Frequent pattern growth algorithm for maximizing display items

Nasyuha, Asyahri Hadi; Jama, Jalius; Abdullah, Rijal; Syahra, Yohanni; Azhar, Zulfi; Hutagalung, Juniar; Hasugian, Buyung Solihin

doi:10.12928/telkomnika.v19i2.16192

Cited by 31 publications

(30 citation statements)

References 9 publications

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“…Dalam data mining terdapat metode-metode yang dapat digunakan seperti klasifikasi, clustering, regresi, seleksi variabel, dan analisis [10]. Data mining adalah suatu kegiatan analisa data untuk mencari suatu pola tertentu, dengan jumlah data yang besar dan bertujuan utuk menghasilkan informasi yang dapat digunakan dan dikembangkan lebih lanjut [11].…”

Section: Data Miningunclassified

Penerapan Metode K-Means Untuk Menganalisis Minat Nasabah

Hutagalung¹,

Sonata²

2021

mib

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Insurance is a mechanism of protection or protection from the risk of loss by transferring the risk to another party. Sometimes a product that has just emerged becomes a product that is superior in terms of sales, so that interest in a product is not absolutely measured from the year the product was released. The constraint factors include the marketing of the product when it was launched. Offering products with low premiums along with the benefits that customers want. However, insurance companies still have difficulty in classifying superior products that are in great demand by prospective customers. For this reason, a technique for grouping insurance products is needed to make it easier for companies to see superior products and choose products that suit the needs of their customers. Analyzing and processing data using the K-Means method in the clustering of insurance products is the aim of this study. The application of the K-Means algorithm is to help calculate the purity value from the results of the clustering carried out so that the clustering of insurance products is in accordance with the needs of its customers. The application of the K-Means method with clustering techniques for data mining produces information on insurance products that are more attractive to potential customers. This is very appropriate in grouping data types because it is easier to implement and its application can filter quickly and precisely. Calculations using the K-Means method with a data sample of 55 customers obtained 3 clusters, namely cluster 1 for fire insurance which has 30 customers, cluster 2 for accident insurance 24 people and cluster 3 for health insurance 1 person.

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Section: Data Miningunclassified

Penerapan Metode K-Means Untuk Menganalisis Minat Nasabah

Hutagalung¹,

Sonata²

2021

mib

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“…Tahapan proses dalam penggunaan menurut (Nasyuha et al, 2021) Data Mining merupakan salah satu dari rangkaian Knowledge Discovery in…”

Section: Metode 1 Proses Tahapan Data Miningunclassified

Penerapan Data Mining Untuk Mendapatkan Paket Promo Perlengkapan Pesta Menggunakan Algoritma Apriori Di Celebration Peak

Erwansyah¹,

Purwadi²,

Saniman³

et al. 2021

JSSR

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Celebration Peak adalah perusahaan retail berbasis dalam penjualan perlengkapan pesta dengan produk-produk terkini sesuai dengan perkembangan zaman. Adapun produk-produk yang dijual berasal dari perusahaan dari US langsung dan produk yang dijual dibawah pengawasan dari perusahaan induk. Dengan sistem kerja yang berjalan sekarang dalam mencari barang yang kurang peminatnya terdapat masalah. Permasalahan yang terjadi di dalam sistem yang ada di Celebration Peak adalah penjualan yang tidak menentu yang artinya penjualan tersebut dapat naik dan dapat turun, sehingga dapat memperngaruhi keuntungan perusahaan, untuk meningkatkan penjualan, perusahaan melakukan promosi barang. Sehingga pihak manajemen strategi dapat menentukan kelayakan barang yang dapat dijadikan sebagai paket promosi. Untuk dapat menyelesaikan permasalahan yang ada maka salah satu cara yang dapat dilakukan untuk penentuan paket promosi adalah dengan menggunakan teknik data mining. Adapun teknik yang digunakan dalam hal ini adalah Algoritma Apriori. Apriori adalah aturan yang menyatakan asosiasi antara beberapa atribut untuk menemukan aturan satu kombinasi item atau lebih Hasil dari penelitian ini adalah, kombinasi item barang yang memiliki keterkaitan / hubungan yang diolah dengan teknik data mining menggunakan algoritma apriori dapat membantu pihak Managemen Strategi Celebration Peak dalam menentukan barang yang akan dijadikan paket promosi

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“…(Hutagalung & Sonata, 2021). The primary purpose of data mining is to obtain knowledge that is still hidden in chunks of data and the ability to be explored in the form of patterns that still need to be extracted from existing chunks of data (Nasyuha et al, 2021). With data mining, it is possible to classify cases and deaths from COVID-19 in Southeast Asia based on the attributes of the assessment of total confirmed cases and total deaths .…”

Section: Introductionmentioning

confidence: 99%

Implementation of K-Medoids Clustering Method for Indihome Service Package Market Segmentation

Hutagalung¹,

Syahril²,

Sobirin³

2022

CNAHPC

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IndiHome (Indonesia Digital Home) is a leading digital fibre optic service product consisting of fibre optic internet services, landline telephones, and interactive TV services. Although the coverage of Indihome products is extensive in the city of Medan, in marketing, Indihome products have not reached the planned target. Based on data from Indihome service package users that have been received, Indihome product users only numbered 6419 customers in all STOs in Medan City. At the same time, the target was planned by PT. Telkom Access Medan, namely Marketing Indihome products, must reach 5,000 customers per month in all STOs in Medan City. Indihome product marketing is an obstacle for PT. Telkom Access Medan, because the Indihome product is a new product, the people of Medan City do not fully know what Indihome is and what facilities they get from using the Indihome service package. Therefore PT. Telkom Access Medan needs to make a plan to make a marketing strategy. The first step that needs to be done is to segment the market for the Indihome service package. This study aimed to determine the application of Data Mining using the K-Medoids Clustering method in the Indihome service package market segmentation at PT. Telkom Access Medan. With this research, it is hoped that it can provide a reference for the results of the decision so that it can help related parties to make it easier to classify the market segmentation of the Indihome service package at PT. Telkom Access Medan. Because the value of S > 0, then the calculation is stopped and ends in the 3rd iteration. Indihome service package data processing uses the k-medoids clustering method in the form of potential, potential, and not potential STO (Sentral Telephone Automated) cluster members.

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Frequent pattern growth algorithm for maximizing display items

Cited by 31 publications

References 9 publications

Penerapan Metode K-Means Untuk Menganalisis Minat Nasabah

Penerapan Metode K-Means Untuk Menganalisis Minat Nasabah

Penerapan Data Mining Untuk Mendapatkan Paket Promo Perlengkapan Pesta Menggunakan Algoritma Apriori Di Celebration Peak

Implementation of K-Medoids Clustering Method for Indihome Service Package Market Segmentation

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