s published version of the work (e.g. acknowledgement 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 websites) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published works.
1.gif)
