eprintid: 50291
rev_number: 11
eprint_status: archive
userid: 13116
dir: disk0/00/05/02/91
datestamp: 2022-04-07 03:53:40
lastmod: 2022-04-07 03:53:40
status_changed: 2022-04-07 03:53:40
type: thesis
metadata_visibility: show
contact_email: muchti.nurhidaya@uin-suka.ac.id
creators_name: Nelly Amalia, NIM.: 16650059
title: ANALISIS PERBANDINGAN ALGORITMA APRIORI DAN
FP-GROWTH TERHADAP DATA TRANSAKSI PENJUALAN
UNTUK MENGETAHUI POLA PEMBELIAN KONSUMEN
(STUDI KASUS TOKO MAGENTA CELL)
ispublished: pub
subjects: TB
divisions: jur_tinf
full_text_status: restricted
keywords: teknik data mining; algoritma; apriori; weka
note: Pembimbing: Rahmat Hidayat., S. Kom.,M.Cs.
abstract: Magenta Cell is a store with a variety of gadget products located at Indramayu
district. More than just tens or even hundreds transactions happens from the sale of
products everyday. However, that data is only continuously pilling up without being
utilized. Meanwhile, this data can be used for being a useful information to support
the business processes in the store that are also profitable.
Therefore, with data mining techniques, transaction data at Magenta Cell
stores in August, September, October 2019 will be processed to find consumer
purchasing patterns. The data was previously grouped into 12 groups namely
Accessories, SIM card, Vouchers, Headsets, Flashdisks, Memory, Tampered Glass,
Cases, Power Banks, Batteries, Chargers, and Cables. The information to be
obtained is obtained from the results of analyzing Apriori Algorithms and
FP-Growth using Python and Weka tools.
This research is able to implement data mining techniques using Apriori
Algorithms and FP-Growth with Python and Weka tools. Then, the result of the
research is analyzed to know the comparison from each algorithm, so that it will be
known the estimated patterns of consumer purchases from the Magenta Cell store.
From the result of the analysis, ut can be known that apriori algorithm is better if be
seen from accuracy because it can reach the value of confidence 91 % on rule
voucher => sim card, at the same time FP growth algorithm is better if be seen based
on shorter time. There are 6 purchase pattern rule that can be known, i.e. tampered
glass =>case, case =>headset, cables => case, accerssories + tampered glass =>case,
voucher =>sim card, and tampered glass + headset => case.
date: 2020-02-16
date_type: published
pages: 127
institution: UIN SUNAN KALIJAGA YOGYAKARTA
department: FAKULTAS SAINS DAN TEKNOLOGI
thesis_type: skripsi
thesis_name: other
citation:   Nelly Amalia, NIM.: 16650059  (2020) ANALISIS PERBANDINGAN ALGORITMA APRIORI DAN FP-GROWTH TERHADAP DATA TRANSAKSI PENJUALAN UNTUK MENGETAHUI POLA PEMBELIAN KONSUMEN (STUDI KASUS TOKO MAGENTA CELL).  Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.   
document_url: https://digilib.uin-suka.ac.id/id/eprint/50291/1/16650059_BAB%20I_BAB%20PENUTUP_DAFTAR%20PUSTAKA.pdf
document_url: https://digilib.uin-suka.ac.id/id/eprint/50291/2/16650059_BAB%20II%20SAMPAI_BAB%20IV.pdf