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Nova Agustina
Universitas Teknologi Bandung, Indonesia
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Biomedical Image Processing for Enhanced Classification of Medicinal Plants Using Voting Ensemble Nova Agustina; Candra Nur Ihsan; Kelik Sussolaikah
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 15 No. 1 (2026)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v15i1.95457

Abstract

Indonesia harbors extensive biodiversity, including many medicinal plants used in health. Accurate and scalable identification remains challenging because of high intra class variation, strong visual similarity among species, and variable capture conditions in the field. This study evaluates three convolutional neural networks for Indonesian medicinal plant classification: ResNet18, VGG16, and EfficientNet_B0, and an average voting ensemble that aggregates their class probabilities. Using a held-out test set, ResNet18 achieves 87.00% accuracy, 91.16% precision, 87.00% recall, and 87.35% F1 score; VGG16 records 63.50% accuracy, 63.89% precision, 63.50% recall, and 61.10% F1 score; EfficientNet_B0 attains 94.00% accuracy, 94.49% precision, 94.00% recall, and 94.01% F1 score; the ensemble reaches 93.00% accuracy, 94.99% precision, 93.00% recall, and 93.22% F1 score. Confusion matrix analysis indicates that remaining errors concentrate on visually similar pairs, notably Aloe Vera versus Green Tea and Basil Leaf versus Green Tea. The findings show that EfficientNet_B0 is the strongest single backbone, while the ensemble improves precision and stability with accuracy close to the best individual model. These results demonstrate a practical pathway for reliable, field deployable recognition of medicinal plants in Indonesia. Future work will expand the dataset across seasons and regions, increase class balance, and explore stronger augmentation, focal or class weighted losses, and probability calibration. We also plan segmentation assisted pipelines, staged fine tuning at higher resolution, and lightweight deployment through pruning and quantization on mobile or embedded devices. To support reproducibility and adoption, the training pipeline and plotting scripts are made available in an open Kaggle notebook.
Co-Authors Abdul Fatah Aditiya Suryana Adrian, Adrian Ahmad Taufik Andrian Cahyanto Antika, Azizah Qolbu Ardini, Siska Nova Arizal Sabila Nurhikam Azril Tazidan Octa Nuryawan Botha, Marten Candra Nur Ihsan Danny Aidil Rismayadi Deni Rinaldi Dini Rohmayani Diyah Wijayati Elisabeth, Grace Erlin Fahmi Abdullah, Fahmi Fahrullah, Fahrullah Falah, Hadi Mirojul Guterres, Antonio Hadah Muallimah Hadhiwibowo, Ari Harya Gusdevi Ihsan, Candra Nur Ipan Saepul Milal Irfan Ahmad Baid Irfan Ahmad Baid Jack Febrian Rusdi Jamil, Muhammad Ridwan Joko Herwanto Junadhi Junadhi Kelik Sussolaikah Komara, Deri Laili Suryati M. Hasanudin M. Hasanudin Mamun Hasbullah Marlim, Yulvia Nora Mercy Hermawati Miftahul Rizal Mochamad Saidiman Mochammad Aliffiallathifa Nur Azhari Muchammad Naseer Muhamad Fauzan Rajab Muhammad Fadhil Muttaqin Nur Sakti Nur Sakti Prihandoko . Puji Rahmah Puji Rahmah Raden Meina Widiastuti Rahayudin Rahayudin Rahma Salsa Bilah Ratih Ratnasari Rifki Aditya Nugraha Riri Mardaweni Rivaldi Syaputra Riyanti, Niken Rusdan, Muchamad Saepul Rohman Sandi Hermawan Satrio Rully Priyambodo Sayuti Sintia Dewi Slamet Risnanto Sri Erina Damayanti Sri Kuswayati Suherman Suherman Sumita Wardani Supriadi, Irwin Sussolaikah, Kelik Syifa Nur Fauziah Tatang Bukhori Yandy, Eza Tri Yanto Yanto