Perbandingan Kinerja Apriori Dan FP-Growth Dalam Pencarian Pola Asosiasi Pada Data Promosi Pendidikan
DOI:
https://doi.org/10.37253/joint.v6i2.10973Abstract
Data mining, khususnya Association Rule Mining, digunakan untuk mengidentifikasi hubungan tersembunyi dalam data dan menemukan aturan asosiasi yang bermakna, dengan algoritma seperti Apriori dan FP-Growth yang meningkatkan efisiensi dalam proses ini. Dalam konteks pendidikan, teknik ini dapat membantu sekolah dalam mengoptimalkan strategi promosi dengan menyesuaikan pesan dan penawaran berdasarkan preferensi audiens, sehingga meningkatkan efektivitas alokasi sumber daya. Dengan memanfaatkan Association Rule Mining, sekolah dapat merancang strategi berbasis data untuk menarik dan mempertahankan siswa. Pada penelitian ini akan dilakukan perbandingan penggunaan Apriori dan FP-Growth dalam mencari rule asosiasi pada data pendidikan untuk menunjang promosi sekolah SMP 1 Muhammadiyah. Algoritma Apriori dan FP-Growth dapat digunakan untuk menganalisis pola asosiasi dalam data pendidikan guna mendukung strategi promosi SMP Muhammadiyah 1. Hasil analisis menghasilkan 16 aturan asosiasi dari Apriori dan 6 aturan dari FP-Growth, Apriori menunjukkan bahwa mayoritas calon siswa berasal dari Kecamatan Kaliwates dan lebih banyak berasal dari Sekolah Dasar Negeri, sedangkan FP-Growth mengidentifikasi bahwa calon siswa potensial juga berasal dari Kecamatan Patrang dan Sumbersari, yang dapat menjadi fokus dalam promosi sekolah.
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