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M.Kom., Anita,
Fakultas Teknik Universitas Muhammadiyah Sumatera Barat
Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering

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PENENTUAN TINGKAT MINAT BELANJA ONLINE MELALUI MEDIA SOSIAL MENGGUNAKAN METODE CLUSTERING K-MEANS M.Kom., Anita,
Rang Teknik Journal Vol 1, No 2 (2018): VOLUME 1 NO 2 JUNI 2018
Publisher : Fakultas Teknik Universitas Muhammadiyah Sumatera Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31869/rtj.v1i2.758

Abstract

Social media becomes the pre-eminent businessperson in marketing the goods. This is based on the increasing use of social media from year to year. The determination of the level of interest in online shopping through social media aims to help find out which social media are in demand in online shopping. In this study the data used are social media BBM Group, Instagram, Facebook, Twitter and Whatsapp and age and gender. In determining the level of online shopping interest one method of Data Mining used is the method of Clustering K-Means. Clustering is the process of dividing data into classes or clusters based on their similarity. To assist in data processing used Rapid Miner application that is able to provide information on the level of interest in online shopping in social media
PENENTUAN TINGKAT MINAT BELANJA ONLINE MELALUI MEDIA SOSIAL MENGGUNAKAN METODE CLUSTERING K-MEANS M.Kom., Anita,
Rang Teknik Journal Vol 1, No 2 (2018): VOLUME 1 NO 2 JUNI 2018 Rang Teknik Journal
Publisher : Fakultas Teknik Universitas Muhammadiyah Sumatera Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (879.104 KB) | DOI: 10.31869/rtj.v1i2.758

Abstract

Social media becomes the pre-eminent businessperson in marketing the goods. This is based on the increasing use of social media from year to year. The determination of the level of interest in online shopping through social media aims to help find out which social media are in demand in online shopping. In this study the data used are social media BBM Group, Instagram, Facebook, Twitter and Whatsapp and age and gender. In determining the level of online shopping interest one method of Data Mining used is the method of Clustering K-Means. Clustering is the process of dividing data into classes or clusters based on their similarity. To assist in data processing used Rapid Miner application that is able to provide information on the level of interest in online shopping in social media
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