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All Journal Lontar Komputer: Jurnal Ilmiah Teknologi Informasi
osphanie mentari
UNIVERSITAS ISLAM NUSANTARA
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Multi Classification of Strawberry Leaves Using Support Vector Machine (SVM) Method on Smart Greenhouse Plants Based on Internet Of Things (IoT) osphanie mentari; Agung Surya Wibowo; Muhammad Zimamul Adli; Agung Muhamad Toha
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol. 16 No. 02 (2025): Vol.16, No. 02 August 2025
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2025.v16.i02.p07

Abstract

Strawberry plants, or Fragaria x ananassa, are shrubs in the Rosaceae (rose) family that produce sweet and scented red fruit. Strawberries are high in vitamin C and other minerals. The benefits of growing strawberries in smart greenhouses are one of the hydroponic farming sectors advances. The construction of a smart greenhouse system that can be monitored and controlled automatically simplifies agricultural research, which formerly relied on traditional farming and wet labs with long study timeframes and high expenses. This innovation makes it easier for researchers to study the impact of the Internet of Things (IoT) on strawberry plant growth by using several sensors in the greenhouse and Artificial Intelligence (AI) to save time and money in optimizing strawberry plant growth. Meanwhile, the Support Vector Machine (SVM) algorithm with a multi-classification category on leaves 3, 4, and 5 achieved Precision: 0.96, Recall: 0.95, F1-Score: 0.95, and Accuracy: 0.95. The accuracy level reaches 95%, implying that machine learning can be used in strawberry cultivation to assist hydroponic farmers.
Co-Authors Agung Muhamad Toha Agung Surya Wibowo Muhammad Zimamul Adli