PEMBENTUKAN ATURAN ASOSIASI MENGGUNAKAN ALGORITMA APRIORI UNTUK REKOMENDASI BUNDLING PRODUK SEMBAKO (Studi Kasus : Toko Solo Latri)

SARI, OKTALIA KUMALA (2023) PEMBENTUKAN ATURAN ASOSIASI MENGGUNAKAN ALGORITMA APRIORI UNTUK REKOMENDASI BUNDLING PRODUK SEMBAKO (Studi Kasus : Toko Solo Latri). S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

The increasing number of buying and selling transactions of goods in Indonesia is shown by the rapid development of minimarkets into modern markets and new equipment. The Solo Latri shop sells various daily necessities for Indonesians, such as granulated sugar, flour and instant noodles. However, there was one problem: an ineffective sales process. In other words, the store suffers a loss because there are still unsold items. research using industry standard process methods for data mining (CRISP-DM) in this study. By using IT-based applications, the apriori algorithm can be used in a sales system to provide recommendations to customers and calculate the relationship between the apriori scores and the products they buy. A term "product bundling" refers to when several different goods or services are combined and provided to a customer as a single package. Data used for three months, from February 1 to April 20, 2023, generated seven association rules in the test. This test uses a minimum support value of 50% and a minimum confidence of 50% from 4290 food sales transaction data; the test results show that the apriori algorithm can be applied to web-based applications, as shown by the results of calculations performed manually and generated by the application yielding the same conclusion. Keywords: Apriori, Rule Association, Industry Standard Process For Data Mining Semakin banyaknya transaksi jual beli barang di Indonesia ditunjukkan oleh pesatnya perkembangan minimarket menjadi salah satu pasar modern dan perlengkapan baru. Toko Solo Latri menjual berbagai kebutuhan sehari-hari, seperti gula pasir, tepung terigu, dan mie instan. Namun, ada satu masalah: proses penjualan yang tidak efektif. Dengan kata lain, toko tersebut mengalami kerugian karena masih ada barang yang tidak terjual. Penelitian menggunakan metode cross industry standard process for data mining (CRISP-DM). Dengan menggunakan aplikasi berbasis TI, algoritma apriori dapat digunakan dalam sistem penjualan untuk memberikan rekomendasi kepada pelanggan dan menghitung hubungan antara skor apriori dan produk yang dibelinya. Sebuah istilah "bundling produk" mengacu pada ketika beberapa barang atau jasa berbeda digabungkan dan diberikan kepada pelanggan sebagai satu paket. Data yang digunakan selama tiga bulan, dari tanggal 1 Februari hingga 20 April 2023, menghasilkan tujuh aturan asosiasi dalam pengujian. Pengujian ini menggunakan nilai minimum support 50% dan minimum confidence 50% dari 4290 data transaksi penjualan makanan; hasil pengujian menunjukkan bahwa algoritma apriori dapat diterapkan pada aplikasi berbasis web, seperti yang ditunjukkan oleh hasil perhitungan yang dilakukan secara manual dan dihasilkan oleh aplikasi menghasilkan kesimpulan yang sama. Kata Kunci : Apriori, Rule Association, Industry Standard Process For Data Mining

Item Type: Thesis (S1)
Call Number CD: FIK/INFO. 23 070
Call Number: SIK/15/23/047
NIM/NIDN Creators: 41519010014
Uncontrolled Keywords: Apriori, Rule Association, Industry Standard Process For Data Mining
Subjects: 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum
000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika
500 Natural Science and Mathematics/Ilmu-ilmu Alam dan Matematika > 510 Mathematics/Matematika > 518 Numerical Analysis/Analisis Numerik, Analisa Numerik > 518.1 Algorithms/Algoritma
Divisions: Fakultas Ilmu Komputer > Informatika
Depositing User: ADELINA HASNA SETIAWATI
Date Deposited: 08 Sep 2023 04:11
Last Modified: 08 Sep 2023 04:11
URI: http://repository.mercubuana.ac.id/id/eprint/79938

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