Optimasi Algoritma K- Nearest Neighbor Berbasis Particle Swarm Optimization Untuk Meningkatkan Kebutuhan Barang - Taofik Safrudin; Gatot Tri Pranoto; Wahyu Hadikristanto (Forum Kerjasama Pendidikan Tinggi (FKPT), 2023)
Abstract− The application of the K-Nearest Neighbor algorithm can be implemented where the results also show a new insight, namely predicting the level of need. With a ratio of 90%:10%, where there are 50 data objects tested to predict the level of needs in 2 groups, namely low needs or high needs. The results of the model scenario show that there are 2 objects in the Low needs group and 1 object in the High needs group. In evaluating this model, it was obtained from 10 fold Cross Validation that the Accuracy value was 82%, then the Precision value was 87.50%, and the Recall value was 80%. By measuring the performance of the model with Cross Validation, the resulting accuracy has a standard value or standard deviation, which aims to see the distance between the average accuracy and the accuracy of each experiment. While the Test Results using PSO In the evaluation of this model, it is obtained from 10 fold Cross Validation the Accuracy value is 100%, then the Precision value is 100%, and the Recall value is 100%, the test results have increased significantly