Klasifikasi Menggunakan Metode Support Vector Machine Untuk Mendeteksi Penyakit Tanaman Bawang Merah
Year: 2024
Type: article
Abstract: Shallots are a spice that is always present in various dishes in Indonesia, and red onions also contain many diseases that attack the plant. In this research, digital image processing is used to classify shallot diseases. The aim is to make it easier to recognize, identify or analyze the object. The research stage begins with collecting data on shallot plants, then carrying out various scenarios, then in this research we classify shallot objects using the SVM algorithm. The data we use is 250 image data, which are classified as normal shallot diseases, bottom rot, caterpillars, and leaf mold. Testing is based on image size and the amount of training and testing data. The test results show that the SVM algorithm runs well and produces the highest performance, accuracy of 79%, precision of 79% and F1-score of 79%.
Source: Nusantara of Engineering (NOE)
Institution: Universitas Nusantara PGRI Kediri
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: hybrid
OpenAlex ID: W4410608380