KLASIFIKASI SPESIES BUNGA IRIS MENGGUNAKAN ALGORITMA KLASIFIKASI KNN DI RAPIDMINER - Zainur Rahman; Zaehol Fatah; Jarot Dwi Prasetyo (Jurnal Ilmiah Multidisiplin Ilmu, 2024)
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.