Prediksi Pemilihan Warna Hijab Berdasarkan Tone Kulit Menggunakan Algoritma K-Nearest Neighbor (KNN)
Article Sidebar
Main Article Content
Abstract
Article Summary
Choosing the right hijab color that matches a person's skin tone is essential for many Muslim women to achieve a harmonious and attractive appearance. However, selecting a suitable color is often subjective and requires specific knowledge of color compatibility. This study aims to develop an automated prediction system that recommends hijab colors based on the user’s skin tone using the K-Nearest Neighbor (KNN) algorithm. KNN was chosen for its simplicity and effectiveness in classifying data based on proximity. The dataset used includes skin tone and corresponding hijab color data, collected through both primary and secondary sources. The classification process involves extracting color features from images and calculating Euclidean distances to determine the best hijab color prediction. The experimental results show that the KNN model provides fairly accurate predictions in recommending hijab colors based on skin tone. This system is expected to assist users in selecting appropriate hijab colors in a more objective and efficient manner.
Keywords
Article Keywords
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC-BY 4.0) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
License: All articles are licensed under 
Adenugraha, S. P., Arinal, V., & Mulyana, D. I. (2022). Klasifikasi kematangan buah pisang ambon menggunakan metode KNN dan PCA berdasarkan citra RGB dan HSV. J. Media Inform. Budidarma, 6(1), 9.
Amraee, S., Chinipardaz, M., & Charoosaei, M. (2022). Analytical study of two feature extraction methods in comparison with deep learning methods for classification of small metal objects. Visual Computing for Industry, Biomedicine, and Art, 5(1), 13.
Farokhah, L. (2020). Implementasi k-nearest neighbor untuk klasifikasi bunga dengan ekstraksi fitur warna rgb. Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK), 7(6), 1129-1135.
Halder, R. K., Uddin, M. N., Uddin, M. A., Aryal, S., & Khraisat, A. (2024). Enhancing K-nearest neighbor algorithm: a comprehensive review and performance analysis of modifications. Journal of Big Data, 11(1), 113.
Halim, A. A. D., & Anraeni, S. (2021). Analisis klasifikasi dataset citra penyakit pneumonia menggunakan metode k-nearest neighbor (KNN). Indonesian Journal of Data and Science, 2(1), 01-12.
Imani, R. K., Wijoyo, S. H., & Amalia, F. (2024). Penerapan Algoritma K-Nearest Neighbor untuk Klasifikasi Kemampuan Lulusan Siswa Dalam Bersaing untuk Mendapatkan Pekerjaan (Studi Kasus: SMK “SORE” Tulungagung). Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 8(10).
Mutlag, W. K., Ali, S. K., Aydam, Z. M., & Taher, B. H. (2020, July). Feature extraction methods: a review. In Journal of Physics: Conference Series (Vol. 1591, No. 1, p. 012028). IOP Publishing.
Nasyah, R., & Wilianca, A. (2024). D., & Akbar, A.(2024). Hijab Trend: Combining Religious Values And Modern Fashion. Journal of Multidisciplinary Sustainability Asean, 1(5), 304-312.
Nugraha, W. Y. Aplikasi Kecocokan Warna Baju Berdasarkan Warna Kulit (Doctoral dissertation, Universitas Kanjuruhan Malang).
Prasath, V. B., Alfeilat, H. A. A., Hassanat, A., Lasassmeh, O., Tarawneh, A. S., Alhasanat, M. B., & Salman, H. S. E. (2017). Distance and similarity measures effect on the performance of K-nearest neighbor classifier--a review. arXiv preprint arXiv:1708.04321.
Prihatini, P. M. (2016). Implementasi Ekstraksi Fitur Pada Pengolahan Dokumen Berbahasa Indonesia. Jurnal Manajemen Teknologi dan Informatika (MATRIX), 6(3), 174-178.
Sakti, R., & Daulay, A. Analisis Kritis dan Pengembangan Algoritma K-Nearest Neighbor (KNN): Sebuah Tinjauan Literatur. vol, 4, 131-141.
Semenkin, A., Sokolov, Y., & Vu, E. (2024, April). Context Composing for Full Line Code Completion. In Proceedings of the 1st ACM/IEEE Workshop on Integrated Development Environments (pp. 15-17).
Smirnov, O., Lobanov, A., Golubev, Y., Tikhomirova, E., & Bryksin, T. (2021, November). Revizor: A Data-Driven Approach to Automate Frequent Code Changes Based on Graph Matching. In 2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE) (pp. 1242-1246). IEEE.