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Abas Saritua Gultom
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Klasifikasi Masa Panen Varietas Unggul Kedelai menggunakan Support Vector Machine (SVM) Abas Saritua Gultom; Muhammad Tanzil Furqon; Sutrisno Sutrisno
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 12 (2021): Desember 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Soybean is an agricultural commodity that is very much needed in Indonesian, because soybeans are widely consumed in various food products, soybeans are also used as industrial raw materials. Soybean farmers need to know what type of soybean plant is included in the seeds to be planted, so that the increase in soybean production is maintained. To facilitate the process, data from various types of soybeans will be used. The research will be conducted using the SVM (Support Vector Machine) method because the SVM method can generalize high without having to have additional datasets. In this study, there were 6 variables and objects belonging to 3 classes, namely early age, medium age, and deep age. The best test results use a polynomial degree 2 kernel, using the lamda (λ) value of 10, Constant 1, Epsilon 0.01 and iter max of 10. Based on various tests and scenarios that have been carried out, the best evaluation value is generated in tests using K-Fold Cross Validation with a value of K = 5 and produces an accuracy value of 56.666%.
Co-Authors Muhammad Tanzil Furqon Sutrisno Sutrisno