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All Journal Jurnal Pekommas Al Tijarah Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Surya Masyarakat JURIKOM (Jurnal Riset Komputer) Ensiklopedia of Journal JUPE : Jurnal Pendidikan Mandala Abdimas Universal Jurnal Ilmu Pendidikan Islam Jurnal Surya Masyarakat Journal of Intelligent Computing and Health Informatics (JICHI) INDRA: Jurnal Pengabdian kepada Masyarakat JURNAL MUARA PENDIDIKAN Jurnal Ilmu Pendidikan dan Sains Islam Interdisipliner Journal of Community Empowerment Jurnal Social Society
Ilham, Ahmad
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Religion Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics 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.
Evaluation of a Semantic Representation-Based Retrieval Model on a Text Dataset Generated from Image Transformation Firmansyah, Muhammad; Marutho, Dhendra; Ilham, Ahmad; Saputra, Irwansyah
Journal of Intelligent Computing & Health Informatics Vol 6, No 2 (2025): September
Publisher : Universitas Muhammadiyah Semarang Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jichi.v6i2.19240

Abstract

The increasing demand for efficient multimodal information retrieval has driven significant research into bridging visual and textual data. While sophisticated models like CLIP offer state-of-the-art semantic alignment, their substantial computational requirements present challenges for deployment in resource-constrained environments. This study introduces a lightweight retrieval framework that leverages the BLIP image captioning model to transform image data into rich textual descriptions, effectively reframing cross-modal retrieval as a text-to-text task. We systematically evaluated three retrieval models BM25, SBERT, and T5 on caption-transformed MSCOCO and Flickr30K datasets, utilizing both classical metrics (Recall@5, mAP) and semantic-aware metrics (SAR@5, Semantic mAP). Experimental results demonstrate that T5 achieves superior semantic performance (SAR@5 = 0.561, Semantic mAP = 0.524), surpassing SBERT (SAR@5 = 0.524) and outperforming the lexical BM25 baseline (SAR@5 = 0.312). Notably, the proposed BLIP+T5 pipeline attains 88% of CLIP’s semantic accuracy while reducing inference latency by approximately 60% and decreasing GPU memory consumption by over 60%. These findings underscore the potential of caption-based retrieval frameworks as scalable, cost-effective alternatives to computationally intensive multimodal systems, especially in latency-sensitive and resource-limited scenarios. Future work will explore fine-tuning strategies, domain-adapted semantic metrics, and robustness under real-world conditions to further advance retrieval effectiveness.
Co-Authors A. Octamaya Tenri Awaru Abdollahi, Jafar Abdul Nizar Adi Nugroho Adilla, Nia Adinullhaq, Juyus Muhammad Agatra, Denaya Ferrari Noval Ahmad Ahmad Farhan, Ahmad Ahyana, Afan Arga Aini, Isna Nur Akhmad Fathurohman Akhmad Fathurrohman Al Malik, M. Warisa Alfiana, Elsa Wahyu Amal Witonohadi Amylia, Aura Anam, A Khoirul Andi Aco Agus, Andi Aco Anggana, Muhammad Wahyu April Liana, Dhewi Apriliah, Mifta Apriyanto, Riki Ardhani, Yevi Alviatul Ariyanto, Nova Bahari Putra, Fajar Rahardika Bayu Kristianto Cornella, Barisma Ami Dewi Citrawati Dhendra Marutho Disma, Amanda Fatma Putri Dwi Setia Anugrah, Muhamad Fadli Emelia Sari Erwin Budianto Estuhono, Estuhono Fadilatul Fajriyah, Rizqi Febrianto Febrianto, Febrianto Firmasyah, Teguh Fitri Ayuning Tyas Habyba, Anik Nur Herlyana, Yuniar Iveline Anne Marie Kahar, Muhammad Syahrul Kahayani, Zahra Kamaruddin, Syamsu A Khatimah, Andi Weyana Nurul Khomsiana, Yeni Aqnes Khumairah, Tuffahati Sahna Khusna, Meisya Maulida Kindarto, Asdani Koli, Yulenni Bandora Kurnia, Janu Yogi Lorenza, Diana Lukman Assaffat Luqman Assaffat Mahaputra, Wahyu Maharani, Anisya Maulida, Nur Khilya Miftah Arifin Muhamad, Farezki Muhammad Firmansyah, Muhammad Muhammad Munsarif Muhammad Rizki Setyawan Muhammad Sam'an Muhammad Taufiqurrahman, Muhammad Munsarif, Muhammad Muza'in, Muhammad Muzayyanah, Ulfatul Elsa Nabila, Shadrina Putri Najamuddin Najamuddin, Najamuddin Natalia, Devitri Ni'am, Falahun Novia, Syakila Ana Sajidah Putri Noviandi Noviandi, Noviandi Nur, Muhammad Adiv Anas Nurmantoro, Irvan Parwadi Moengin Putri, Berliana Qori’nurrahman, Faqihana Ananda Ramadhani, Arfido Ramadhani, Rima Dias Ramea Astri, Tita Riski Amaliah, Riski Rizki Jayanti, Dian Safuan Safuan Sam’an, Muhammad Sangadji, Zulkarnain Saputra, Irwansyah Saputra, Tegar Sasmita, Nanda Yulia Setia Iriyanto Setianama, Mamur Setyaningsih, Ayu Sholakhudin, Akhmad Sundari Sundari Suryana, Yunita Friscilia Suseno, Dimas Adi Sutarno Sutarno Syafitiri, Urzha Dian Syaifani, M. Amin Trianita, Nisa Adelia Ulfa, Helya Cholifatul Ulinuha, Mohammad Wulan Cahya Ningrum