K-NEAREST NEIGHBOR (KNN) ANALYSIS FOR CLOTHING SALES CLASSIFICATION BASED ON MATERIALS USED

Jepri Banjarnahor; Regina Siregar; Christian Frederic Lumbantobing; Muhammad Alfathan Ridho; Muhammad Fikri Akbar Zuhdi

doi:10.34012/jurnalsisteminformasidanilmukomputer.v7i1.3903

PDF

Published: Aug 4, 2023

DOI: https://doi.org/10.34012/jurnalsisteminformasidanilmukomputer.v7i1.3903

Jepri Banjarnahor

Universitas Prima Indonesia

Regina Siregar

Universitas prima indonesia

Christian Frederic Lumbantobing

Universitas Prima Indonesia

Muhammad Alfathan Ridho

Universitas Prima Indonesia

Muhammad Fikri Akbar Zuhdi

Universitas Prima Indonesia

Abstract

Abstract Classification is a method to see the behavior and characteristics of certain groups. The K-nearest neighbor method is a learning algorithm for classifying new data based on the K-Nearest Neighbor majority class. The main purpose of this algorithm is to classify new objects based on attributes and training samples. In today's digital era, competition in the business world is getting tougher and growing rapidly, especially when it comes to online marketing systems. Every market driver must always pay attention to the needs and desires of consumer satisfaction when buying products from online stores. However, the problem that consumers often complain about is the use of clothing size charts in online stores that do not match the consumer's body size. This study aims to reduce the frustration of consumers buying clothes online and in such a way that products do not have to be returned via the internet. Based on these problems, these conditions must be improved by selecting clothes to achieve optimal customer satisfaction. This application was built using the K-Nearest Neighbor (KNN) method and Profile Matching to help you determine what clothes are most suitable for your consumer size.

How to Cite

[1]

J. Banjarnahor, R. Siregar, C. F. Lumbantobing, M. A. Ridho, and M. F. A. Zuhdi, “K-NEAREST NEIGHBOR (KNN) ANALYSIS FOR CLOTHING SALES CLASSIFICATION BASED ON MATERIALS USED”, JUSIKOM PRIMA, vol. 7, no. 1, pp. 42-49, Aug. 2023.

Issue

Vol. 7 No. 1 (2023): Jurnal Sistem Informasi dan Ilmu Komputer Prima (JUSIKOM Prima)

Section

Articles

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

Authors who publish their manuscripts through the Journal of Information Systems and Computer Science agree to the following:

Copyright to the manuscripts of scientific papers in this Journal is held by the author.
The author surrenders the rights when first publishing the manuscript of his scientific work and simultaneously the author grants permission / license by referring to the Creative Commons Attribution-ShareAlike 4.0 International License to other parties to distribute his scientific work while still giving credit to the author and the Journal of Information Systems and Computer Science as the first publication medium for the work.
Matters relating to the non-exclusivity of the distribution of the Journal that publishes the author's scientific work can be agreed separately (for example: requests to place the work in the library of an institution or publish it as a book) with the author as one of the parties to the agreement and with credit to sJournal of Information Systems and Computer Science as the first publication medium for the work in question.
Authors can and are expected to publish their work online (e.g. in a Repository or on their Organization's/Institution's website) before and during the manuscript submission process, as such efforts can increase citation exchange earlier and with a wider scope.

References

Alfani, A., Rozi, F., & Sukmana, F. (2021). Unilever Product Sales Prediction Using the K-Nearest Neighbor Method. JIPI (Scientific Journal of Informatics Research and Learning).

Amalia Rizki, Y. (2018). Application of Data Mining for Predicting Sales of Best Selling Electronic Products Using the K-Nearest Neighbor Method (Case Study: PT. Bintang Multi Sarana Palembang). Retrieved from http://eprints.radenfatah.ac.id/3302/

Annur, H. (2018). Classification of the Poor Using the Naïve Bayes Method. ILKOM Scientific Journal, 10, 6. Retrieved from http://103.226.139.203/index.php/ILKOM/article/view/303

Bode, A. (2017). K-Nearest Neighbor With Feature Selection Using Backward Elimination To Predict Arabica Coffee Commodity Prices. ILKOM Scientific Journal, 9(2), 188–195. https://doi.org/10.33096/ilkom.v9i2.139.188-195

Cholil, S., Handayani, T., & Tria, A. (2021). Implementation of the K-Nearest Neighbor (KNN) Classification Algorithm for Scholarship Recipient Selection Classification. IJCIT (Indonesian Journal on Computer and Information Technology), 6(2), 118–127.

