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Arif Indra Kurnia
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Klasifikasi Kualitas Susu Sapi Menggunakan Algoritme Support Vector Machine (SVM) (Studi Kasus: Perbandingan Fungsi Kernel Linier dan RBF Gaussian) Arif Indra Kurnia; Muhammad Tanzil Furqon; Bayu Rahayudi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 11 (2018): November 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Cow milk has a lot of animal protein and have benefit for children and whoever in process for grow up. Cow milk contains good essential amino acids. Malang Animal Health Laboratory as the unit executor in east java Animal Husbandry Department do a test in kesmavet for efforts to secure milk as a farm product with appropriate testing in suitable with the Indonesian National Standard (SNI). The classification of cow milk quality is still using organoleptic (smell, taste, color) that are linguistic, so that variable and parameter are uncertain and become themain obstacle of expert in determining good milk quality. To resolve this issue, this can be done with schizophrenia classification using support vector machine (SVM) algorithm, which SVM performace is more suitable than other classification methods. In this study there are 269 data that is divided into two data that is data training and data testing with three classification result, that is low, medium, and hight. The result in this paper get the best acuracy based K-Fold Cross Validation as much 10 fold, with Kernel RBF and Kernel Linear with value λ (lambda) = 0,0001, C (complexity) = 1, γ (gamma) =0,0001, maximum iteration = 30 and σ kernel RBF= 10. The highest accuracy using SVM method in cow milk quality classification use Kernel RBF was 96% and the highest accuracy use Kernel Linear was 62%.
Co-Authors Bayu Rahayudi Muhammad Tanzil Furqon