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All Journal Sebatik J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Informasi Interaktif Jurnal Tekinkom (Teknik Informasi dan Komputer) Community Development Journal: Jurnal Pengabdian Masyarakat Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat Journal of Information Technology (JIfoTech) J-SAKTI (Jurnal Sains Komputer dan Informatika) Edukasia: Jurnal Pendidikan dan Pembelajaran Epmas: Edukasi dan Pengabdian Masyarakat
Listra Firgia
Institut Shanti Bhuana
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Public Health Other

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Journal : Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika)

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Deteksi Jenis Penyakit Dan Hama Pada Tanaman Jagung Menggunakan Arsitektur Spatial Pyramid Pooling Pada YOLOv5s M Mira; Listra Firgia; Shanti Thomas
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 8, No 2 (2023): Edisi Agustus
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v8i2.630

Abstract

Corn is one of the important crops in the agricultural sector in the global and national economy because it is a food resource as food, animal feed and other raw materials for the community. Based on satudata.pertanian.go.id, the projected corn production in 2020-2024 will still increase between 0.94% and 0.97% per year. In this study, detection of diseases and pests in maize was carried out using YOLO technology with spatial pyramid pooling (SPP) architecture as a form of intelligent innovation in maize farming. The research data consisted of 309 image data with class values as labels representing types of disease and types of pests in corn plants consisting of Locusta (Locust), Sitophilus (Powder Flower), Spodoptera (Arrayworm), Mysus Persicase (Aphids), and Bulai . The indicators for testing and evaluating the model use precision, recall, f1 score, mAP0.5 and Map0.5:0.95 as evaluation metrics. Based on the results of training and evaluation of the model, it is known that the precision value with batch size 32 epoch 64 produces a precision value of 0.65, recall, 0.76, f1 score 0.65 Map0.5 0.704 and Map-.5:0.95 0.298. Whereas with a batch size of 64 epoch 100 the precision value is 0.73, the recall is 0.77 f1 score is 0.73 Map0.5 0.795 and Map0.5:0.95 0.346. Model predictions using YOLO technology with spatial pyramid pooling architecture in detecting types of diseases and pests in corn plants contribute to smart agriculture. With accurate information about the types of diseases and pests that attack corn plants, farmers can respond quickly and take appropriate actions, such as using specific pesticides or suitable organic control methods.
Co-Authors Ag. Rudatyo Himamunanto Agustinus Rudatyo Himamunanto Azril Christian Nurcahyo Candra Gudiato Fra Siskus Dian Arianto Hammad, Rifqi M Mira Manggu, Blasius Margaretha Lidya Sumarni Maya Sari Mira Mira Noviyanti P Noviyanti. P Noviyanti. P Nurcahyo, Azriel Christian Sabinus Beni Shanti Thomas Siprianus Jewarut Soedjatmiko Veneranda Rini Hapsari Vuspitasari, Benedhikta Kikky Winda Lidya Lumbantobing Y Yuliana Yulianto Mustaqim