Prediksi Jumlah Produksi Perakitan Komponen Menggunakan ANFIS Yang Dioptimasi Dengan Algoritma K-Means

Ari Sujiana ari; Utomo Budiyanto

doi:10.31154/cogito.v9i2.513.252-265

Authors

Ari Sujiana ari Universitas Budi Luhur
Utomo Budiyanto Universitas Budi luhur

DOI:

https://doi.org/10.31154/cogito.v9i2.513.252-265

Keywords:

ANFIS, K-means, Fuzzy, Production, Prediction

Abstract

In the assembly industry, the number of products produced is very important to meet customer demand, so an appropriate production plan needs to be made. The many influencing factors become obstacles to estimating production results. One prediction method that is widely used in conditions with many influencing factors is the Adaptive Neuro-Fuzzy Inference System (ANFIS). However, the weakness of ANFIS when used on databases with sparse density is that it is difficult to establish fuzzy basic rules. To overcome this, optimization was carried out in this research by grouping the range of membership degree label values in the input and output variables using the K-means algorithm approach before the dataset was input to the network. Based on the Average Forecasting Error Rate (AFER) method, the prediction results of the ANFIS method with K-means optimization have an error percentage of 0.000018%, while the ANFIS prediction results with determining the degree of membership on fuzzy input and output labels are carried out arbitrarily, producing an error percentage of 0.000023%. The results concluded that the use of the K-means algorithm for sparse database grouping to determine the degree of ANFIS membership can be applied and produces a lower error rate than random grouping.

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