ANALISIS SEGMENTASI PELANGGAN CINEMA BOOKING BIOSKOP XYZ DAN ASSOCIATION RULES PEMESANAN MAKANANNYA

SETIAWAN, ERIC (2022) ANALISIS SEGMENTASI PELANGGAN CINEMA BOOKING BIOSKOP XYZ DAN ASSOCIATION RULES PEMESANAN MAKANANNYA. S1 thesis, Universitas Mercu Buana Jakarta.

[img]
Preview
 Text (HAL COVER)
01. COVER.pdf
Download (1MB) | Preview
[img]
Preview
 Text (ABSTRAK)
02. ABSTRAK.pdf
Download (558kB) | Preview
[img] Text (BAB I)
03. BAB 1.pdf
Restricted to Registered users only
Download (623kB)
[img] Text (BAB II)
04. BAB 2.pdf
Restricted to Registered users only
Download (793kB)
[img] Text (BAB III)
05. BAB 3.pdf
Restricted to Registered users only
Download (629kB)
[img] Text (BAB IV)
06. BAB 4.pdf
Restricted to Registered users only
Download (1MB)
[img] Text (BAB V)
07. BAB 5.pdf
Restricted to Registered users only
Download (614kB)
[img] Text (DAFTAR PUSTAKA)
08. DAFTAR PUSTAKA.pdf
Restricted to Registered users only
Download (555kB)
[img] Text (LAMPIRAN)
09. LAMPIRAN.pdf
Restricted to Registered users only
Download (891kB)

Abstract

The rapid development of technology makes competition in various business fields getting tighter. In a business that deals with customers, a strategy is needed to be able to make customers remain royal. Companies are required to make data-driven decisions for formulating the right strategy to maintain good relationships with customers. To maintain this good relationship, it is necessary to understand the behavior or habits of customers based on transaction history. Data mining becomes one of the solutions. K-Means clustering algorithm can be used to divide customers into several groups based on their characteristics. Eclat's association rules algorithm can be used to find out the pattern of customer habits in buying food packages. Based on the K-Means algorithm, 3 clusters are obtained. Cluster 0 as bronze customer, cluster 1 as gold customer, and cluster 2 as silver customer. From gold customers, the results of association rules using a minimum support value of 1 % and a minimum confidence value of 75% using Eclat algorithm are 5 rules. Popcorn Sweet Glaze Medium, Popcorn Salt Medium, and Mineral Water are the results of the rules with the highest confidence value of 79,59%. Key words: Customer Segmentation, RFM, Clustering, Association Rules, Data Mining Perkembangan teknologi yang pesat membuat persaingan diberbagai bidang bisnis menjadi semakin ketat. Dalam bisnis yang berhubungan dengan pelanggan, dibutuhkan strategi untuk dapat membuat pelanggan tidak berpaling ke kompetitor. Perusahaan diharuskan mengambil keputusan berdasarkan data-driven dalam penyusunan strategi yang tepat untuk dapat menjaga hubungan baik dengan pelanggan. Untuk menjaga hubungan baik tersebut, diperlukan pemahaman perilaku atau kebiasaan yang dilakukan pelanggan berdasarkan histori transaksi. Data mining menjadi salah satu solusi untuk dapat melakukan hal tersebut. Algoritma clustering K-Means dapat digunakan untuk membagi pelanggan kedalam beberapa grup berdasarkan karakteristiknya. Algoritma association rules Eclat dapat digunakan untuk mengetahui pola kebiasaan pelanggan dalam membeli paket makanan. Berdasarkan algoritma K-Means didapatkan hasil 3 cluster. Cluster 0 sebagai cluster pelanggan bronze, cluster 1 sebagai cluster pelanggan gold, dan cluster 2 sebagai cluster pelanggan silver. Dari pelanggan gold didapatkan hasil association rules menggunakan algoritma Eclat sebanyak 5 rules dengan nilai minimal support 1% dan nilai minimal confidence 75%. Popcorn Sweet Glaze Medium, Popcorn Salt Medium, dan Mineral Water merupakan hasil rules dengan nilai confidence tertinggi yaitu 79,59%. Kata kunci: Segmentasi Pelanggan, RFM, Clustering, Association Rules, Data Mining

Item Type: Thesis (S1)
Call Number CD: FIK/INFO. 22 204
Call Number: SIK/15/23/023
NIM/NIDN Creators: 41518110017
Uncontrolled Keywords: Segmentasi Pelanggan, RFM, Clustering, Association Rules, 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
000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 020 Library and Information Sciences/Perpustakaan dan Ilmu Informasi > 025 Operations, Archives, Information Centers/Operasional Perpustakaan, Arsip dan Pusat Informasi, Pelayanan dan Pengelolaan Perpustakaan > 025.3 Bibliographic Analysis and Control/Bibliografi Analisis dan Kontrol Perpustakaan > 025.35 Cooperative Cataloging, Classification, Indexing/Pengatalogan Khusus, Klasifikasi, Pengindeksan
100 Philosophy and Psychology/Filsafat dan Psikologi > 150 Psychology/Psikologi > 154 Subconscious and Altered States and Process/Psikologi Bawah Sadar > 154.6 Sleep Phenomena/Fenomena Tidur > 154.63 Dreams/Mimpi > 154.634 Analysis/Analisis
Divisions: Fakultas Ilmu Komputer > Informatika
Depositing User: ADELINA HASNA SETIAWATI
Date Deposited: 06 Apr 2023 06:38
Last Modified: 06 Apr 2023 06:38
URI: http://repository.mercubuana.ac.id/id/eprint/76146

Actions (login required)

View Item View Item
UMB Repository is powered by EPrints 3 which is developed by the School of Electronics and Computer Science at the University of Southampton. More information and software credits.