Implementasi Convolutional Neural Network Untuk Deteksi Dan Klasifikasi Penyakit Tanaman Hias Berdasarkan Citra Daun
DOI:
https://doi.org/10.33367/ijhass.v5i4.6397Keywords:
Convolutional Neural Network, CNN, Deteksi dini, penyakit tanaman hias, kecerdasan buatan, citra daun, teknologi pertanianAbstract
The manual detection of diseases in ornamental plants is often slow, inaccurate, and prone to errors. This situation negatively impacts the quality and market value of ornamental plants in Indonesia, which possess high aesthetic and economic value. This study aims to develop a Convolutional Neural Network (CNN) model based on leaf images to detect and classify ornamental plant diseases quickly and accurately. The methodology involves collecting a dataset of leaf images categorized into five classes: bacterial, fungal, viral, pest-induced, and healthy. The CNN model was trained using the TensorFlow framework and integrated into a web-based application utilizing Laravel and FastAPI to enhance user accessibility. The results indicate that the developed CNN model achieved an accuracy of 89.67%. The implemented application is capable of early detection of ornamental plant diseases with high speed and accuracy, equipped with detection history and treatment recommendation features. This solution demonstrates a significant contribution to the application of artificial intelligence technology in agriculture, particularly in supporting the sustainable health of ornamental plants in Indonesia.
References
Afandi, Hanip, and Danang Arbian Sulistyo. “Sistem Pakar Untuk Diagnosa Hama Dan Penyakit Pada Bunga Krisan Menggunakan Forward Chaining | Jurnal Ilmiah Teknologi Informasi Asia,” July 11, 2021. https://jurnal.stmikasia.ac.id/index.php/jitika/article/view/409.
Albakia, Sri Adiningsi Erni, and Rizal Adi Saputra. “Identifikasi Jenis Daun Tanaman Obat Menggunakan Metode Convolutional Neural Network (CNN) Dengan Model VGG16.” Jurnal Informatika Polinema 9, no. 4 (August 22, 2023): 451–60. https://doi.org/10.33795/jip.v9i4.1420.
Amalia, Mia Martha, Ernawati Ernawati, and Andang Wijanarko. “Implementasi Metode Naïve Bayes Dalam Sistem Pakar Diagnosis Hama dan Penyakit Pada Tanaman Hias Aglaonema SP.” Rekursif: Jurnal Informatika 10, no. 1 (April 24, 2022): 23–39. https://doi.org/10.33369/rekursif.v10i1.18953.
Anuar, Asyraf Wahi, Nazri Kama, Azri Azmi, Hazlifah Mohd Rusli, and Yazriwati Yahya. “Re-CRUD Code Automation Framework Evaluation Using DESMET Feature Analysis.” International Journal of Advanced Computer Science and Applications (IJACSA) 13, no. 5 (40/31 2022). https://doi.org/10.14569/IJACSA.2022.0130552.
Apriyani, Hilda, and Kurniati Kurniati. “Perbandingan Metode Naïve Bayes Dan Support Vector Machine Dalam Klasifikasi Penyakit Diabetes Melitus.” Journal of Information Technology Ampera 1, no. 3 (December 20, 2020): 133–43. https://doi.org/10.51519/journalita.volume1.isssue3.year2020.page133-143.
Aqil, Aji Naufal, Burhanuddin Dirgantara, Istikmal, Umar Ali Ahmad, Reza Rendian Septiawan, and Alex Lukmanto Suherman. “Robot Chat System (Chatbot) to Help Users ‘Homelab’ Based in Deep Learning.” International Journal of Advanced Computer Science and Applications (IJACSA) 12, no. 8 (31 2021). https://doi.org/10.14569/IJACSA.2021.0120870.
Azizah, Qudsiah Nur. “Klasifikasi Penyakit Daun Jagung Menggunakan Metode Convolutional Neural Network AlexNet.” sudo Jurnal Teknik Informatika 2, no. 1 (February 17, 2023): 28–33. https://doi.org/10.56211/sudo.v2i1.227.
Bansal, Priya, and Abdelkader Ouda. “Study on Integration of FastAPI and Machine Learning for Continuous Authentication of Behavioral Biometrics.” In 2022 International Symposium on Networks, Computers and Communications (ISNCC), 1–6, 2022. https://doi.org/10.1109/ISNCC55209.2022.9851790.
