Prediksi Jumlah Kunjungan Wisatawan Mancanegara Pada Negara Singapura Menggunakan Algoritme Extreme Learning Machine - Muhammad Sanzabi Libianto; Tibyani Tibyani; Yuita Arum Sari (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2019)
Tourism is one of the important aspects contributing to income for the country, especially Singapore. Singapore is ranked 2nd on the Asian continent and ranks 13th in the world in the tourism sector. One of Singapore's state revenue generated in the tourism sector is 14.8%. More than 16 million foreign tourists come to Singapore every year. However, the number of tourist visits has increased and decreased every month. Changes in the value of the fluctuations can lead to less than the maximum tourism industry in Singapore, especially the infrastructure sector. Because the accommodations are limited in accommodating a large number of tourists. Therefore, a prediction of the number of tourist visiting to the country of Singapore is needed, so that it becomes material for consideration in preparing better accommodation. One prediction algorithm that can be used is Extreme Learning Machine (ELM). From the results of the research that has been done, the optimal algorithm parameters on ELM are, the number of feature data = 5, the ratio of training data and testing data = 80%: 20%, and hidden neuron = 10 with the data on the number of tourist visiting in January to December from 2010 to 2016, the error value obtained using MAPE was 7.41%.