Application of the Naïve Bayes and K-Nearest Neighbor Methods for Classifying Roses
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Published:
Aug 30, 2023
Keywords:
Classification, K-NN, Naive Bayes Roses
Section
Decision Support System
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Abstract
They are faced with the rapid development of plant science, especially the science of rose flora. The rose has a sweet-smelling charm; beautiful color. Many people, like roses, are deliberately cultivated by the beauty industry as the main ingredient in making cosmetics. Roses have various varieties, and the types have similarities, so it is difficult to distinguish, know and determine the varieties of roses; in plain view, it requires a long time and precision. In this study, the Naïve Bayes and K-Nearest Neighbor applications were used. Algorithms will be carried out for the classification of roses in addition to proving the identification and classification of rose varieties based on morphological characteristics using K-NN and Naïve Bayes to understand the diversity of roses. The Naive Bayes method produced maximum accuracy with little training data. Meanwhile, K-Nearest Neighbor was chosen because it is robust against noise data. The performance of the two methods will be compared to determine which method is better for classifying roses. The results show that the Naive Bayes method performs better, with an accuracy rate of 75%, while the K-Nearest Neighbor method has an accuracy rate of 62.5%.
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How to Cite
Gunawan, G., Andriani, W. and Santoso, A. A. . (2023) “Application of the Naïve Bayes and K-Nearest Neighbor Methods for Classifying Roses”, Jurnal Mantik, 7(2), pp. 799-809. doi: 10.35335/mantik.v7i2.3911.
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Cranford, S. W. (2020). Seven Seconds or Less: Buzzwordy Titles in the Era of MOFs and Tinder. Matter, 3(4), 965–967.
Cronje, J. C. (2020). Designing Questions for Research Design and Design Research in e Learning. Electronic Journal of E-Learning, 18(1), pp13-24.
Farokhah, L., & Korespondensi, P. (2020). Implementasi K-Nearest Neighbor Untuk Klasifikasi Bunga Dengan Ekstraksi Fitur Warna Rgb Implementation of K-Nearest Neighbor for Flower Classification With Extraction of Rgb Color Features. Jurnal Teknologi Informasi Dan Ilmu Komputer (JTIIK), 7(6), 1129–1136. https://doi.org/10.25126/jtiik.202072608
Fryer, L. K., & Dinsmore, D. L. (2020). The Promise and Pitfalls of Self-report: Development, research design and analysis issues, and multiple methods. Frontline Learning Research, 8(3), 1–9.
Grieshaber, S. (2020). Equity and research design. In Doing early childhood research (pp. 177–191). Routledge.
Heydarian, M., Doyle, T. E., & Samavi, R. (2022). MLCM: Multi-label confusion matrix. IEEE Access, 10, 19083–19095.
Karunia Ayuningsih. (2018). Moment Dan Local Binary Pattern Dengan. Klasifikasi Citra Makanan Menggunakan Hsv Color Moment Dan Local Binary Pattern Dengan Naïve Bayes Classifier.
Liantoni, F., & Cahyani, L. (2017). Pemanfaatan Hierarchical Clustering Untuk Pengelompokkan Daun Berdasarkan Fitur Moment Invariant. Edutic - Scientific Journal of Informatics Education, 3(2), 91–98. https://doi.org/10.21107/edutic.v3i2.2932
Liantoni, F., & Nugroho, H. (2015). Klasifikasi Daun Herbal Menggunakan Metode Naïve Bayes Classifier Dan Knearest Neighbor. Jurnal Simantec, 5(1), 9–16.
Lubis, A. R., & Lubis, M. (2020). Optimization of distance formula in K-Nearest Neighbor method. Bulletin of Electrical Engineering and Informatics, 9(1), 326–338.
Manik, F. Y., & Saragih, K. S. (2017). Klasifikasi Belimbing Menggunakan Naïve Bayes Berdasarkan Fitur Warna RGB. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 11(1), 99. https://doi.org/10.22146/ijccs.17838
Marthin Luter Laia, & Yudi Setyawan. (2020). Perbandingan Hasil Klasifikasi Curah Hujan Menggunakan Metode SVM dan NBC. Jurnal Statistika Industri Dan Komputasi, 5(2), 51–61.
Meiriyama, M., Devella, S., & Adelfi, S. M. (2022). Klasifikasi Daun Herbal Berdasarkan Fitur Bentuk dan Tekstur Menggunakan KNN. JATISI (Jurnal Teknik Informatika Dan Sistem Informasi), 9(3), 2573–2584. https://doi.org/10.35957/jatisi.v9i3.2974
Pratama, A. R., Mustajib, M., & Nugroho, A. (2020). Deteksi Citra Uang Kertas dengan Fitur RGB Menggunakan K-Nearest Neighbor. Jurnal Eksplora Informatika, 9(2), 163–172. https://doi.org/10.30864/eksplora.v9i2.336
Prayogo, S., Chamid, A. A., & Murti, A. C. (2022). Perancangan Sistem Klasifikasi Jenis Bunga Mawar Menggunakan Metode K-Nearest Neighbor (Knn). Indonesian Journal of Technology, Informatics and Science (IJTIS), 3(2), 52–56. https://doi.org/10.24176/ijtis.v3i2.7881
Riadi, I., Umar, R., & Aini, F. D. (2019). Analisis Perbandingan Detection Traffic Anomaly Dengan Metode Naive Bayes Dan Support Vector Machine (Svm). ILKOM Jurnal Ilmiah, 11(1), 17–24. https://doi.org/10.33096/ilkom.v11i1.361.17-24
Saragih, M., & Novimariono, N. (2020). An Experimental Study of The Effectiveness PEOW MODEL Through Applying Quartet Card in Teaching English Writing. Indonesian Journal of Education, Social Sciences and Research (IJESSR), 1(1), 32–40.
Sari, W. S., & Sari, C. A. (2022). Klasifikasi Bunga Mawar Menggunakan Knn Dan Ekstraksi Fitur Glcm Dan Hsv. Skanika, 5(2), 145–156. https://doi.org/10.36080/skanika.v5i2.2951
Shu, K., Mahudeswaran, D., Wang, S., Lee, D., & Liu, H. (2020). Fakenewsnet: A data repository with news content, social context, and spatiotemporal information for studying fake news on social media. Big Data, 8(3), 171–188.
Stewart, E. (2021). Detecting Fake News: Two Problems for Content Moderation. Philosophy & Technology, 1–18.
syaeful achmad, ilham fadilah muhammad, muftadi imam, & iskandar dadang. (2022). Klasifikasi Citra Bunga Dahlia Berdasarkan Warna Menggunakan Metode Otsu Thresholding Dan Naïve Bayes. Sains Komputer & Informatika, 6, 575–582.
Syam, A. A., Rifka, S., & Aulia, S. (2021). Implementasi Pengolahan Citra Untuk Identifikasi Daun Tanaman Obat Menggunakan Levenberg-Marquardt Backpropagation. Elektron?: Jurnal Ilmiah, 13, 1–8. https://doi.org/10.30630/eji.0.0.176
Utami, P. N., Arinal, V., & Mulyana, D. I. (2022). Klasifikasi Dehidrasi Tubuh Manusia Berdasarkan Citra RGB Pada Warna Urine Menggunakan Metode K-Nearest Neighbor. Jurnal Media Informatika Budidarma, 6(1), 18. https://doi.org/10.30865/mib.v6i1.3290
van der Giesen, C., Cucurachi, S., Guinée, J., Kramer, G. J., & Tukker, A. (2020). A critical view on the current application of LCA for new technologies and recommendations for improved practice. Journal of Cleaner Production, 259, 120904
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