Penerapan Fungsi Exponential Pada Pembobotan Fungsi Jarak Euclidean Algoritma K-Nearest Neighbor

Abstract

– k-Nearest Neighbor (k-NN) is one of the popular classification algorithms and is widely used to solve classification cases. This is because the k-NN algorithm has advantages such as being simple, easy to explain, and easy to implement. However, the k-NN algorithm has a lack of classification results that are strongly influenced by the scale of input data and Euclidean which treats attribute data evenly, not according to the relevance of each data attribute. This causes a decrease in the classification results. One way to improve the classification accuracy performance of the k-NN algorithm is the method of weighting its features when measuring the Euclidean distance. The exponential function of the optimized Euclidean distance measurement is applied to the k-NN algorithm as a distance measurement method. Improving the performance of the k-NN method with the Exponential function for weighting features on k-NN will be carried out by experimentation using the Data Mining method. Then the results of the performance of the objective method will be compared with the original k-NN method and the previous k-NN weighting research method. As a result of the closest distance decision, taking the closest distance to k-NN will be determined with a value of k=5. After the experiment, the goal algorithm was compared with the k-NN, Wk-NN, and DWk-NN algorithms. Overall the comparison results obtained an average value of k-NN 85.87%, Wk-NN 86.98%, DWk-NN 88.19% and the k-NN algorithm given the weighting of the Exponential function obtained a value of 90.17%.

Implementasi Algoritma Path Planning A* Pada Base Station Robot Sepak Bola Beroda

“…Metode pengukuran jarak yang paling sering digunakan diantaranya euclidean distance dan manhattan distance. Euclidean distance merupakan fungsi pengukuran jarak yang umum digunakan pada K-NN dengan menerapkan pembobotan fitur [7], sedangkan manhattan distance menghitung perbedaan absolut dari kedua titik koordinat dari dua buah objek yang diketahui. Pada algoritma A*, manhattan distance bekerja lebih baik dibandingkan euclidean distance dikarenakan perhitungannya yang lebih sederhana [8] sehingga path planning pada penelitian ini menerapkan perhitungan jarak menggunakan manhattan distance.…”