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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Elkom: Jurnal Elektronika dan Komputer Bulletin of Electrical Engineering and Informatics International Journal of Advances in Intelligent Informatics Martabe : Jurnal Pengabdian Kepada Masyarakat Jurnal Surya Masyarakat Indonesian Journal of Electrical Engineering and Computer Science Computer Science and Information Technologies Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Jurnal Surya Masyarakat Journal of Intelligent Computing and Health Informatics (JICHI) Aspirasi : Publikasi Hasil Pengabdian dan Kegiatan Masyarakat Jurnal Komputer dan Teknologi Informasi
Muhammad Munsarif
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Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Medicine & Pharmacology Public Health Social Sciences Other

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Journal : Journal of Intelligent Computing and Health Informatics (JICHI)

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Enhancing Agricultural Pest Detection with EfficientNetV2-L and Grad-CAM: A Comprehensive Approach to Sustainable Farming Agatra, Denaya Ferrari Noval; Cornella, Barisma Ami; Muza'in, Muhammad; Munsarif, Muhammad; Abdollahi, Jafar; Ilham, Ahmad
Journal of Intelligent Computing & Health Informatics Vol 5, No 1 (2024): March
Publisher : Universitas Muhammadiyah Semarang Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jichi.v5i1.13959

Abstract

In modern agriculture, quickly identifying agricultural pests is essential for maintaining high crop yields and ensuring global food security. In diverse and dynamic agricultural environments, traditional pest detection methods exhibit reduced accuracy, limited scalability, and lack interpretability. In this study, EfficientNetV2-L and Grad-CAM were used to significantly enhance pest detection system performance and transparency. EfficientNetV2-L, a fast and resource-efficient model, excels particularly in computationally constrained environments. Traditional CNN models, including EfficientNetV2-L, are criticized as uninterpretable "black boxes" despite their high accuracy. To address this issue, Grad-CAM was used to generate salient maps that visually show the most influential areas of the input image in the model’s decision-making process. This combination not only provides superior pest detection accuracy but also provides actionable insights into the model’s predictions, which is an important feature for building trust among agricultural practitioners. Our experimental results show a 15% improvement in detection accuracy compared to conventional models, especially in identifying visually similar-looking pest species that are often misclassified. In addition, the enhanced interpretability provided by Grad-CAM has led to a deeper understanding of the model’s behaviour, enabling iterative adjustments and improvements that further enhance the reliability of the system. The practical implications of these findings are significant: this integrated model offers a robust solution that can be seamlessly applied to real-time agricultural monitoring systems. With the early detection and proper classification of pests, this model can be used as a more effective pest management strategy to minimize crop damage and increase agricultural productivity. This research not only advances the technological frontier of pest detection but also contributes to broader goals related to sustainable agriculture and food security. Future research will focus on expanding the applicability of this model across different agricultural contexts, improving its adaptability to different environmental conditions, and further optimizing its performance through advanced techniques such as transfer learning and ensemble methods.
EfficientNet for Medical Image Classification: Performance vs. Efficiency in Skin Cancer Detection Purbandanu, Muhammad Wigig; Kurniawan, Arif; Yanuarta, Rizky; Munsarif, Muhammad; A. Awoseyi, Ayomikun
Journal of Intelligent Computing & Health Informatics Vol 5, No 2 (2024): September
Publisher : Universitas Muhammadiyah Semarang Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jichi.v5i2.14338

Abstract

This study applies EfficientNetB2, a computationally efficient convolutional neural network (CNN), to improve the accuracy of skin cancer detection using the heterogeneous HAM10000 dataset. Skin cancer classification poses challenges, including overfitting and class imbalance, which we address through data augmentation, class weighting, and SMOTE (Synthetic Minority Over-sampling Technique). Our model achieved accuracy of 86%, precision of 0.87, recall of 0.85, and an AUC of 0.90. These results outperform comparable architectures, such as ResNet50 and GoogleNet, while maintaining lower computational complexity. The proposed model demonstrates high precision in detecting actinic keratoses and basal cell carcinoma, which require timely treatment, but faces difficulties in differentiating melanoma from benign nevi because of their similar visual appearance. This study highlights the potential of EfficientNetB2 for real-world deployment in resource-limited settings, such as mobile health applications and telemedicine platforms. Future research will focus on integrating attention mechanisms and exploring cross-dataset validation to enhance model generalizability and performance.
Co-Authors A. Awoseyi, Ayomikun Abdollahi, Jafar Adinullhaq, Juyus Muhammad Agatra, Denaya Ferrari Noval Ahmad Ilham Ahmad Miftakhul Akhmad Fathurrohman Akhmad Fathurrohman Alfi Widianto Alif Hardika Ardhani, Yevi Alviatul Arfido Ramadhani ARIF KURNIAWAN Ariyanto, Nova Asdani Kindarto Caesarizky, Ovien Yoga Cornella, Barisma Ami Edi Noersasongko Edi Noersasongko Eny Winaryati Fahrezi, Andrian Febrianto Febrianto Febrianto Febrianto, Febrianto Galih Ratna Hakim, Muhammad Ilham Ilham, Ahmad Irvan Nurmantoro Javier Rayhan Jian Janata Juyus Muhammad Adinullhaq Kholifatul Afwah Kindarto, Asdani Luqman Assaffat Luqman Assaffat Mardiana Mardiana Meike Amelia Hanifah Moch Arief Soeleman Mochammad Arief Soeleman Muhammad Sam'an Muhammad Saman Muhammad Sam’an Muza'in, Muhammad Nova Ariyanto Nurmantoro, Irvan Pulung Nurtantio Andono Purbandanu, Muhammad Wigig Ramadhani, Arfido Safuan Safuan Safuan Safuan Safuan Safuan Safuan Safuan Salsabila Rahma Sam'an, Muhammad SAMSUDI RAHARJO Samsudi Raharjo Sefrina Ainun Shabrina Mirza Siti Muzdalifah Suwahono Suwahono Tirta Ramadhani Wibowo, Bhanu Lintang Yanuarta, Rizky Yevi Alviatul Ardhani