Hakim, LAR, Rizal, AA, & Ratnasari, D. (2019). K-Nearest Neighbor (K-NN) Based Student Graduation Prediction Application. JTIM : Journal of Information Technology and Multimedia, 1(1), 30–36. https://doi.org/10.35746/jtim.v1i1.11 [3] Jauhari, nurdin, Edge Linking Detection and Comparison of the 3 Methods,http://ahtovicblogs.blog.ug.ac.id/?p=80, March 17, 2011 20.25

Harun, R., Pelangi, K., & Yuliyanti, L. (2020). Application of Mining Data to Determine Daily Rain Potential Using the K Nearest Neighbor (KNN) Algorithm. MISSION (Journal of Information Management & Information Systems). Retrieved from http://ejournal.stmiklombok.ac.id/index.php/misi/article/view/125/84

Hermawan, F., & Agung, H. (2017). Implementation of the K-Nearest Neighbor Method in the Sales Data Application of PT. Multitek Mitra Sejati.

Wahyuningsih, S., & Utari, DR (2018). Comparison of the K-Nearest Neighbor, Naive Bayes and Decision Tree Methods for Predicting Creditworthiness. Information Systems National Conference 2018 STMIK Atma Luhur Pangkalpinang, 8 – 9 March 2018, 619–623.

Lauwrenza, X., Fitriyah, H., & Syauqi, D. (2021). Design of a Shirt Size Classification System based on Body Size with the Arduino-based K-Nearest Neighbor Method. Journal of Information Technology Development and Computer Science, 6(1), 74-81.

Nikmatun, IA, & Waspada, I. (2019). Implementation of Data Mining for Classification of Student Study Period Using the K-Nearest Neighbor Algorithm. Symmetric: Journal of Mechanical Engineering, Electrical and Computer Science, 10(2),421-432.

Rizaldi, R., Kurniawati, A., & Angkoso, CV (2018). Implementation of the Euclidean distance method for clothing size recommendations in virtual dressing room applications. Journal of Information Technology and Computer Science, 5(2), 129-138.

Yolanda, Ike and Hasanul Fahmi, 2021, Application of Data Mining for Predicting Sales of Best Selling Bread Products at PT. Nippon Indosari Corpindo Tbk Using the K-Nearest Neighbor Method, Journal of Computer Science and Information Systems. E-ISSN: 2723-6129. pp. 9-15.

Widaningsih, Sri, 2019, Comparison of Data Mining Methods for Predicting Value and Graduation Time of Informatics Engineering Study Program Students with the C4.5 Algorithm, Naïve Bayes, K-NN and SVM, Incentive Tekno Journal. ISSN(p):1907-4964. Vol.13, No. 1 (pp. 16-25). [4] Jose, Stephane, Why Should I Care About SQL Server,http://blog.iweb.com/en/2010/06/why-should-i-care-about-sql-server/4772.html, March 17, 2011 19.30

Umam, Khaerul and Muhammad Hilman Fakhriza, 2021, Analysis of Best Selling Products Using the K-Means Clustering Method at PT. Sukanda Jaya, Journal of Informatics. E-ISSN: 2722-2713. p. 8-15.

Meliala, Meilida Dina, Penda Hasugian, 2020, Comparison of the K-Nearest Neighbor Algorithm with a Decision Tree in Predicting Pet Food Sales at Petshop Dore Vet Clinic, Information Technology Journal. ISSN: 1907-2430. Vol. XV (pp. 35-39).

Pradnyana, Aditra, Gede, Agus Aan Jiwa Permana, 2017, Comparison of K-Means and Hybrid K-Means KNN Algorithms for Class Division of Student Lectures, National Innovative Research Seminar. ISBN: 978-602-6428-11-0 (pp. 941-949).