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All Journal ComEngApp : Computer Engineering and Applications Journal Jurnal EECCIS JOIV : International Journal on Informatics Visualization Journal of Environment and Sustainability Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Window of Health : Jurnal Kesehatan JSAI (Journal Scientific and Applied Informatics) Jurnal Teknik Informatika (JUTIF)
Arie Vatresia
Program Studi Teknik Infomatika, Fakultas Teknik, Universitas Bengkulu.
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Environmental Science Medicine & Pharmacology Nursing Public Health

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Herbal Plant Classification Using EfficientNetV2B0 Model and CRISP-DM Approach Sonita, Anisya; Anggriani, Kurnia; Vatresia, Arie; Putri, Tiara Eka; Darnita , Yulia; Zahra, Syakira Az; Aprilia, Vilda; Aziz, Dzakwan Ammar
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.5141

Abstract

Herbal remedies have long been utilized by Indonesian communities as part of traditional medicine. However, identification of these natural resources is often challenging due to the morphological similarities among various species, which demand expert knowledge to differentiate. This study aims to implement the EfficientNetV2B0 model architecture for classifying medicinal leaves through an Android-based application designed to support recognition tasks. The dataset was composed of augmented images of plant foliage. The model was trained using the TensorFlow framework and evaluated to measure classification performance. Results demonstrate that EfficientNetV2B0 achieves excellent accuracy, with validation scores exceeding 97%, outperforming several other deep learning models. The resulting application allows the general public to identify local medicinal species more easily. This study contributes to the field of computer vision by providing an accurate and efficient classification framework, particularly beneficial for health-related informatics in biodiversity-rich regions.
Co-Authors Agustian, Indra Aprilia, Vilda Asahar Johar Aziz, Dzakwan Ammar Bismantolo, Putra Daratha, Novalio Darnita , Yulia Dedi Suryadi Faurina, Ruvita Febrianti, Tiara E Gupta, Neeraj Ida Ayu Putu Sri Widnyani Kennedy, John Kurnia Anggriani, Kurnia Lestari, Etika Dwi Okta Piani, Sri Prasetyo, Bimo Putri, Tiara Eka Rahma, Silvia Rais, Rendra Regen Rais, Rendra Regen Regen, Rendra Sadler, Jonathan P. Santosa, Hendy Sonita, Anisya Utama, Ferzha Utama, Ferzha Putra Zahra, Syakira Az