Data Mining untuk Meningkatkan Efisiensi dan Prediksi Produk Garmen Menggunakan Algoritma K-Nearest Neighbor di PT Mas Silueta

Taufik Hidayat

doi:10.53863/kst.v6i01.1085

Authors

Taufik Hidayat Universitas Selamat Sri, Kendal, Indonesia

DOI:

https://doi.org/10.53863/kst.v6i01.1085

Keywords:

Optimization, Garment, K-Nearest Neighbor

Abstract

Clothing is a basic human need, especially for women. MAS SILUETA PT is located at Wijaya III Monument Street, Randu Garut, District. Tugu, Semarang City and produces women's clothing and underwear such as Bra Cups, supportive trousers and shirts, with so many variations of this clothing, you can predict which ones are popular or not so that the production process is more productive. Based on the number of products sold, they will be grouped into two, namely achieving or products that are in demand and not achieving or less popular. So to find out what garment production has been achieved, a data calculation process is needed to solve the problems found using data mining. K-Nearest Neighbor (KNN) is a process in data mining for predictions so that the data obtained is accurate and efficient. And with the K-Nearest Neighbor method, you can carry out a data grouping process and then analyze the data group and produce predictions that can be used to analyze products based on data from a lot of underwear sales (big data) so that you can get useful new information. This research discusses garment production using 84 test data. And produces an accuracy of 87.50%.

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