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All Journal Jurnal Buana Informatika Jurnal Teknologi dan Sistem Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Penelitian Sains Jurnal TAM (Technology Acceptance Model) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI JurTI (JURNAL TEKNOLOGI INFORMASI) ABSYARA: Jurnal Pengabdian Pada Masyarakat IJAIRTec Algoritme Jurnal Mahasiswa Teknik Informatika Informatics and Enginering Dedication Klik - Jurnal Ilmu Komputer MDP Student Conference Jurnal Ilmiah Sistem Informasi INOVTEK Polbeng - Seri Informatika International Journal of Artificial Intelligence and Robotic Technology (IJAIRTec) Jurnal TAM (Technology Acceptance Model)
Derry Alamsyah
Universitas Multi Data Palembang
Agriculture, Biological Sciences & Forestry Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Library & Information Science Mathematics Physics Other

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Banana Leaf Disease Identification Using SqueezeNet Architecture with Convolutional Block Attention Module Daniel Wijaya; Derry Alamsyah
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/ktx6vp08

Abstract

Banana leaf diseases significantly reduce crop productivity and quality, while conventional visual inspection methods are often subjective, time-consuming, and inefficient for large-scale plantations. This study proposes an automated banana leaf disease identification approach using a lightweight Convolutional Neural Network (CNN) based on the SqueezeNet architecture integrated with the Convolutional Block Attention Module (CBAM). The dataset consists of four classes Cordana, Healthy, Pestalotiopsis, and Sigatoka with image augmentation applied to increase data variability. Several experimental scenarios were conducted to evaluate the impact of data augmentation and CBAM integration on model performance. The models were evaluated using accuracy, precision, recall, and F1-score metrics. Experimental results show that SqueezeNet combined with CBAM achieved superior performance compared to the baseline SqueezeNet model, particularly in non-augmented conditions, with an accuracy of 93.75% while maintaining a relatively small number of parameters. Although data augmentation alone led to performance degradation, the inclusion of CBAM mitigated this effect by enhancing spatial and channel-wise feature representation. These findings indicate that the proposed SqueezeNet–CBAM model offers an effective and computationally efficient solution for banana leaf disease identification, with strong potential for real-world agricultural applications.
Co-Authors Alwanda, Muhammad Rafly Andreyas andreyas Callista Virginia Daniel Wijaya Daniel Wijaya Devella, Siska Dicky Pratama Farisi, Ahmad Ferdyansyah Wijaya gasim, Gasim Hafiz Irsyad Hartati, Ery Hocwin Hebert Hocwin Hebert Idham Cholid Ignatius Rivaldo Lie Inayatullah Inayatullah Irmeilyana Karolina, umi Khan, Zicola Vladimir VIky Leonardo Leonardo Maryati Gultom Muhammad Bemby Putra Mansyah Mukhlisa Mukhlisa Nataliatara Nataliatara Novan Wijaya Paniza, Mehri Pranata, Steven Putra BJ Bangun Putra, Ivan Pratama Ramadhan, Raden Putra Kurniawan RENDY PRATAMA Reynard Saputra Ricky Julianto rusbandi rusbandi, rusbandi Sanders, Carmel Edra Tinaliah, Tinaliah Tommy Oktavianus Saputra Triana Elizabeth, Triana Valentino Ruben Ho Virginia, Callista Wijang Widhiarso Willyanto, Aldo Yoannita Yoannita, Yoannita