Application of the perceptron algorithm in selecting statistics laboratory assistants - Astuti, Febriani; Kris Suryowati; Olin Putra Pratama; Anggelina Karolina Teti (Desimal: Jurnal Matematika, 2025)
Artificial Neural Networks (ANN) are computational models inspired by the structure and function of the human brain, particularly in utilizing interconnected neurons to solve complex problems. One of the foundational algorithms in ANN is the Perceptron algorithm, which is capable of identifying patterns and classifying data based on a set of input variables. This research aims to implement the Perceptron algorithm in the selection process of Statistics Laboratory Assistants at Akprind University. The method involves the use of fuzzy logic to assign weights to seven assessment variables determined by the laboratory: student semester, Grade Point Average (GPA), attitude, activeness during lectures, health condition, organizational involvement, and leadership. A dataset of 40 student records was used for training and testing the model. The results showed that the Perceptron algorithm, when integrated with fuzzy logic, successfully classified the data with an accuracy of 100%. In conclusion, the combination of the Perceptron algorithm and fuzzy logic proved to be effective in recognizing selection patterns, making it a reliable method for supporting the assistant recruitment process in the Statistics Laboratory at Akprind University.