IMAGE SEARCHING DATA GAMBAR BERWARNA ALGORITMA K MEANS CLUSTERING PADA DATA PENJUALAN

Authors

  • Ariadi Retno Hayati Politeknik Negeri Malang

DOI:

https://doi.org/10.36080/skanika.v5i2.2943

Keywords:

Image Searching, K Means Clustering, Scilab, Data Gambar Warna Produk Penjualan

Abstract

Pencarian data dengan data gambar berwarna yang diterapkan pada penelitian ini adalah data penjualan dengan dataset gambar berwarna. Terdapat beberapa produk yang akan dideskripsikan yaitu produk makanan, produk bahan pokok, produk rumah tangga yang akan dikelompokkan berdasarkan kategori kelompok dengan kategori produk yang sama. Metode yang diterapkan adalah K Means Clustering pada penelitian ini dengan data pembelajaran dan data uji coba yang berbeda. Sistem pencarian gambar dengan memilih file gambar dari data uji coba dan sistem akan mencari kelompok dari data pembelajaran dan ditampilkan gambar hasil pencarian untuk mempermudah pengguna aplikasi mencari data penjualan jika tidak memiliki atau kesulitan mencari informasi nama produk atau kode produk. Proses pengolahan data warna baik data gambar berwarna pada data pembelajaran dan data uji coba harus melalui proses normalisasi data warna dimana proses ini meliputi pemilihan objek, pemotongan objek, dan menyamakan ukuran gambar objek. Hasil pembelajaran pengelompokan K Means Clustering mendapatkan hasil yang baik, pada penelitian ini mendapatkan prosentase 93,33% pada data pembelajaran dan 60% pada data uji coba.

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References

[1] Singh, Laxman, and Joshi, Mauli, Face Detection System Using Scilab Image Processing Tool, IJETCR, vol. 3, no. 3, pp.84-90, 2015.
[2] Cassim Alibuhttoa, Mohamed, and Idayu Mahat, Nor., Distance Based K-Means Clustering Algorithm for Determining Number of Clusters for High Dimensional Data, Decision Science, vol. 9, no.1, pp.51-58, 2020.
[3] Chandra, Bhushit., Mandhaniya, Khushboo., Proposed Algorithm for Content Based Image Recognition Using Enhanced K-Means Clustering Algorithm, IJESRT, vol. 6, no. 10, pp. 184-190, 2017.
[4] Sharma, Anamika., Sharma, Sarita., A Framework for Picture Extraction on Search Engine Improved and Meaningful Result, IJCSI, vol. 8, no. 5, pp. 1-5, 2011.
[5] Chavan, Urvashi., Shahane, N.M., Content Based Image Retrieval Using Clustering, IJAIEM, vol. 3, no. 10, pp. 181-184, 2014
[6] Ramteke, Komal., Bhad, Ashwini Vinayak., Feature Extraction Approach for Content Based Image Retrieval, IJARCCE, vol. 4, no. 11, pp. 63-68, 2015.
[7] Kamshetty, Shradha., Kasar, Shivani., Kadu, Mrunalini., Nanajkar, J.A., Content Based Image Retrieval in Java Using K-Means Clustering Algorithm, IJCRT, vol. 6, no. 2, pp. 1492-1497, 2018.
[8] Mundada, Ritu A., Waghmare, Akash D., An Approach for Content Based Image Retrieval Using Similarity Measures, IJCST, vol. 4, no. 3, pp. 124-128, 2016.
[9] Moriwal, Rahul, Survey Paper on Content Based Image Retrieval, IJSRD, vol. 7, no. 7, pp. 190-193, 2019.
[10] Wu, Chunqiong., Yan, Bingwen., Yu, Rongrui., Yu, Baoqin., Zhou, Xiukao., Yu, Yanliang., Chen, Na., K-Means Clustering Algorithm and Its Simulation Based on Distributed Computing Platform, HINDAWI, vol. 1, pp.1-10, 2021.
[11] Ismia, Dewi Pramudi., Panchoob, Shireen., Murintoc, K-Means Clustering Based Filter Feature Selection on High Dimensional Data, International Journal of Advances in Intelligent Informatics, vol. 2, no. 1, pp. 38-45, 2016.
[12] Umesh., Suresha., Advanced Web Image Retrieval Using Clustering Algorithms, IJMA, vol. 3, no. 4, pp. 159-169, 2011.
[13] Hu, YuPing., Yin, Hua., Han, Dezhi., Yu, Fei., The Application of Similar Image Retrieval in Electronic Commerce, HINDAWI, pp.1-7, 2014.
[14] Irianto, Eko., Sensune, Dana Indra., Sistem Monitoring Berbasis Project Management: PT BFI Finance Indonesia, Tbk, Prosiding Seminar Nasional Multidisiplin Ilmu Universitas Budi Luhur, ISSN: 2087 – 0930.

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Published

2022-07-26

How to Cite

[1]
A. R. Hayati, “IMAGE SEARCHING DATA GAMBAR BERWARNA ALGORITMA K MEANS CLUSTERING PADA DATA PENJUALAN”, SKANIKA, vol. 5, no. 2, pp. 203–215, Jul. 2022.

Issue

Vol. 5 No. 2 (2022): Jurnal SKANIKA Juli 2022

Section

Articles

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