Segmentation of university customers loyalty based on RFM analysis using fuzzy c-means clustering

Hidayat, Syahroni; Rismayati, Ria; Tajuddin, Muhammad; Merawati, Ni Luh Putu

doi:10.14710/jtsiskom.8.2.2020.133-139

Cited by 3 publications

(3 citation statements)

References 24 publications

(38 reference statements)

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“…So, 28.86% tend to leave 17 Agustus University in the future. The prediction is almost near the previous study, a 29% churn customer predicted [11].…”

Section: Resultssupporting

confidence: 75%

“…To identify a user who tends to leave or remain in the institution, the churn prediction can be used so the campus possibilities of losing students can be avoided [1]. In churn analysis, one model is commonly applied is the analysis of Recency, Frequency, and monetary (RFM) [10], [11]. RFM model analysis generally processes the customer's transaction data include latest transaction date (Recency), transaction amount (Frequency), and monetary amount per transaction (monetary) [10].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Churn Prediction on Higher Education Data with Fuzzy Logic Algorithm

Supangat¹,

Saringat²,

Kusnanto³

et al. 2021

Journal of Information Systems

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Colleges are an optional final stage of formal education. However, with time, the Management Section finds the fact that the student churn rate in the university scope becomes a problem. The purpose of this research is to predict whether the student will be churn or loyal in the future, the data will be taken from 2014. The analytical technique used in this research process is the Fuzzy method of C-Means. At a university, the variable that will be tested is the Length of students who are currently attending higher education at the University or campus, when the last student pays enrollment, the last payment period, and the student's total payment. The data used as many as 100 datasets were collected in this research, starting from 2014 to 2020. Of the 100 Datasets of informatics engineering, students gained 92% of loyal students, and 8% of students predicted to churn.

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“…So, 28.86% tend to leave 17 Agustus University in the future. The prediction is almost near the previous study, a 29% churn customer predicted [11].…”

Section: Resultssupporting

confidence: 75%

Section: Introductionmentioning

confidence: 99%

Churn Prediction on Higher Education Data with Fuzzy Logic Algorithm

Supangat¹,

Saringat²,

Kusnanto³

et al. 2021

Journal of Information Systems

View full text Add to dashboard Buy / Rent full text

show abstract

“…Klaster adalah sebuah himpunan dari objek data yang mirip satu sama lain dalam suatu klaster dan tidak mirip dengan objek-objek yang ada di luar klaster. Pemanfaatan klasterisasi sangat luas di berbagai bidang, antara lain untuk menentukan prioritas perbaikan jalan [2], pengolahan citra digital [3], segmentasi pelanggan universitas [4], dan pengelompokan wilayah berdasarkan kapasitas investasi [5].…”

Section: Pendahuluanunclassified

Regional clustering based on economic potential with a modified fuzzy k-prototypes algorithm for village developing target determination

Prasetyo

2022

J. Teknol. dan Sist. Komput

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The clustering algorithm can group regions based on economic potential with mixed attributes data, consisting of numeric and categorical data. This study aims to group villages according to their economic potential in determining village development targets in Demak Regency using the fuzzy k-prototypes algorithm and modified Eskin distance to measure the distance of categorical attributes. The data used are PODES2018 data and the 2019 Wilkerstat Mapping. Village clustering produces three village clusters according to their economic potential, namely low, medium, and high economic clusters. Clusters of high economic potential are located on the main transportation routes of Semarang–Kudus and Semarang–Grobogan. However, villages on the main transportation route are still included in the low economic cluster. Considering the status of the urban/rural village classification, most of these villages are included in the urban village category. The results of this clustering can be used to determine village development targets in increasing the Village Developing Index in Demak Regency.

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Segmentation of university customers loyalty based on RFM analysis using fuzzy c-means clustering

Cited by 3 publications

References 24 publications

Churn Prediction on Higher Education Data with Fuzzy Logic Algorithm

Churn Prediction on Higher Education Data with Fuzzy Logic Algorithm

Regional clustering based on economic potential with a modified fuzzy k-prototypes algorithm for village developing target determination

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