Biehl, Matthias. RESTful API Design. API-University Press, 2016.
Claudia, Cyntia, and Joseph Edwin. “Motivasi Membagikan Konten Tanaman Hias Di Social Networking Sites.” WACANA: Jurnal Ilmiah Ilmu Komunikasi 21, no. 2 (December 29, 2022): 178–95. https://doi.org/10.32509/wacana.v21i2.2000.
Ehsan, Adeel, Mohammed Ahmad M. E. Abuhaliqa, Cagatay Catal, and Deepti Mishra. “RESTful API Testing Methodologies: Rationale, Challenges, and Solution Directions.” Applied Sciences 12, no. 9 (January 2022): 4369. https://doi.org/10.3390/app12094369.
Engel, Ventje Jeremias Lewi, and Sinung Suakanto. “Model Inferensi Konteks Internet of Things pada Sistem Pertanian Cerdas.” Jurnal Telematika 11, no. 2 (2016): 6–6. https://doi.org/10.61769/telematika.v11i2.140.
Husodo, Kelvianto, Charisini Lubis, and Zyad Rusdi. “KLASIFIKASI TANAMAN ANGGREK MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK DENGAN ARSITEKTUR VGG-19.” Simtek : jurnal sistem informasi dan teknik komputer 8, no. 2 (October 2, 2023): 253–58. https://doi.org/10.51876/simtek.v8i2.214.
Idrus, Syaikhul Anam Al, Aziz Musthafa, and Oddy Virgantara Putra. “Deteksi Penyakit Pada Daun Tanaman Padi Menggunakan Metode Convolutional Neural Network.” Prosiding Seminar Nasional Mahasiswa Bidang Ilmu Komputer dan Aplikasinya 2, no. 2 (2021): 103–9.
Indrawan, Adysta Marsha, Mochammad Firman Arif, and Rudi Hariyanto. “Deteksi Jenis Penyakit Tanaman Hias Aglaonema Menggunakan Metode Convolutional Neural Network Pada ‘As Florist.’” Jurasik (Jurnal Riset Sistem Informasi Dan Teknik Informatika) 9, no. 2 (August 30, 2024): 1013–20. https://doi.org/10.30645/jurasik.v9i2.832.
Jinan, Abwabul, and B. Herawan Hayadi. “Klasifikasi Penyakit Tanaman Padi Mengunakan Metode Convolutional Neural Network Melalui Citra Daun (Multilayer Perceptron).” Journal of Computer and Engineering Science, August 3, 2022, 37–44.
Jocher, Glenn, Alex Stoken, Ayush Chaurasia, Jirka Borovec, NanoCode012, TaoXie, Yonghye Kwon, et al. “Ultralytics/Yolov5: V6.0 - YOLOv5n ‘Nano’ Models, Roboflow Integration, TensorFlow Export, OpenCV DNN Support.” Zenodo. Accessed December 22, 2024. https://doi.org/10.5281/zenodo.5563715.
Lawi, Armin, Naili Suri Intizhami, Rio Mukhtarom, and Supri Amir. “KLASIFIKASI PENYAKIT CITRA DAUN TANAMAN TOMAT DENGAN ENSEMBLE CONVOLUTIONAL NEURAL NETWORK.” Seminar Nasional Teknik Elektro Dan Informatika (SNTEI) 8, no. 1 (2022): 239–43.
Mahacakri, I. Gusti Ayu Cynthia. “Manajemen Usaha Dan Penerapan Digital Marketing Tanaman Hias Di Kota Mataram.” AGROTEKSOS 30, no. 1 (April 28, 2020): 1–10. https://doi.org/10.29303/agroteksos.v30i1.549.
Muliany, P. Hanny, ed. Statistik pertanian, 2016 = Agricultural statistics, 2016. Ragunan, Jakarta Selatan: Pusat Data dan Sistem Informasi Pertanian, Kementerian Pertanian, 2016.
Pamungkas, Nicholas Bagus, and Agus Suhendar. “Penerapan Metode Convolutional Neural Network Pada Sistem Klasifikasi Penyakit Tanaman Apel Berdasarkan Citra Daun.” Edumatic: Jurnal Pendidikan Informatika 8, no. 2 (December 19, 2024): 675–84. https://doi.org/10.29408/edumatic.v8i2.27958.
Peralta, Jose Haro. Microservice APIs: Using Python, Flask, FastAPI, OpenAPI and More. Simon and Schuster, 2023.
