Klasifikasi Jenis Bunga Iris Menggunakan Algoritma Klasifikasi Tradisional - Alwi Syahputra; Rusma Riansyah; Dimas Aqila Aptanta; Muhammad Farhan; Mhd. Furqan (Jurnal ilmiah Sistem Informasi dan Ilmu Komputer, 2025)
This study aims to implement and compare the performance of two traditional classification algorithms, namely K-Nearest Neighbor (K-NN) and Naive Bayes to classify Iris flower types. The dataset used is the Iris Dataset which is a classic dataset in machine learning consisting of 150 samples with four features (sepal length, sepal width, petal length, and petal width) and three target classes (Iris Setosa, Iris Versicolor, and Iris Virginica). The research methodology includes data preprocessing, algorithm implementation, model evaluation using accuracy, precision, recall, and F1-score metrics, and comparative performance analysis. The results showed that the K-NN algorithm with k = 3 achieved an accuracy of 96.67%, while Naive Bayes achieved an accuracy of 93.33%. Both algorithms showed good performance in classifying Iris flower types, with K-NN slightly superior in terms of accuracy. This study proves that traditional classification algorithms are still relevant and effective for classification problems with less complex datasets.