Prediksi Penerimaan Mahasiswa Baru dengan Menggunakan Metode Extreme Learning Machine (ELM) (Studi Kasus pada Universitas 17 Agustus 1945 Surabaya) - Aulia Jasmin Safira; Imam Cholissodin; Putra Pandu Adikara (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2022)
Admission of new students is a routine activity carried out by all educational institutions in Indonesia every year, which is a reflection of the public's views and interests in the educational institution. Predictions of the development of new student admissions so far have only been made based on speculation using data from previous years. An Extreme Learning Machine (ELM) is one of the methods that can be used to predict good results. Therefore, this study used the Extreme Learning Machine (ELM) method. The results of the trial in this study showed that the ELM method has a good error value measured by an error rate using the Mean Absolute Percentage Error (MAPE) of 0,20% with a comparison of the amount of training data and testing data of 90%:10%, the input weight range between-0.5 and 0.5, the number of neurons in the hidden layer as many as 2, using the Binary Sigmoid activation function, and using the number of features 2. This proves that using the Extreme Learning Machine (ELM) method, it can predict new student admissions well and get the number of new student admissions in the future.