Pitaloka, Dyah. “Hortikultura: Potensi, Pengembangan Dan Tantangan.” G-Tech: Jurnal Teknologi Terapan 1, no. 1 (2017): 1–4. https://doi.org/10.33379/gtech.v1i1.260.
Prasetyo, Yudho Yudhanto dan Helmi Adi. Mudah Menguasai Framework Laravel. Elex Media Komputindo, 2019.
Rahmatulloh, A., R. Gunawan, and F. M. S. Nursuwars. “Performance Comparison of Signed Algorithms on JSON Web Token.” IOP Conference Series: Materials Science and Engineering 550, no. 1 (July 2019): 012023. https://doi.org/10.1088/1757-899X/550/1/012023.
Roslita, Dina. “Analisa Dan Implementasi Sistem Informasi Manajemen Kelurahan Berbasis Web Model Goverment to Citizen.” Jurnal Ilmiah Mahasiswa Sistem Informasi Dan Komputer Akuntansi 1, no. 1 (May 3, 2023): 7–12. https://doi.org/10.33365/jimasika.v1i1.2498.
Rosyidah, Putri, and Siswahyudianto. “Strategi Marketing Mix Pada Bisnis Tanaman Hias Untuk Mencapai Keunggulan Bersaing Ditinjau Dari Etika Bisnis Islam.” Idarotuna : Journal of Administrative Science 4, no. 1 (May 10, 2023): 1–11. https://doi.org/10.54471/idarotuna.v4i1.39.
Rozaqi, Abdul Jalil, Andi Sunyoto, and M Rudyanto Arief. “Deteksi Penyakit Pada Daun Kentang Menggunakan Pengolahan Citra dengan Metode Convolutional Neural Network.” Creative Information Technology Journal 8, no. 1 (March 31, 2021): 22. https://doi.org/10.24076/citec.2021v8i1.263.
Santosa, Atharizky Ade, R. Yunendah Nur Fu’adah, and Syamsul Rizal. “Deteksi Penyakit Pada Tanaman Padi Menggunakan Pengolahan Citra Digital Dengan Metode Convolutional Neural Network.” JOURNAL OF ELECTRICAL AND SYSTEM CONTROL ENGINEERING 6, no. 2 (February 27, 2023): 98–108. https://doi.org/10.31289/jesce.v6i2.7930.
Sastrahidayat, Ika Rochdjatun. Penyakit pada Tanaman Hias. Universitas Brawijaya Press, 2015.
Sheila, Syenira, Irma Permata Sari, Adrie Bagas Saputra, Muhammad Kharil Anwar, and Farid Restu Pujianto. “Deteksi Penyakit Pada Daun Padi Berbasis Pengolahan Citra Menggunakan Metode Convolutional Neural Network (CNN).” MULTINETICS 9, no. 1 (April 13, 2023): 27–34. https://doi.org/10.32722/multinetics.v9i1.5255.
Soedarmanto, Heri, Rabiatul Adawiyah, Nur Diana, Evy Setiawati, Yuniati, and Fauzia Ayu Nurhany. Bunga rampai hasil penelitian terapan : potensi pengembangan komoditas bunga melati, kenanga dan mawar untuk minyak atsiri dan diversifikasi produknya di Kalimantan Selatan. Poliban Press, 2022.
Sulistiyana, Fiviana, and Sri Anardani. “Aplikasi Deteksi Penyakit Tanaman Jagung Dengan Metode CNN dan SVM.” Prosiding Seminar Nasional Teknologi Informasi dan Komunikasi (SENATIK) 6, no. 1 (August 8, 2024): 423–32.
Talele, Ajay, Aseem Patil, and Bhushan Barse. “Detection of Real Time Objects Using TensorFlow and OpenCV.” Asian Journal For Convergence In Technology (AJCT) ISSN -2350-1146, April 12, 2019. http://www.asianssr.org/index.php/ajct/article/view/783.
Tintin Febrianti, S. P., Ai Yanti Rismayanti, MP SP, S. P. Ardli Swardana, S. P. Asti Asfianti, Ati Atul Quddus, S. Pt, S. T. P. Atia Fizriani, M. P. Ir Dadi Nurdiana, and Ervi Herawati. Goresan Pena 24 Dosen Pertanian. Deepublish, 2023.
Abstract
Views:
455, 
PDF downloads: 319
