A New Framework for Dynamic Educational Marketing Segmentation in Student Recruitment: Optimizing Fuzzy C-Means with Metaheuristic Techniques
An effective educational marketing strategy requires accurate school segmentation to enhance new student recruitment. Traditional segmentation methods such as K-means are often used, but they have limitations in capturing the flexibility of school characteristics. Fuzzy C-Means (FCM) offers a more a...
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| Format: | Article |
| Language: | English |
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Ikatan Ahli Informatika Indonesia
2025-06-01
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| Series: | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
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| Online Access: | https://jurnal.iaii.or.id/index.php/RESTI/article/view/6515 |
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| author | Rizal Bakri Bobur Sobirov Niken Probondani Astuti Ansari Saleh Ahmar Pawan Kumar Singh |
| author_facet | Rizal Bakri Bobur Sobirov Niken Probondani Astuti Ansari Saleh Ahmar Pawan Kumar Singh |
| author_sort | Rizal Bakri |
| collection | DOAJ |
| description | An effective educational marketing strategy requires accurate school segmentation to enhance new student recruitment. Traditional segmentation methods such as K-means are often used, but they have limitations in capturing the flexibility of school characteristics. Fuzzy C-Means (FCM) offers a more adaptive approach by allowing each school to simultaneously have a degree of membership in several clusters. However, the performance of FCM highly depends on determining parameters such as the number of clusters (k) and the level of fuzziness (m), which are not always optimal when determined manually. This study develops a new framework for dynamic educational marketing segmentation in student recruitment by optimizing FCM using three metaheuristic techniques: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Differential Evolution (DE). Performance was evaluated using the Fuzzy Silhouette Index (FSI). The experimental results showed that DE yielded the best results with the highest FSI value (0.8023), producing eight main clusters based on the Recency, Frequency, and Monetary (RFM) model. Based on the clustering results, a personalized and adaptive marketing strategy was designed to enhance the effectiveness of student recruitment. The proposed framework enhances segmentation accuracy and supports the implementation of dynamic data-driven marketing in the context of higher education. This study also opens new directions for educational data mining research and machine-learning-based marketing strategies. |
| format | Article |
| id | doaj-art-a7387cd79a184e769f308bfca30792a1 |
| institution | Kabale University |
| issn | 2580-0760 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Ikatan Ahli Informatika Indonesia |
| record_format | Article |
| series | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| spelling | doaj-art-a7387cd79a184e769f308bfca30792a12025-08-20T03:30:56ZengIkatan Ahli Informatika IndonesiaJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)2580-07602025-06-019365966910.29207/resti.v9i3.65156515A New Framework for Dynamic Educational Marketing Segmentation in Student Recruitment: Optimizing Fuzzy C-Means with Metaheuristic TechniquesRizal Bakri0Bobur Sobirov1Niken Probondani Astuti2Ansari Saleh Ahmar3Pawan Kumar Singh4Universitas Negeri MakassarSamarkand Branch of Tashkent State University of EconomicsSTIEM BongayaUniversität de BarcelonaUniversity of DelshiAn effective educational marketing strategy requires accurate school segmentation to enhance new student recruitment. Traditional segmentation methods such as K-means are often used, but they have limitations in capturing the flexibility of school characteristics. Fuzzy C-Means (FCM) offers a more adaptive approach by allowing each school to simultaneously have a degree of membership in several clusters. However, the performance of FCM highly depends on determining parameters such as the number of clusters (k) and the level of fuzziness (m), which are not always optimal when determined manually. This study develops a new framework for dynamic educational marketing segmentation in student recruitment by optimizing FCM using three metaheuristic techniques: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Differential Evolution (DE). Performance was evaluated using the Fuzzy Silhouette Index (FSI). The experimental results showed that DE yielded the best results with the highest FSI value (0.8023), producing eight main clusters based on the Recency, Frequency, and Monetary (RFM) model. Based on the clustering results, a personalized and adaptive marketing strategy was designed to enhance the effectiveness of student recruitment. The proposed framework enhances segmentation accuracy and supports the implementation of dynamic data-driven marketing in the context of higher education. This study also opens new directions for educational data mining research and machine-learning-based marketing strategies.https://jurnal.iaii.or.id/index.php/RESTI/article/view/6515dynamic educational marketingfuzzy c-meansmetaheuristic optimizationrfmstudent recruitment |
| spellingShingle | Rizal Bakri Bobur Sobirov Niken Probondani Astuti Ansari Saleh Ahmar Pawan Kumar Singh A New Framework for Dynamic Educational Marketing Segmentation in Student Recruitment: Optimizing Fuzzy C-Means with Metaheuristic Techniques Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) dynamic educational marketing fuzzy c-means metaheuristic optimization rfm student recruitment |
| title | A New Framework for Dynamic Educational Marketing Segmentation in Student Recruitment: Optimizing Fuzzy C-Means with Metaheuristic Techniques |
| title_full | A New Framework for Dynamic Educational Marketing Segmentation in Student Recruitment: Optimizing Fuzzy C-Means with Metaheuristic Techniques |
| title_fullStr | A New Framework for Dynamic Educational Marketing Segmentation in Student Recruitment: Optimizing Fuzzy C-Means with Metaheuristic Techniques |
| title_full_unstemmed | A New Framework for Dynamic Educational Marketing Segmentation in Student Recruitment: Optimizing Fuzzy C-Means with Metaheuristic Techniques |
| title_short | A New Framework for Dynamic Educational Marketing Segmentation in Student Recruitment: Optimizing Fuzzy C-Means with Metaheuristic Techniques |
| title_sort | new framework for dynamic educational marketing segmentation in student recruitment optimizing fuzzy c means with metaheuristic techniques |
| topic | dynamic educational marketing fuzzy c-means metaheuristic optimization rfm student recruitment |
| url | https://jurnal.iaii.or.id/index.php/RESTI/article/view/6515 |
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