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All Journal Bulletin of Electrical Engineering and Informatics JUTI: Jurnal Ilmiah Teknologi Informasi Scan : Jurnal Teknologi Informasi dan Komunikasi Proceeding International Conference on Information Technology and Business Jurnal Informatika dan Teknik Elektro Terapan International conference on Information Technology and Business (ICITB) JURNAL MEDIA INFORMATIKA BUDIDARMA JIEET (Journal of Information Engineering and Educational Technology) Indonesian Journal of Artificial Intelligence and Data Mining SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Antivirus : Jurnal Ilmiah Teknik Informatika JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Journal of Information Systems and Informatics bit-Tech Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics Jifosi TIN: TERAPAN INFORMATIKA NUSANTARA Nusantara Science and Technology Proceedings Jurnal Ilmiah Teknologi Informasi dan Robotika Algoritme Jurnal Mahasiswa Teknik Informatika Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer Sewagati
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Universitas Pembangunan Nasional Veteran Jawa Timur
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Journal : bit-Tech

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Implementation of MobileNetV3-Large in Rhizome Classification Nurdiansyah N.A, M. Ryan; Via, Yisti Vita; Nurlaili, Afina Lina
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3365

Abstract

Rhizomes are widely used in Indonesia as cooking spices and herbal ingredients, yet their visual similarity often causes misidentification when recognition relies on manual inspection, leading to inconsistent product quality and economic losses. This study presents an automatic rhizome image classification system based on the MobileNetV3-Large architecture to distinguish eight Indonesian rhizome types (bangle, ginger, kencur, kunci, turmeric, galangal, temu ireng, and temulawak) from RGB images. The dataset is organised by class and processed with a pipeline that includes resizing to 224×224 pixels, image flipping and rotation, brightness adjustment, zoom, and normalisation, before being split into training, validation, and testing subsets with an 80:10:10 ratio. MobileNetV3-Large pretrained on ImageNet is used as a feature extractor with a three layer dense classification head and dropout regularisation, trained using the Adam optimiser with a learning rate of 0.0001 and a checkpoint mechanism to store the best validation model. The proposed model achieves 97.50% accuracy, 97.65% precision, 97.50% recall, and 97.51% f1-score on the test set, indicating stable and balanced performance across all rhizome classes despite their similarity. Compared with earlier rhizome classification approaches based on handcrafted features, which typically report lower accuracies on fewer classes, and with heavier VGG or ResNet backbones, this work provides, to the best of the authors’ knowledge, the first evaluation of MobileNetV3-Large for multi class rhizome classification and shows that it offers a practical and computationally efficient baseline for image based rhizome identification on resource constrained mobile or embedded devices.
Optimization of Tea Leaf Disease Detection Based on YOLOv8 Using CBAM and BFP Armijantoro, Gilang Rahmadhan; Nugroho, Budi; Via, Yisti Vita
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3394

Abstract

Early identification of tea leaf diseases is essential for sustaining crop productivity and preventing significant yield losses, making accurate automated detection a critical requirement in modern agricultural management. This study aims to improve the robustness of YOLOv8 for disease detection by integrating two complementary optimization modules chosen for their suitability in addressing common challenges in plant imagery: the Convolutional Block Attention Module (CBAM), which enhances discriminative feature focus under complex visual noise, and the Bidirectional Feature Pyramid Network (BiFPN), which strengthens multi-scale feature fusion to capture small or low-contrast lesions. The target diseases include Algal Leaf Spot, Brown Blight, and Grey Blight, using a combined dataset of primary field images and secondary data from Kaggle. Four models were developed—YOLOv8n (baseline), YOLOv8-CBAM, YOLOv8-BiFPN, and YOLOv8-CBAM-BiFPN. Experimental results demonstrate consistent performance gains across all enhanced variants. The baseline model obtained a precision of 0.760, recall of 0.735, and mAP50 of 0.793. Incorporating CBAM increased precision to 0.824 and recall to 0.780, while BiFPN yielded the highest recall of 0.820 with superior multi-scale generalization. The combined CBAM-BiFPN model achieved the strongest overall results, with a precision of 0.879, recall of 0.814, mAP50 of 0.886, and mAP50–90 of 0.739. These findings indicate that integrating CBAM and BiFPN substantially enhances YOLOv8’s capability in complex leaf-disease scenarios and offers practical potential for deployment in real agricultural settings to support faster decision-making and more effective disease management.
Co-Authors Affandi, Masfi Ulil Akbar, Fawwaz Ali Alfath Daryl Alhajir Andreas Nugroho Sihananto Anggraini Puspita Sari Armijantoro, Gilang Rahmadhan Budi Nugroho Dhian Satria Yudha Kartika Dita Atasa Diyasa, I Gede Susrama Mas Eka Prakarsa Mandyartha Eva Yulia Puspaningrum Fetty Tri Anggraeny Fitri, Anivea Fachmi Nur I Gede Susrama Mas Diyasa Intan Yuniar Purbasari, Intan Yuniar Isnaini, Frisda Dita Kartika, Dhian Satri Yudha Matondang, Natalia Maulana, Hendra Maulana, Raihan Thobie Nabil Miftahul Nuril Silviyah Muhammad Muharrom Al Haromainy Natalia Matondang Nurdiansyah N.A, M. Ryan Nurlaili, Afina Lina Nurrahman, Sintya Fadillah Pande Putu Sunaryang Yogam Prasanta Prayoga, Aji Putra Dwi Wira Gardha Yuniahans Retno Mumpuni Sadewa, Rahma Danu SALAMUN ROHMAN NUDIN Saputra, Dewa Raka Krisna Sari, Nabella Permata Shodiq, Mochammad Lukman Sufi Miftakhoneki SUGIARTO - Sujayanti, Forentina Kerti Pratiwi Syahrul Hidayat Tataq Distasianto Teguh Yulianto, Teguh Utami, Hapsari Wiji Wahyu Saifullah Jauharis Saputra Wahyu Syaifullah Jauharis Saputra Wijaya, Pandu Ali Yana, Baktiar Yudha Yudha K., Dhian Satria ZAMAZANI, ZAIN MUZADID