Miftahuddin, Yusup and SHIHAB, IBNU FARHAN (2023) Perbandingan Metode LVQ Dan SVM Dalam Klasifikasi Produk Makanan Untuk Pengidap Penyakit Stroke Non-Hemoragik. In: Prosiding Diseminasi Fakultas Teknik Industri Semester Genap 2023/2024, Bandung.

Text (Perbandingan Metode LVQ Dan SVM Dalam Klasifikasi Produk Makanan Untuk Pengidap Penyakit Stroke Non-Hemoragik) Perbandingan Metode LVQ Dan SVM Dalam Klasifikasi Produk Makanan Untuk Pengidap Penyakit Stroke Non-Hemoragik.pdf Download (874kB)

Abstract

One of the risks of non-hemorrhagic stroke is caused by excess body weight or obesity, so an effort to overcome this problem requires a system that can determine the classification of food products that are allowed for people with non-hemorrhagic stroke. This research was conducted using the LVQ and SVM methods by measuring the level of accuracy, precision, recall and error goal method in the classification of food products. Implementation of the LVQ classification model with a learning rate of 0.1 and epoch 100 parameters resulted in an accuracy value of 0.7797, a precision of 0.7568, a recall of 0.8750, and an error goal of 0.2203. While the SVM classification model with the hyperparameter model (default) produces an accuracy value of 0.9153, a precision of 0.9375, a recall of 0.9091, and an error goal of 0.0847. In the experimental stage, tests were carried out on the LVQ and SVM models, where LVQ produced optimal parameter pairs at a learning rate of 0.001 and epoch 10 with an accuracy value of 0.9068. Whereas SVM with a polynomial kernel model produces optimal parameters at a cost of 100 with an accuracy value of 0.9661. Based on the tests conducted, the SVM model is a better classification method than LVQ. Keywords: learning vector quantization, support vector machine, stroke

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