Analisis perbandingan Algoritma Apriori dan FP-Growth pada transaksi penjualan sparepart mobil

Putra, Ramadhan Reggya (2020) Analisis perbandingan Algoritma Apriori dan FP-Growth pada transaksi penjualan sparepart mobil. Diploma thesis, UIN Sunan Gunung Djati Bandung.

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Abstract

Suku cadang atau yang disebut sparepart biasanya tidak selalu tersedia secara siap ada dipasaran melainkan sangat terbatas keberadaanya. Suku cadang ini merupakan alat penunjang mesin-mesin yang di gunakan untuk memproduksi suatu produk sehingga suku cadang mempunyai peranan yang sangat vital bagi kelangsungan proses produksi disetiap perusahaan manufaktur. Dalam penelitian ini akan mencoba mengimplementasikan algoritma Apriori dan FP-Growth ke dalam sebuah sistem penjualan yang dapat pola pembelian produk sparepart mobil chevrolet yang terjual bersama dan kecepatan proses kinerja algoritma Apriori dan FP-Growth untuk mengetahui pola pembelian produk sparepart mobil yang terjual bersama. Metode yang digunakan untuk mengetahui pola pembelian produk sparepart mobil yaitu menggunakan algoritma Apriori dan FP-Growth. Hasil yang didapat dalam pola pembelian produk sparepart mobil, algoritma Apriori mendapatkan hasil 2 rule jika service kategori angine maka akan service kategori suspensi dengan kemungkinan 54% dan jika service kategori angine maka akan service kategori bodi dengan kemungkinan 59% dan algoritma FP-Growth mendapatkan hasil 2 rule jika service kategori angine maka akan service kategori suspensi dengan kemungkinan 54% dan jika service kategori angine maka akan service kategori bodi dengan kemungkinan 59%, dan dalam perbandingan waktu pemrosesan, algoritma Apriori mendapatkan hasil 10 detik sedangkan algoritma FP-Growth mendapatkan hasil 0 detik. Dari hasil tersebut dapat disimpulkan bahwa algoritma Apriori lebih baik dalam segi waktu pemrosesannya. Spare parts or what are called spare parts are usually not always readily available in the market but are very limited in their existence. These spare parts are a means of supporting machines that are used to produce a product so that they have a very vital role in the continuity of the production process in every manufacturing company. In this study, we will try to implement the Apriori and FP-Growth algorithms into a sales system that can purchase patterns for chevrolet car spare parts that are sold together and the speed of the performance process of the Apriori and FP-Growth algorithms to determine the pattern of purchasing car spare parts products sold together. The method used to determine the pattern of purchasing car spare parts products is using the Apriori algorithm and FP-Growth. The results obtained in the pattern of purchasing car spare parts products, the Apriori algorithm gets 2 rules, if the service is in the angine category, then the service is in the suspension category with a 54% possibility and if the service is in the angine category, then the body category service will be with a probability of 59% and the FP-Growth algorithm gets the results. 2 rule if service category is angine then service category will be suspension with a possibility of 54% and if service category is angine then it will be service category body with possibility of 59%, and in comparison to processing time, Apriori algorithm gets 10 seconds result while FP-Growth algorithm gets result 0 second. From these results it can be concluded that the Apriori algorithm is better in terms of processing time.

Item Type: Thesis (Diploma)
Uncontrolled Keywords: Sparepart; Basis Data; Apriori; FP-Growth
Subjects: Data Processing, Computer Science
Divisions: Fakultas Sains dan Teknologi > Program Studi Teknik Informatika
Depositing User: Ramadhan Reggya Putra
Date Deposited: 20 Nov 2020 00:29
Last Modified: 20 Nov 2020 00:29
URI: https://digilib.uinsgd.ac.id/id/eprint/35106

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