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All Journal METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi
Sopany, Mikael Reichi
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Computer Science & IT Economics, Econometrics & Finance

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Leaf-Type Image Classification Using Deep Learning Method Convolution Neural Network Sopany, Mikael Reichi; Handhayani, Teny
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 9 No. 1 (2025): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol9No1.pp86-91

Abstract

One of the most important parts of an ecosystem is a plant, Plants life has given us many benefits from food, oxygen, and medicine. There are many species of plant each with its unique benefits and utilities. In this paper, we try to identify plants by their leaf using deep learning. For this research, we use the convolution neural network architecture Xception to classify 5 different types of leaves. We used 1075 images of leaves that can be classified into 5 different types of leaves. the classification model achieved an overall accuracy score of 74%. We hoped that the result of our research can help people's life by helping them to identify plants that they have so that they can use them for their benefit.
Leaf-Type Image Classification Using Deep Learning Method Convolution Neural Network Sopany, Mikael Reichi; Handhayani, Teny
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 9 No. 1 (2025): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol9No1.pp86-91

Abstract

One of the most important parts of an ecosystem is a plant, Plants life has given us many benefits from food, oxygen, and medicine. There are many species of plant each with its unique benefits and utilities. In this paper, we try to identify plants by their leaf using deep learning. For this research, we use the convolution neural network architecture Xception to classify 5 different types of leaves. We used 1075 images of leaves that can be classified into 5 different types of leaves. the classification model achieved an overall accuracy score of 74%. We hoped that the result of our research can help people's life by helping them to identify plants that they have so that they can use them for their benefit.
Co-Authors Teny Handhayani