Solusi Prediksi Persediaan Barang dengan Menggunakan Algoritma Apriori (Studi Kasus: Regional Part Depo Auto 2000 Palembang)

Usman Ependi, Ade Putra

Abstract


Dalam memprediksi persediaan barang banyak metode yang dapat dilakukan antara lain yaitu dengan melakukan pengolahan data penjualan menggunakan metode Data Mining yang disertai dengan algoritma apriori didasarkan pada proses pembelian yang dilakukan oleh konsumen berdasarkan keterkaitan antar produk yang dibeli. Dengan menggunakan algoritma apriori pihak perusahaan dalam hal ini adalah Regional Part Depo Auto 2000 Palembang dapat menyediakan spare part yang dibutuhkan oleh konsumen khususnya dilingkungan Sumatera Selatan tanpa harus melakukan proses indent hal ini dikarenakan banyaknya jumlah spare part yang harus di sediakan oleh PT. Depo Toyota guna melayani kebutuhan konsumen di lingkungan Sumatera Selatan. Adapun tahapan data mining yang di gunakan yaitu Knowledge Discovery in Database (KDD) yang terdiri dari proses data cleaning and integration, data selection and integration, data mining, evaluation and prentation. Dari proses diatas didapat pola keterkaitan spare part sebanyak 646 dari jumlah spare part sebanyak 338.


Keywords


Data Mining; Apriori; Spare Part; KDD

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References


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DOI: http://dx.doi.org/10.26418/jp.v5i2.32648

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