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All Journal JURNAL KARYA TEKNIK SIPIL AcTion: Aceh Nutrition Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Risalah Kebijakan Pertanian dan Lingkungan Indo-MathEdu Intellectuals Journal Kreatifitas: Jurnal Ilmiah Pendidikan Islam Al-Marsus : Jurnal Manajemen Pendidikan Islam Paramacitra : Jurnal Pengabdian Masyarakat
Salsabila, Sarah
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Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Education Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Mathematics Medicine & Pharmacology Public Health Social Sciences Other

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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Comparison of Deep Learning Architectures in Identifying Types of Medicinal Plant Leaf Images Salsabila, Sarah; Suharso, Aries; Purwantoro, Purwantoro
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.6289

Abstract

This study focuses on the identification of 3500 images of medicinal plant leaves using Deep Learning CNN Transfer Learning models such as MobileNet, VGG16, DenseNet121, ResNet50V2, and NASNetMobile. The dataset used is the "Indonesian Herb Leaf Dataset 3500," consisting of 10 classes of medicinal plants. This research has the potential to efficiently and accurately recognize medicinal plants using machine learning workflow methods. The objective of this study is to compare the performance of these five methods in conducting plant identification. The testing phase involves various data handling schemes, dividing the data into two scenarios: 80:10:10 and 70:20:10. Performance comparison is also done between augmented and non-augmented data. The research findings indicate that MobileNet exhibits the best performance with an accuracy, precision, recall, and f1-Score of 98.86%. Accurate leaf identification supports further research on the properties and benefits of medicinal plants and can be applied in the development of decision support systems for plant recognition.
Co-Authors Abdul Karim, Hamdi Agief Julio Pratama Aries Suharso Armedo, Muhammad Azni Aisyah Charles Charles Dewi, Metha Frisila Frida Kistiani Ghaida, Ni'ma Harbes, Beni Istianah, Isti Iswantir M M.Agung Wibowo Mia Srimiati Purwantoro Ratnayani Ridha Ahida Safitri, Pebriani Sakura, Meyra Sukmawati, Reka Kurnia Syukri Syukri Wedra Aprison Widya Hasian Situmeang Wulandari, Inayah Yusritawati , Ita Zulfani Sesmiarni