Implementasi Artificial Neural Network dalam Memprediksi Jumlah Peserta Les Bahasa Inggris Menggunakan Metode Back Propagation (Studi Kasus di Lembaga Kursus Global English) - Ardi, Noper; Isnayanti, Isnayanti (Jurnal Teknik Komputer dan Informatika, 2021)
English speaking ability has become a mandatory skill that must be possessed by academics in Indonesia. Mastery of English orally and in writing is one of the main requirements in continuing education to higher education or in applying for jobs. Pare is a well-known city in Indonesia as a center for learning foreign languages, especially English. Global English is one of the largest course institutions in Pare with the number of students reaching hundreds of students per period. Often there is an unpreparedness on the part of the course institution to anticipate events or even a decrease in the number of students, causing the quality of services they can provide is less than optimal. This study aims to make accurate predictions of the number of students who come to study languages ​​at Global English. With this accurate prediction, Global English can make more thorough preparations. Artificial Neural Network (ANN) or artificial neural network is a computing system in which architecture and computing are inspired by knowledge of nerve cells in the brain. ANN is a model that mimics the workings of biological neural networks. By doing the learning process, the artificial neural network can organize itself to produce a consistent response to a series of inputs. Artificial neural networks are designed and trained to have human-like abilities. One of the advantages of ANN is that it can be used to predict reliably on certain conditions based on available data. Therefore, Predictions in this study will be carried out using an Artificial Neural Network (ANN) using the Back Popagation method