Implementasi Deep Learning Pada Kematangan Buah Pala Menggunakan Convolutional Neural Network Berbasis Android

Rendra Soekarta; Muzakkir Pangri; Maskia Katmas

doi:10.33506/insect.v10i1.3650

Authors

Rendra Soekarta Program Studi Teknik Informatika, Universitas Muhammadiyah Sorong
Muzakkir Pangri Teknik Informatika
Maskia Katmas Program Studi Teknik Informatika, Universitas Muhammadiyah Sorong

DOI:

https://doi.org/10.33506/insect.v10i1.3650

Keywords:

Nutmeg, Detection, Deep Learning, Convolutional Neural Network, Android

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.

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