RFM stands for Recency, Frequency, and Monetary. RFM is a simple but effective method that can be applied to market segmentation. RFM analysis is used to analyze customer’s behavior which consists of how recently the customers have purchased (recency), how often customer’s purchases (frequency), and how much money customers spend (monetary). In this study, RFM analysis has been used for product segmentation is to be arrayed in terms of recent sales (R), frequent sales (F), and the total money spent (M) using the data mining method. This study has proposed a new procedure for RFM analysis (in product segmentation) using the k-Means method and eight indexes of validity to determine the optimal number of clusters namely Elbow Method, Silhouette Index, Calinski-Harabasz Index, Davies-Bouldin Index, Ratkowski Index, Hubert Index, Ball-Hall Index, and Krzanowski-Lai Index, which can improve the objectivity and similarity of data in product segmentation so that it can improve the accuracy of the stock management process. The evaluation results showed that the optimal number of clusters for the k-Means method applied in the RFM analysis consists of three clusters (segmentation) with a variance value of 0.19113.

<p class="SammaryHeader" align="center"><strong><em>Abstract</em></strong></p><p><em>This paper aims to convey the development of the effect of the population on the number of poverty in the city of Palembang from 2010 to 2015.There is no accurate calculation made to determine the number of poor people in Indonesia, always with controversy because each calculation uses different criteria.This differentiation is based on its causing factors to allow specific alleviation policy implication. The cause of such poverty, in general, is that the poor people have no capacity and capability to access economic sources. This analysis is done using simple linear regression method, population level (X) and poverty (Y) in Palembang city year 2010 - 2015. From the data, it can be concluded that the variable of Population (X) has negative influence to the variable Number of Poverty (Y) in Palembang City. Simultaneously, the number of population has an effect on the amount of poverty in the city of Palembang by 0,398%, while -14,045% and the rest influenced by the variable outside of studied.</em></p><p><strong><em>Keyword : </em></strong><em>Poor people, Regression, Causes Of Poverty</em></p><p align="center"><strong><em>Abstrak</em></strong></p><p><em>Jurnal ini bertujuan menggambarkan pengembangan pengaruh populasi pada jumlah kemiskinan di kota Palembang dari tahun 2010 sampai tahun 2015. Tidak ada penghitungan yang akurat yang telah dibuat untuk menentukan jumlah orang miskin di Indonesia, selalu muncul kontroversi karena setiap penghitungan memiliki kriteria tersendiri. Perbedaan ini didasarkan pada faktor penyebab yang berdampak pada implikasi politik. Penyebab kemisikinan, umumnya adalah bahwa orang-orang miskin tidak memiliki kapasitas untuk memasuki sumber ekonomi. Analisis dilakukan dengan menggunakan metode regresi linier sederhana, tingkat populasi (X) dan kemiskinan (Y) di kota Palembang tahun 2010-2015. Dari data disimpulkan bahwa variabel jumlah populasi (X) memiliki pengaruh negatif pada variabel jumlah kemiskinan di kota Palembang. Secara simultan, jumlah populasi memiliki pengaruh pada jumlah kemiskinan di kota Palembang yaitu 0,398%, sedangkan -14,045% dan sisanya dipengaruhi oleh variabel diluar studi ini.</em></p><strong><em>Kata kunci :</em></strong><em> Orang miskin, Regresi, Penyebab Kemiskinan</em>

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