Implementation of the K-Nearst Neighbor (k-NN) Algorithm in Classification of Angora and Country Cats

Authors

  • Wiwin Andriana Universitas Dian Nuswantoro (UDINUS)
  • Reza Wisnumurti Universitas Dian Nuswantoro (UDINUS)
  • Yuni Lestari Universitas Dian Nuswantoro (UDINUS)
  • Bonifacius Vicky Indriyono Universitas Dian Nuswantoro (UDINUS)
  • Dibyo Adi Wibowo Universitas Dian Nuswantoro (UDINUS)
  • Erika Devi Udayanti Universitas Dian Nuswantoro (UDINUS)

DOI:

https://doi.org/10.33633/jais.v8i1.7129

Abstract

There are so many types of mixed cats from various cat breeds, so many people find it difficult to identify and classify them. Therefore, we need a method that can classify the type of cat breeds. In this study the authors used the K-Nearest Neighbor (k-NN) algorithm to make it easier to recognize and classify cat breeds based on certain characteristics. The author took samples of 2 types of cat races, namely the Anggora race and the Kampung race. The implementation stage is to determine the euclidean distance and sort it and then determine the value of K to find the nearest neighbor. In testing, the authors used 50 training data and 50 test data with 6 attributes used, namely body shape, nose width, nose height, food type, hair type and hair length. The results of the classification of cat breeds using the k-NN method obtained an accuracy rate of 94% and an error rate of 6%.

Author Biographies

Wiwin Andriana, Universitas Dian Nuswantoro (UDINUS)

I am a student at Dian Nuswantoro University, and other activities are as a teacher at training institute.

Reza Wisnumurti, Universitas Dian Nuswantoro (UDINUS)

I am a student at Dian Nuswantoro University, and other activities are as a teacher at training institute.

Yuni Lestari, Universitas Dian Nuswantoro (UDINUS)

I am a student at Dian Nuswantoro University, and other activities are as a teacher at training institute.

Bonifacius Vicky Indriyono, Universitas Dian Nuswantoro (UDINUS)

I am a student at Dian Nuswantoro University, and other activities are as a teacher at training institute.

References

Muhammad Afif A. F, et al. Cat Race Classification using Convolutional Neural Network (CNN) Algorithm. E-Proceeding of Engineering. 2021; Vol.8, p. 2

Anggi N. P, Isnawati. Morphogenetics of the House Cat (Felis domesticus) as a Natural Predator Breeding Tool for Rodents. Bio Lantern. 2022; Vol.11, p. 217

Hasmawati, Nangi, et al. Goods Sales Prediction Application Using the k-Nearest Neighbor (k-NN) Method (Case Study of Tumaka Mart). Semantics. 2017. Vol.3, no.2, pp. 151-160

Ahmad Khairi, et al. Implementation of K-Nearest Neighbor (KNN) for Classification of Pre-Prosperous Communities in Sapikerep Village, Sukapura District. Trilogy: Journal of Technology, Health, and Humanities. 2021

Muhammad Ihsan Al-Hafiz . Implementation of the K-Nearest Neighbor (K-NN) Algorithm in Determining Cat Types. Thesis. Palangkaraya & College of Information and Computer Management (STMIK) Palangkaraya. 2021.

Permana Putra, et al. Analysis of the K-Nearest Neighbor (KNN) Method in Iris Flower Data Classification. Kaputama Informatics Engineering Journal (JTIK). 2022; Vol.6

Anton Yudhana, et al. K-NN Algorithm with Euclidean Distance for Predicting the Yield of Sengon Sawmills. 2020. TRANSMISSION

HAF Willmen TB Panjaitan, et al. Rice Harvest Prediction Using the k-Nearest Neighbor Algorithm. Pros. 5th Year SNATIVE. 2018. Pp. 621-628

M.S. Mustafa and IW Simpen . Designing a Timely Graduation Prediction Application for New Students with Data Mining Techniques (Case Study: Student Academic Data at STIMIK Dipanegara Makassar). Creat, Inf, Technol, J. 2015. Vol.1, no.4, pp.270-281

A. Hamdi, et al. The Timeseries K-Nearest Neighbor Regression Method in Predicting Outgoing Goods at the Warehouse of PT Putra Preneur Banjarbaru. Monday. Nas. Computing Science. 2019. Vol.2, pp.37-45

I. Drajana and C. Rally. Prediction of Cococnut Oil Production Using k-Nearest Neighbor and Backward Elimination. TECNOSCIENZA. 2018. Vol.13, no.1, pp. 51-64

N. Lizarti and AN Ulfah. Application of the k-Nearest Neighbor Algorithm for Determining Study Specialization (Case Study: STIMIK AMIK Riau Informatics Engineering Study Program). Fountain Informatics J. 2019. Vol.4, p.1-7

I. Riadi, et al. Handwriting Identification of Japanese Katakana Letters with the Euclidean Method. J-SAKTI (Journal of Computing and Information Science). 2020. Vol. 4, no. 1, p. 29

A. Fadlil and Saifudin. Wood Image Identification System Based on Texture Using Gray Level Coocurrence Matrix (GLCM) with Euclidean Distance Classification. 2015. Vol.19, no.3, pp.181-186

Sunardi, A. Fadlil, and Suprianto. Sentiment Analysis Using the Naive Bayes Classifier Method in Student Questionnaires. Saintekbu. 2018. Vol.10, no.2, pp. 1-9.

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Published

2023-02-17

Issue

Vol. 8 No. 1 (2023): Journal of Applied Intelligent System

Section

Articles

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