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All Journal Jurnal Indonesia Sosial Teknologi Cerdika: Jurnal Ilmiah Indonesia
Jerry Heikal
Universitas Bakrie Jakarta
Humanities Computer Science & IT Economics, Econometrics & Finance Education Environmental Science Health Professions Law, Crime, Criminology & Criminal Justice Public Health Social Sciences

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SEGMENTASI PELANGGAN MENGGUNAKAN K-MEANS CLUSTERING STUDI KASUS PELANGGAN UHT MILK GREENFIELD Ira Ariati; Reza Nugraha Norsa; Lurinjani Akhsan; Jerry Heikal
Cerdika: Jurnal Ilmiah Indonesia Vol. 3 No. 7 (2023): Cerdika : Jurnal Ilmiah Indonesia
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/cerdika.v3i7.639

Abstract

Penelitian ini bertujuan untuk melakukan segmentasi pelanggan menggunakan metode K-Means Clustering dalam kasus pelanggan susu UHT Greenfield. Segmentasi pelanggan penting untuk memahami preferensi, kebutuhan, dan karakteristik pelanggan yang berbeda, sehingga perusahaan dapat mengarahkan upaya pemasaran dengan lebih efektif. Metode K-Means Clustering digunakan untuk mengelompokkan pelanggan berdasarkan atribut tertentu, seperti preferensi rasa, alamat pengiriman, dan depot penjualan. Data pelanggan Greenfield UHT Milk dikumpulkan, termasuk variabel seperti frekuensi pembelian, volume pembelian, dan preferensi rasa. Data penelitian dianalisis menggunakan analisis K-Means Clustering. Hasil penelitian dikategorikan menjadi 3 klaster, yaitu: 1. Klaster Premium : Pengiriman terbanyak ke Pamengkasan, produk terbanyak yang dibeli adalah Greenfield UHT full cream 250 ml, Meskipun kuantitas pembelian tidak terlalu tinggi, mereka menghasilkan penjualan yang signifikan, karena mereka menyukai kemasan minuman tunggal yang lebih besar yaitu 250 ml2. Cluster Sedang: Pengiriman terbanyak ke Jembrana, produk yang paling banyak dibeli adalah Greenfield UHT full cream 125 ml, Jumlah penjualan yang sedikit, produk yang dibeli dengan ukuran terkecil, membuat cluster ini memberikan penjualan terkecil di antara cluster lainnya dan mereka fokus pada harga dalam pembelian mereka3. Cluster Curah Pengiriman terbanyak ke Jember, Produk yang banyak dibeli adalah Greenfield UHT full cream 250 ml, Intensitas pembelian mereka kecil tetapi jumlah pembelian mereka sangat besar sehingga menghasilkan nilai jual yang signifikan.
Customer Segmentation With K-Means Clustering Suzuki Mobil Bandung Customer Case Study Dedi Kadarsah; Jerry Heikal
Jurnal Indonesia Sosial Teknologi Vol. 5 No. 3 (2024): Jurnal Indonesia Sosial Teknologi
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jist.v5i3.935

Abstract

In the city of Bandung, it is recorded that until February 2023 as many as 500 thousand private cars crowded the streets in the city of Bandung. People's needs for private cars are met through the purchase of new cars from dealers or used purchases. As a dealer, the main task is to meet car sales targets every month and year. Suzuki dealers, especially in Bandung, do not have solid information about what type of car is most liked by the people of Bandung, what is the background of the customers and what marketing efforts are most optimal to increase sales. Suzuki car sales data for the June-October 2023 period was analyzed as many as 165 sales from various types of cars, customer domicile, customer's proffesion and marketing efforts carried out until the purchase occurred and the choice of payment method. In this paper, a clustering analysis of the K-means method with 4 clusters with car type, customer domicile location, marketing effort, customer profession, transmission type and payment method is made. Analysis performed with IBM SPSS v.29 program.The type of Carry passenger vehicle is the choice of many Suzuki customers in Bandung and Suzuki customers mostly come from the people of Bandung and around Bandung who work as entrepreneurs and traders. Suzuki Bandung needs to maintain and improve Canvansing as an effort to acquire customers as can be seen from the analysis of customer segmentation data in this paper
Co-Authors Dedi Kadarsah Ira Ariati Lurinjani Akhsan Reza Nugraha Norsa