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Maharani, Sanila
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering

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Klasifikasi Kualitas Selada Menggunakan Algoritma K-Nearest Neighbors dengan Nutrisi NPK Berdasarkan Fitur HSV Maharani, Sanila; Rusbandi, Rusbandi
MDP Student Conference Vol 4 No 1 (2025): The 4th MDP Student Conference 2025
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/mdp-sc.v4i1.11224

Abstract

Hydroponics is a soilless planting method that uses water containing essential nutrient solutions. Lettuce (Lactuca sativa) is a popular vegetable, including those grown hydroponically because of its high nutritional content. In hydroponics, plants depend on three main elements: Nitrogen (N), Phosphorus (P), and Potassium (K). Deficiency of one of these elements affects the color and quality of lettuce. This study aims to classify the nutritional quality of lettuce using the K-Nearest Neighbors (KNN) algorithm with NPK nutrients based on HSV features, with evaluation through Euclidean Distance. Lettuce is classified into four categories FN (Full Nutrition), K (Potassium), N (Nitrogen), and P (Phosphorus). The results showed the highest accuracy at K = 1 of 86.2%, precision 85.7%, recall 70.5%. So this method is proven effective for classifying the nutritional quality of lettuce.
Co-Authors rusbandi rusbandi, rusbandi