Reduksi Dimensi untuk Meningkatkan Kinerja Pengklasteran Perilaku Siswa pada Sistem e-Learning
Abstrak
Pandemi corona telah mengubah proses pembelajaran dari yang semula tatap muka secara langsung (offline) menjadi pembelajaran secara daring (online). Pembelajaran daring ini menyebabkan kesulitan dalam pemantauan perilaku siswa oleh guru karena berkurangnya interaksi secara langsung. Lebih dari itu, siswa seringkali merasa terisolasi sehingga jika dibiarkan, situasi ini akan menyebabkan kegagalan dalam prestasi belajarnya. Permasalahan ini mendorong banyak dilakukannya penelitian tentang pemodelan yang berkaitan dengan perilaku siswa. Namun, para peneliti sebelumnya tidak banyak yang fokus pada peningkatan kinerja model atau sistem yang dibangun, padahal kinerja model ini sangat berpengaruh terhadap kualitas hasil pemetaan perilaku siswa. Untuk itu, makalah ini berfokus pada peningkatan kinerja pengklasteran perilaku siswa ketika berinteraksi dengan sistem e-Learning. Peningkatan kinerja dilakukan dengan reduksi dimensi pada data siswa dengan Principal Component Analysis (PCA). Selanjutnya, dua teknik inisialisasi titik pusat klaster dieksplorasi untuk mendapatkan hasil yang optimal, yaitu: random dan K-means++. Untuk pengukuran kualitas klaster, makalah ini menggunakan silhouette index. Hasil pengujian menunjukkan bahwa klaster dengan kualitas tertinggi dicapai oleh penerapan PCA dengan tujuh komponen dan banyaknya klaster tiga sampai empat untuk semua teknik inisialisasi titik pusat. Klaster yang berkualitas ini dapat membantu guru dalam memonitor perilaku siswa pada pembelajaran secara daring.
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