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All Journal Insect (Informatics and Security) : Jurnal Teknik Informatika
Katmas, Maskia
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Computer Science & IT Control & Systems Engineering Engineering

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Implementasi Deep Learning Pada Kematangan Buah Pala Menggunakan Convolutional Neural Network Berbasis Android Soekarta, Rendra; Pangri, Muzakkir; Katmas, Maskia
Insect (Informatics and Security): Jurnal Teknik Informatika Vol. 10 No. 1 (2024): Maret 2024
Publisher : Universitas Muhammadiyah Sorong

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33506/insect.v10i1.3650

Abstract

Nutmeg, also known by its Latin name Myristica Fragrans, is a tree-like plant that is rich in benefits. As a spice, the plant is native to the Maluku islands and has a high value. Therefore, Papuan Nutmeg is referred to as a native and endemic species on the island of Papua. However, the distribution of Papuan Nutmeg is mostly in West Papua, especially Fakfak Regency. Nutmeg farmers can generally assess the ripeness of nutmeg by observing its colour, as this is the simplest method. Although this method is easy, there are several obstacles that make the nutmeg selection process less effective, especially if done manually. In this research, a system is needed that aims to detect nutmeg based on the level of maturity using Convolutional Neural Network (CNN). The dataset used is an image with a total of 600 images, which are grouped into 3 classes. The results of the implementation of deep learning in the detection of nutmeg maturity level carried out in this study using Convolutional Neural Netwok (CNN) with VGG16 architecture can classify the maturity level of nutmeg with an accuracy level of 98% for precission, 98% for recall and 98% for f1-score.
Co-Authors Soekarta, Rendra