Algoritma K-Nearest Neighbor dengan Euclidean Distance dan Manhattan Distance untuk Klasifikasi Transportasi Bus


Rozzi Kesuma Dinata(1*); Hafizal Akbar(2); Novia Hasdyna(3);

(1) Universitas Malikussaleh
(2) Universitas Malikussaleh
(3) Universitas Islam Kebangsaan Indonesia
(*) Corresponding Author

  

Abstract


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.

Keywords


Komparasi; Klasifikasi; K-Nearest Neighbor; Euclidean Distance; Manhattan Distance

  
  

Full Text:

PDF
  

Article Metrics

Abstract view: 62 times
PDF view: 18 times
     

Digital Object Identifier

doi  https://doi.org/10.33096/ilkom.v12i2.539.104-111
  

Cite

References


G. Abdillah et al., “Penerapan Data Mining Pemakaian Air Pelanggan Untuk Menentukan Klasifikasi Potensi Pemakaian Air Pelanggan Baru Di Pdam Tirta Raharja Menggunakan Algoritma K-Means,” Sentika 2016, vol. 2016, no. Sentika, pp. 18–19, 2016.

W. Yustanti, “Algoritma K-Nearest Neighbour untuk Memprediksi Harga Jual Tanah,” J. Mat. Stat. dan komputasi, vol. 9, no. 1, pp. 57–68, 2012.

S. A. Novarina, “Klasifikasi Jenis Infeksi Berdasarkan Hasil Pemeriksaan Leukosit Menggunakan K-Nearest Neighbor ( KKN ),” 2018.

S. A. Naufal, A. Adiwijaya, and W. Astuti, “Analisis Perbandingan Klasifikasi Support Vector Machine (SVM) dan K-Nearest Neighbors (KNN) untuk Deteksi Kanker dengan Data Microarray,” JURIKOM (Jurnal Ris. Komputer), vol. 7, no. 1, p. 162, 2020, doi: 10.30865/jurikom.v7i1.2014.

R. Siringoringo, “Klasifikasi Data Tidak Seimbang Menggunakan Algoritma SMOTE dan k-Nearest Neighbor,” J. ISD, vol. 3, no. 1, pp. 44–49, 2018.

S. R. Andani, “Penerapan Metode SMART dalam Pengambilan Keputusan Penerima Beasiswa Yayasan AMIK Tunas Bangsa,” J. Sist. dan Teknol. Inf., vol. 7, no. 3, p. 166, 2019, doi: 10.26418/justin.v7i3.30112.

N. H. A. Sari, M. A. F. Fauzi, and P. P. Adikara, “Klasifikasi Dokumen Sambat Online Menggunakan Metode K-Nearest Klasifikasi Dokumen Sambat Online Menggunakan Metode K-Nearest Neighbor dan Features Selection Berbasis Categorical Proportional Difference,” J. Pengemb. Teknol. Inf. dan Ilmu Komput. Univ. Brawijaya, vol. 2, no. August, pp. 2449–2454, 2018.

M. Reza Noviansyah, T. Rismawan, and D. Marisa Midyanti, “Penerapan Data Mining Menggunakan Metode K-Nearest Neighbor Untuk Klasifikasi Indeks Cuaca Kebakaran Berdasarkan Data Aws (Automatic Weather Station) (Studi Kasus: Kabupaten Kubu Raya),” J. Coding, Sist. Komput. Untan, vol. 06, no. 2, pp. 48–56, 2018, [Online]. Available: http://jurnal.untan.ac.id/index.php/jcskommipa/article/view/26672.

D. Sebastian, “Implementasi Algoritma K-Nearest Neighbor untuk Melakukan Klasifikasi Produk dari beberapa E-marketplace,” J. Tek. Inform. dan Sist. Inf., vol. 5, no. 1, pp. 51–61, 2019, doi: 10.28932/jutisi.v5i1.1581.

R. Latifah, E. Susilowati, and W. Febriyanti, “Sistem Pendukung Keputusan Penetuan Calon Penerima Kartu Jakarta Pintar ( KJP ) Menggunakan K-Nearest Neighbor,” J. Sist. Informasi, Teknol. Inform. dan Komput., vol. 8, pp. 97–104, 2017.

J. Riany, M. Fajar, and M. P. Lukman, “Penerapan Deep Sentiment Analysis pada Angket Penilaian Terbuka Menggunakan K-Nearest Neighbor,” Sisfo, vol. 06, no. 01, pp. 147–156, 2016, doi: 10.24089/j.sisfo.2016.09.011.

F. Liantoni, “Klasifikasi Daun Dengan Perbaikan Fitur Citra Menggunakan Metode K-Nearest Neighbor,” J. Ultim., vol. 7, no. 2, pp. 98–104, 2016, doi: 10.31937/ti.v7i2.356.

N. Hasdyna, “Information Gain dalam Reduksi Dimensi Dataset untuk Peningkatan Kinerja Algoritma K-Nearest Neighbor,” TESIS. Universitas Sumatera Utara. 2019.

F. Tempola, M. Muhammad, and A. Khairan, “Perbandingan Klasifikasi Antara Knn Dan Naive Bayes Pada Penentuan Status Gunung Berapi Dengan K-Fold Cross Validation Comparison of Classification Between Knn and Naive Bayes At the Determination of the Volcanic Status With K-Fold Cross,” J. Teknol. Inf. dan Ilmu Komput., vol. 5, no. 5, pp. 577–584, 2018, doi: 10.25126/jtiik20185983.

I. Hasimah and H. Yasin, “Klasifikasi Calon Debitur Kredit Pembiayaan Rumah (KPR) Multiguna Take Over Menggunakan Metode k Nearest Neighbor Dengan Pembobotan Global Diversity Index 1,2,3,” J. Gaussian, vol. 8, pp. 407–417, 2019, [Online]. Available: http://ejournal3.undip.ac.id/index.php/gaussian.


Refbacks



Copyright (c) 2020 Rozzi Kesuma Dinata

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

 ILKOM Jurnal Ilmiah indexed by

doaj_logoCROSSREF_logoROAD_logoPKP_Index_logoGoogle_Scholar_logosinta_logogaruda_logoonesearch_logoBASE_logoWordcat_logo

___________________________________________________________
ILKOM Jurnal Ilmiah
ISSN 2548-7779
Published by Teknik Informatika Fakultas Ilmu Komputer Universitas Muslim Indonesia
W : https://fikom.umi.ac.id/
E : jurnal.ilkom@umi.ac.id

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0