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All Journal Jurnal Sain Veteriner Proceedings of Annual International Conference Syiah Kuala University - Life Sciences & Engineering Chapter Dentika Dental Journal Jurnal Ilmiah Mahasiswa Veteriner JURNAL PENDIDIKAN TAMBUSAI Jurnal Review Pendidikan dan Pengajaran (JRPP) Ainet : Jurnal Informatika Al-Bukhari: Jurnal Ilmu Hadis Atta'dib Jurnal Pendidikan Agama Islam Nusantara Hasana Journal Indonesian Community Journal Jurnal Informatika Progres Jurnal Pengabdian kepada Masyarakat Jurnal Kedokteran Hewan The International Journal of Tropical Veterinary and Biomedical Research Jurnal Ilmiah Multidisiplin Amsir Jurnal Indonesia : Manajemen Informatika dan Komunikasi Advances in Management & Financial Reporting Jurnal Medika Veterinaria Jurnal Agripet Bata Ilyas Educational Management Review Jurnal Profesi Guru Indonesia Jurnal Pendidikan Dasar
. Darniati
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KLASIFIKASI TANAMAN OBAT TRADISIONAL BERBASIS CITRA BUAH DAN DAUN Kusumawardani, Nurul; Danuputri, Chyquitha; Darniati; Faisal, Muhammad; A.M Hayat, Muhyiddin; S. Kuba, Muhammad Syafaat; Anggreani, Desi
PROGRESS Vol 18 No 1 (2026): April
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56708/progres.v18i1.534

Abstract

Indonesia is a megabiodiversity country with extensive use of traditional medicinal plants; however, plant identification in natural environments remains largely manual and error-prone. Recent advances in deep learning, particularly Vision Transformer (ViT), provide a promising solution by effectively capturing global spatial features for image classification. This study applies a ViT-Base/16 model to automatically classify fruit and leaf images of Indonesian medicinal plants. The dataset comprises 1,000 field-collected images from Galung Village, West Sulawesi, covering 20 classes (10 medicinal and 10 non-medicinal plants). The model was fine-tuned using the AdamW optimizer with a learning rate of 2×10⁻⁵ and trained for 30 epochs with cosine annealing. The proposed approach achieved high performance, with 99.33% accuracy, 99.41% precision, 99.33% recall, and a 99.33% F1-score, while binary classification between medicinal and non-medicinal plants reached 100% accuracy. The system was deployed as a Flask-based web application, demonstrating reliable functionality and practical response times. Overall, the results confirm the effectiveness of Vision Transformer for medicinal plant classification under natural conditions and highlight its potential to support digital documentation, education, and the preservation of local ethnobotanical knowledge.
Co-Authors . Darmawi . Darmawi . Darmawi . Fakhrurrazi . Muthmainnah . Syafruddin ., arneta Abdil lah Imron Nasution Abdul Harris Abdul Wahid Abdullah Hamzah Agusni Yahya Agustin Indrawati Ahmad, Muh Ihsan Said AK, M Daud Akbar, Muh Akbar, Muh Ilham Alvina Felicia Watratan Andi Novita Andi Novita Anggreani, Desi Ansar Ansar Arifuddin Arifuddin Arman Sayuti Ayuti, Siti Rani Azhari Azhari Azmansyah Azmansyah Baharuddin, Suardi Hi Bakti, Rizki Yusliana Balqis Alrasyid Suyoto Basri Gani Bunganan , Rey Richard Chyquitha Danuputri Cut Nila Thasmi Cut Soraya Danuputri, Chyquitha Dara Hayati Rapi Darmawi Darmawi Darnanengsih Darnanengsih Diva, Farah Dwi Rosa Selfiana Erina Erina Erina Erina Erina Erina Erina Erina Erina Erina Erina Erina Erina Erina Erina Erina Ety Mukhlesiyeni Fadlah, Iga fadli amri Fahkrurrazi Fahkrurrazi Faisal Jamin Faisal Jamin Faisal Jamin Faisal Jamin Fakhrurrazi Fakhrurrazi Fakhrurrazi Fakhrurrazi Florentina Magdalena Girsang Ginta Riady Ginta Riady Habi Talib, Emil Agusalim Hamdani Budiman Haruna, Suardi B. Hasibuan, Asnita Herrialfian, Herrialfian Iccha Elvioleta Ida Ida Mulyadi, Ida Isa, M Ismail Ismail Khoirunnisa, Fathonah Kholifah, Ida Kusumawardani, Nurul Latifa Suryandari LIA PERMATA SARI Lisa Fitriani Ishak M Daud AK M Nur Salim M. Nur Salim Mahdi Abrar Mahdi Abrar Mahdi Abrar Maryulia Dewi Maryulia Dewi Maryulia Dewi Masda Admi Mirna Safrani Fauzi Muhammad Amin muhammad aroza Muhammad Faisal Muhammad Faisal Muhammad Faisal Muhammad Hambal Muhsal, Muhammad Ilham Muhyiddin A.M Hayat Musdalifa Thamrin Mustari Mustari Muttaqien Bakri Muttaqien Muttaqien Nasir Usman Nazaruddin Nazaruddin nisma hayani Nugraha, Ronaldi Fajar Nur Ramadhan, Nur Nurahmad Nurfadillah Nurhayati NURLIANA NURLIANA Nurwahida Nurwahida, Nurwahida Nuzul Asmilia Olivia salsa dilla putri Pratiwi, Zahwa Amelia Prihatmono, Medy Wisnu Rahmadhini, Vivi Rahmadia Fitri Rastina Rastina Razali Daud Resti Reimena Rezi Maghfira Rezky, Annisyah Rima Rizky Amiruddin Rinidar Rinidar Risal riza maulita Robbi Ghani Roslizawaty Roslizawaty S. Kuba, Muhammad Syafa'at Safika S, Safika Saharuddin Salehha, Osey Putri Samsuria, Samsuria Sarea, Muh Syahrul Sari Dewi Sari, Wahyu Eka Satriani Satriani Silvia P.N Keliat Surachmi Setiyaningsih T. Armansyah T. Armanyah TR Tadampali, Andi Caezar To Teuku R. Ferasyi Teuku Reza Ferasyi Thamrin, Musdalifa Tiong, Piter Titin Wahyuni Tongku N Siregar Ummu Balqis wahyu eka sari Wanini, Wanini Windian Tajuk Masmah Bengi Yudha Fahrimal Zainuddin, Zainuddin Zakiah Heryawati Manaf Zakiyah Heryawati Manaf Zinatul Hayati Zuhrawati NA