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All Journal Epsilon: Jurnal Matematika Murni dan Terapan KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Jambura Journal of Mathematics Journal of Mathematics: Theory and Applications Jurnal Pengabdian Ilung (Inovasi Lahan Basah Unggul) Bioscientiae, Jurnal ilmu ilmu Biologi RAGAM: Journal of Statistics and Its Application
Soesanto, Oni
Program Studi Ilmu Komputer, Fakultas Matematika Dan Pengetahuan Alam, Universitas Lambung Mangkurat, Indonesia
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Journal : RAGAM: Journal of Statistics and Its Application

 1 
SEGMENTASI PELANGGAN MENGGUNAKAN METODE K-MEANS CLUSTERING BERDASARKAN MODEL RFM (RECENCY, FREQUENCY, MONETARY) Muhammad Hafidz Anshary; Oni Soesanto; Ayatullah Ayatullah
RAGAM: Journal of Statistics & Its Application Vol 1, No 1 (2022): RAGAM: Journal of Statistics and Its Application
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/ragam.v1i1.7382

Abstract

Companies or entrepreneurs must better understanding customers data in all aspects, including detecting similarities and differences among customers, predicting their behavior, and offering better options and opportunities to customers. Customer segmentation is carried out to obtain this information, which is part of CRM (Customer Relationship Management). One of the general models in the application of customer segmentation is the RFM (Recency, Frequency, and Monetary) model. This research method uses a combination of the RFM model and clustering. RFM is used as a description of customer behavior in conducting transactions. Clustering is a process that is widely used and is designed to categorize data. Clustering uses the K-Means Algorithm to determine the number of clusters using the Elbow and Silhouette methods. The application of RFM analysis and the K-Means resulted in two customer segments, namely potential customers and non-potential customers. Potential customers have the characteristics of frequent transactions and also large expenses. Non-potential customers have the characteristics of infrequent transactions and also standard expensesKeywords:  Customer Segmentation, RFM Model, K-Means Clustering
QUICK ROBUST CLUSTERING USING LINKS (QROCK) UNTUK PENGELOMPOKAN DESA KABUPATEN BANJAR Muhammad Rizki Shofari; Oni Soesanto
RAGAM: Journal of Statistics & Its Application Vol 3, No 1 (2024): RAGAM: Journal of Statistics & Its Application
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/ragam.v3i1.12805

Abstract

AbstractAccurate village profile planning needs to be done with mapping based on its characteristics. Village grouping based on the characteristics of village facilities and potential or its characteristics based on the Building Village Index indicator can help determine priorities in village development. In this study, mixed data was used, with numerical data grouping using Hierarchical Agglomerative Nesting (AGNES) algorithm and categorical data with Quick Robust Clustering Using Links (QROCK). The resulting clusters are then combined using the QROCK Ensemble algorithm (algCEBMDC). The data is sourced from the 2021 Village Potential Data Collection (PODES) by the Central Statistics Agency in 277 villages in Banjar Regency, including 18 numerical variables and 29 categorical variables. The results of the study obtained  optimal clusters based on the ratio of within-group standard deviation (SW) to between-group standard deviation (SB)  resulting in a ratio of  4.82.10-9 with a threshold of 0.4 to 0.9 resulting in 6  clusters. The  best cluster results are cluster 4 (4 villages) and cluster  3 (14  villages), then cluster 2 (villages) and cluster 1 (186  villages), and clusters that need development priorities are cluser 5 (2 villages) and cluster 6 (1 village) which are outliers based on processing results. Keywords:   Village Grouping,  Cluster, AGNES Algorithm, Quick Robust Clustering Using Links (QROCK), AlgCEBMDC
Co-Authors Abdul Hakim Maulana Andi Farmadi Andry Nor Indrawan Arfan Eko Fahrudin Arief Rahmad Maulana Akbar Ayatullah Ayatullah Azidi Irwan Bambang Sugiyanto Sugiyanto Dewi Sri Susanti Dodon Turianto Nugrahadi Fairudz Shahura Fatma Indriani Hayqal Hazmi Qastari Hayqal Mochamad Arief Soendjoto Mohammad Mahfuzh Shiddiq Muhammad Ahsar Karim Muhammad Hafidz Anshary Muhammad Mahfuzh Shiddiq Muhammad Mefta Eryshady Muhammad Rizki Shofari Nurul Dasima Astuti Oktarini Oktarini Pamona Dwi Rahayu Paola Anna Elena Bianchi Paola Bianchi Pardi Affandi Rahmad Eko Sanjaya Rahmat Rahmat Yunus Rahmat Yunus Raifa Azizia Mustaqima Rani Sasmita Siti Sulistiani Sri Elina Herni Yulianti Susilo, Tanto Budi Thresye Thresye Yuana Sukmawaty Yuniardi Wahyu Nugraha Yuyun Hidayat