KLASIFIKASI SPESIES BUNGA IRIS MENGGUNAKAN ALGORITMA KLASIFIKASI KNN DI RAPIDMINER
DOI:
https://doi.org/10.69714/0syd5n74Keywords:
Iris flower, classification, KNN algorithm, RapidMiner, data miningAbstract
The classification of Iris flower species based on morphological features is a crucial challenge in biological research and data science. This study aims to address this issue using the K-Nearest Neighbor (KNN) algorithm, implemented via RapidMiner, to automate and enhance the accuracy of the classification process. Fisher's Iris Dataset, consisting of 150 samples across three species (Iris setosa, Iris versicolor, and Iris virginica), was utilized. The research followed the Knowledge Discovery in Database (KDD) methodology, involving data preprocessing, model training, and evaluation. The results showed that the KNN algorithm achieved 100% accuracy in classifying the dataset, validating the effectiveness of both the algorithm and the RapidMiner platform for data mining. These findings underline the potential of KNN as a reliable tool for similar classification tasks.
References
R. Hamonangan, R. K. Sari, S. Anwar, and T. Hartati, “Klasifikasi Algoritma KNN dalam menentukan Penerima Bantuan Langsung Tunai,” vol. 6, no. 1, pp. 198–204, 2024.
A. Naufal Hilmi et al., “Implementasi Algoritma K-Nearest Neighbor (KNN) untuk Identifikasi Penyakit pada Tanaman Jeruk Berdasarkan Citra Daun,” no. 2, pp. 107–117, 2024, [Online]. Available: https://doi.org/10.62951/router.v2i2.78
Isman, Andani Ahmad, and Abdul Latief, “Perbandingan Metode KNN Dan LBPH Pada Klasifikasi Daun Herbal,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 3, pp. 557–564, 2021, doi: 10.29207/resti.v5i3.3006.
Y. finsha bintang prayoga, siswan syahputra, “KLASIFIKASI TINGKAT KEMATANGAN BUAH ASAM JAWA MENGGUNAKAN METODE K-NEAREST NEIGHBORS CLASSIFICATION OF MATURITY LEVEL OF TAMARIND FRUIT USING THE K-NEAREST,” vol. 1, no. 1, pp. 46–54, 2024.
Djaali, Metodologi Penelitian Kuantitatif. PT Bumi Aksara, 2021. [Online]. Available: https://www.google.co.id/books/edition/Metodologi_Penelitian_Kuantitatif/wY8fEAAAQBAJ?hl=id&gbpv=0
R. Iriane, “KLIK: Kajian Ilmiah Informatika dan Komputer Penerapan Data Mining Untuk Prediksi Penjualan Produk Pangan Hewan Menggunakan Metode K-Nearest Neighbor,” Media Online, vol. 3, no. 5, pp. 509–515, 2023, [Online]. Available: https://djournals.com/klik
S. D. Novita, A. Fauzi, and V. M. Pakpahan, “‘ Klasifikasi Citra Penyakit Gigi Menggunakan Metode K -Nearest Neighbor ’. STMIK Kaputama Binjai , Indonesia K-Nearest Neighbor,” no. 4, 2024.
Y. E. Yana and N. Nafi’iyah, “Klasifikasi Jenis Pisang Berdasarkan Fitur Warna, Tekstur, Bentuk Citra Menggunakan SVM dan KNN,” Res. J. Comput. Inf. Syst. Technol. Manag., vol. 4, no. 1, p. 28, 2021, doi: 10.25273/research.v4i1.6687.
M. Meiriyama, S. Devella, and S. M. Adelfi, “Klasifikasi Daun Herbal Berdasarkan Fitur Bentuk dan Tekstur Menggunakan KNN,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 9, no. 3, pp. 2573–2584, 2022, doi: 10.35957/jatisi.v9i3.2974.
A. Wasik et al., “Implementasi data mining untuk memprediksi penjualan accessoris handphone dan handphone terlaris menggunakan metode k-nearest neighbor (k-nn) 1,” vol. 1, no. 2, pp. 469–479, 2024
