Garuda - Garba Rujukan Digital

Novia Hasdyna

Faculty Of Computer Science And Multimedia, Universitas Islam Kebangsaan Indonesia

Author-ID : 5286406

Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Other

Published : 27 Documents Claim Missing Document

Claim Missing Document

Articles

Title

1

Algoritma K-Nearest Neighbor dengan Euclidean Distance dan Manhattan Distance untuk Klasifikasi Transportasi Bus Dinata, Rozzi Kesuma; Akbar, Hafizal; Hasdyna, Novia
ILKOM Jurnal Ilmiah Vol 12, No 2 (2020)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v12i2.539.104-111

K-Nearest Neighbor is a data mining algorithm that can be used to classify data. K-Nearest Neighbor works based on the closest distance. This research using the Euclidean and Manhattan distances to calculate the distance of Lhokseumawe-Medan bus transportation. Data that used in this research was obtained from the Organisasi Angkutan Darat Kota Lhokseumawe. The results of the test with k = 3 has obtained the percentage of 44.94% for Precision, 37.06% Recall, and 81.96% Accuracy for the performance of K-NN with Euclidean Distance. Whereas by using Manhattan Distance the result obtained was 45.49% for Precision, 36.39% Recall, and 84.00% Accuracy. The result shown that Manhattan Distance obtained the highest accuracy, with the difference of 2.04% higher than Euclidean Distance. It indicates that Manhattan Distance is more accurate than Euclidean Distance to classify the bus transportation.

K-means algorithm for clustering system of plant seeds specialization areas in east Aceh Rozzi Kesuma Dinata; Novia Hasdyna; Sujacka Retno; Muhammad Nurfahmi
ILKOM Jurnal Ilmiah Vol 13, No 3 (2021)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v13i3.863.235-243

The number of regions and types of plants in East Aceh Regency requires a data clustering process in order to easily find out which areas are most in-demand based on the type of plants. This study applies the k-means algorithm to classify the data. The data used in this study were obtained from the Department of Agriculture, Food Crops and Horticulture, East Aceh Regency. Based on the test results with k-means, the average number of iterations in the 2015-2019 data is 8,7,6,4,3 iterations for each commodity. The test results can show areas of interest for plant seeds with clusters of high demand, attractive, and less desirable. The system in this study was built based on the web using the PHP programming language.

Algoritma K-Nearest Neighbor dengan Euclidean Distance dan Manhattan Distance untuk Klasifikasi Transportasi Bus Rozzi Kesuma Dinata; Hafizal Akbar; Novia Hasdyna
ILKOM Jurnal Ilmiah Vol 12, No 2 (2020)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v12i2.539.104-111

K-Nearest Neighbor is a data mining algorithm that can be used to classify data. K-Nearest Neighbor works based on the closest distance. This research using the Euclidean and Manhattan distances to calculate the distance of Lhokseumawe-Medan bus transportation. Data that used in this research was obtained from the Organisasi Angkutan Darat Kota Lhokseumawe. The results of the test with k = 3 has obtained the percentage of 44.94% for Precision, 37.06% Recall, and 81.96% Accuracy for the performance of K-NN with Euclidean Distance. Whereas by using Manhattan Distance the result obtained was 45.49% for Precision, 36.39% Recall, and 84.00% Accuracy. The result shown that Manhattan Distance obtained the highest accuracy, with the difference of 2.04% higher than Euclidean Distance. It indicates that Manhattan Distance is more accurate than Euclidean Distance to classify the bus transportation.

Title Search

Found 3 Documents Search Journal : ILKOM Jurnal Ilmiah

Abstract

Abstract

Abstract

Title

Found 3 Documents
Search
Journal : ILKOM Jurnal Ilmiah