Prediksi Kunjungan Wisatawan dengan Reduksi Noise pada Google Trends menggunakan Hilbert-Huang Transform dan Long Short-Term Memory
Abstract
In many studies, Google Trends Data is efficient to analyze and estimate as explanatory variables, including tourism predictions. However, data retrieval and tourism are always plagued by noise. Without noise processing, the predictive ability of search engine data may be weak, even invalid. As a noise processing method, Hilbert-Huang Transform (HHT) can reduce or clean noise. Forecasting is the art and science of predicting future events. LSTM is able to overcome long-term dependence. This study tries to provide predictions of tourist visits by processing noise in search engines using the Hilbert-Huang Transform method. The forecasting architecture that is built is composed of 3 hidden LSTM layers with 100 units of neurons or nerves that function to process information, which in the LSTM layer also becomes the input layer. Prediction test results on a dataset of 156 rows, resulting in RMSE values in 2019 getting RMSE LSTM 129249 results, and RMSE HHT + LSTM 653058. so that the resulting RMSE is closer to remembering 0.
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References
Atbi, A, S M Debbal, F Meziani, and A Meziane. 2013. “Separation of Heart Sounds and Heart Murmurs by Hilbert Transform Envelogram” 1902 (6): 375–87. https://doi.org/10.3109/03091902.2013.816379.
Chan, Andi Supriadi. 2018. “Prediksi Kedatangan Wisatawan Pada Pariwisata Kota Batam Dengan Menggunakan Teknik Knowledge Data Discovery.” Jurnal Ilmiah Informatika 6 (01): 11. https://doi.org/10.33884/jif.v6i01.432.
Farid, Ahmed T.M. 2012. “Prediction of Unknown Deep Foundation Lengths Using the Hilbert Huang Transform (HHT).” HBRC Journal 8 (2): 123–31. https://doi.org/10.1016/j.hbrcj.2012.09.008.
George S. Spais, 2010. 2010. “Search Engine Optimization ( SEO ) as a Dynamic Online Promotion Technique : The Implications of Activity Theory for Promotion Managers.” Innovative Marketing 6 (1): 7–24.
Hidayati, Arifia Putri, Fakultas Teknik Elektro, and Universitas Telkom. 2020. “The Design of Brain Computer Interface System For Classification of Dementia's Electroenchephelograph Signal Using Hilbert Huang Tranform Method” 7 (3): 9145–51.
Khadafy, Al Riza. 2015. “Penerapan Naive Bayes Untuk Mengurangi Data Noise Pada Klasifikasi Multi Kelas Dengan Decision Tree.” Journal of Intelligent Systems 1 (2): 136–42.
Kurniadi, Dede, and Asri Mulyani. 2017. “Pengaruh Teknologi Mesin Pencari Google Terhadap Perkembangan Budaya Dan Etika Mahasiswa.” Jurnal Algoritma 14 (1): 19–25. https://doi.org/10.33364/algoritma/v.14-1.19.
Li, Xin, Hengyun Li, Bing Pan, and Rob Law. 2021. “Machine Learning in Internet Search Query Selection for Tourism Forecasting.” Journal of Travel Research 60 (6): 1213–31. https://doi.org/10.1177/0047287520934871.
Lingga, Novelly Naomi, and Aniq Atiqi Rohmawati. 2021. “Pemodelan Dan Peramalan Kedatangan Wisatawan Ke Tempat Wisata Dengan Google Trends Menggunakan Metode Variasi Kalender ARIMAX.” Proceeding of Engineering 8 (2): 3361–72.
Mariyono, Joko. 2017. “Determinants of Demand for Foreign Tourism in Indonesia.” Jurnal Ekonomi Pembangunan: Kajian Masalah Ekonomi Dan Pembangunan 18 (1): 82. https://doi.org/10.23917/jep.v18i1.2042.
Muhammad, Rayhan, Seno Adi Putra, and S Si. 2021. “Wireless Sensor Network Untuk Menganalisis Perilaku Jembatan Single Degree Of Freedom Dengan Menggunakan Metode Hilbert Huang Transform” 8 (2): 3606–20.
Nabillah, Ida, and Indra Ranggadara. 2020. “Mean Absolute Percentage Error Untuk Evaluasi Hasil Prediksi Komoditas Laut.” JOINS (Journal of Information System) 5 (2): 250–55. https://doi.org/10.33633/joins.v5i2.3900.
Nafah, Hanief Khoyyir, and Evita Purnaningrum. 2021. “Penggunaan Big Data Melalui Analisis Google Trends Untuk Mengetahui Perspektif Pariwisata Indonesia Di Mata Dunia.” Snhrp 3 ((2021)): 430–36.
Qomah, Isti, Dimas Anton Asfani, and Dedet Candra Riawan. 2016. “Deteksi Kerusakan Batang Rotor Pada Motor Induksi Menggunakan Analisis Arus Mula Berbasis Hilbert Transform.” Jurnal Teknik ITS 5 (2). https://doi.org/10.12962/j23373539.v5i2.16054.
Raharyani, Mimin Putri, Rekyan Regasari Mardi Putri, and Budi Darma Setiawan. 2018. “Implementasi Algoritme Support Vector Regression Pada Prediksi Jumlah Pengunjung Pariwisata.” Jurnal Teknologi Informasi Dan Ilmu Komputer 2 (4): 1501–9.
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