Article Per Year (5 Year)
p-Index From 2020 - 2025
3.531
P-Index
This Author published in this journals
All Journal Prosiding Semnastek JOIN (Jurnal Online Informatika) JITK (Jurnal Ilmu Pengetahuan dan Komputer) Kurawal - Jurnal Teknologi, Informasi dan Industri Jurnal Riset Informatika JOURNAL INFORMATICS, SCIENCE & TECHNOLOGY MULTINETICS Jurnal Mantik Jurnal Elektra Jurnal Ekonomi Jurnal Penelitian dan Pengabdian Masyarakat Jotika
Septian Rheno Widianto
Universitas Bina Sarana Informatika
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Other

Published : 37 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Jurnal Riset Informatika

 1 
A Study Of Comparing Conceptual And Performance of K-Means and Fuzzy C-Means Algorithms (Clustering Method of Data Mining) of Consumer Segmentation Yunita Yunita; Sukrina Herman; Ahsani Takwim; Septian Rheno Widianto
Jurnal Riset Informatika Vol. 2 No. 2 (2020): March 2020 Edition
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (0.272 KB) | DOI: 10.34288/jri.v2i2.38

Abstract

Consumers, especially potential customers, are an important asset in a company that should be maintained properly. The tight competition requires companies to focus on the customer's needs. Consumer segmentation is one of the processes carried out in the marketing strategy. Consumer or consumer segmentation data mining plays a very important role in supporting the grouping process results. Based on mapping studies on data mining in support of consumer segmentation, two algorithms are often used: K-means clustering and Fuzzy C-means clustering. The attributes used for mining in customer segmentation processes are customer data, products, demographics, consumer behaviour, transactions, RFMDC, RFM (Recency, Frequency Monetary) and LTV (Life Time Value). It is important to combine the clustering algorithm to algorithm Classification, Association, and CPV to get the potential value of each cluster.
Co-Authors Ahsani Takwim Amalia Utami Arifin, Arifin Asto Purwanto B. Syukroni Baso Bayu Rimba Pratama Felicia Alhelga S.B.K Gunadi, Faustina I Putu Eka Warmayudha Indri Tri Julianto Irwan Suwandi Izzudin Abdullah Azzam Lady Agustin Fitriana Leli Leiliawati Lia Kamila Kulsum Lutfi Wardhiyana M Yahtadi Maria Magdalena Mauludyansah, Widhy Al Mohammad Kamal Reza Mohammad Thoip Abdullah Muhammad Muslih Muhammad Sony Maulana Nana Citrawati Lestari Nur Yulias Nurmalasari Nurmalasari Nurmalasari Putri Ekawati Darma Rahmatiah Rakhmadi Rahman Reza Maulana Ricky Rohmanto Rina Septiriana Risdiansyah, Deni Setyawan, Sanjaya Endra Sri Dewi Ayu Safitri Sri Farida Utami Stephen Yonathan Sudiro Sukrina Herman Sulhan Qidri Tohirin Tohirin Tutu Gondewa Ujang Sarifudin Wadi Nuryadi Yoyok Sabar Waluyo Yoyok Sabar Waluyo Yunita Yunita