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Abstrak :
ABSTRAK
Penelitian ini bertujuan untuk mengidentifikasi perilaku pembelian, berapa banyak kelompok dan profil konsumen terbentuk untuk menemukan pola dari perilaku pembelian berdasarkan situasi. Dari data yang diberikan, metode RFM dan K-Means clustering digunakan untuk mengidentifikasi perilaku pembelian dan profil konsumen. Hasil dari penelitian ini menunjukkan bahwa cluster konsumen terbentuk berbeda-beda pada setiap kategori produk berdasarkan nilai RFM dan K-Means clustering. Waktu puncak pembelian juga ada perbedaan pada tiap kelompoknya. Waktu terbaik untuk mengirimkan notifikasi dan pesan adalah ketika mendekati waktu puncak pembelian. Tentunya, hal ini sangat berguna untuk merencanakan marketing kontekstual dan iklan bertarget yang didesain berdasarkan kelompok konsumen dan perilaku pembelian.

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ABSTRACT
The objectives of this research are to identify customer purchase behavior, how many customer clusters, and customer profile are formed also to find the pattern of customer purchase behavior based on situation. From the data supplied, the RFM method and K Means clustering are used to identify the customer purchase behavior and profiles. The result of this research is shown that the customer clusters are formed differently in every product category based on the RFM value and K Means clustering. There are also differences in peak hour for every customer cluster. The best situation to deliver notification and personal message is near the peak hour. Indeed, this matter is useful to make contextual marketing and targeted advertising that designed based on customer cluster data and all the hourly purchase behavior.

Abstrak :
ABSTRACT
The objectives of this research are to identify customer purchase behavior, obtain the number of customer clusters, and form customer profile in order to find situation-based customer purchase behavior pattern. From the given data, the RFM method and K-Means clustering are used to identify the customer purchase behavior and profiles. The result of this research showed that the customer clusters are formed differently in every product category based on the RFM value and K-Means clustering. There are also differences in peak hour for each customer cluster. The best time to deliver notifica- tions and personal messages is near the peak hour. Indeed, this matter is useful to create contextual marketing and targeted advertising that is designed based on customer cluster and purchase behavior.