Penilaian Pengetahuan Siswa Dengan Jaringan Syaraf Tiruan Algoritma Perceptron - Syarifuddin N. Kapita; Samlan Mahdi; Firman Tempola (Universitas Khairun, 2020)
In the process of student assessment in SD Negeri 1 Kota Ternate, they still use MS Excel software and it takes approximately 2 to 4 days, so it is assumed to be less effective. This study aims to apply the Perceptron Artificial Neural Network to facilitate the work of teacher assessment in SD Negeri 1 Kota Ternate. Artificial neural network is an information processing system that is designed to mimic the workings of the human brain in solving problems quickly and accurately. In processing the assessment using the perceptron algorithm, training data and testing are needed to classify student learning outcomes data. The system development method used is prototype. System testing is done by the white box method’s. The data used are data classification of student learning outcomes in the realm of knowledge, namely Daily Assessment Results (HPH), Mid Semester Assessment Results (HPTS), and Final Semester Assessment Results (HAS). The results showed that the highest level of accuracy occurred in the initial treatment with an accuracy of 96%, so it can be concluded that the Artificial Neural Network system with the Perceptron algorithm can be implemented for student assessment in the 2013 curriculum.