Implementasi Cosine Similarity dan K-Means Clustering pada Data Kuesioner untuk Analisis Efektivitas Pembelajaran Daring
DOI:
https://doi.org/10.30872/atasi.v5i1.3577Keywords:
online learning, lecture obstacles, cosine similarity, K-Means clustering, unsupervised learning, pembelajaran daring, kendala perkuliahanAbstract
Penelitian ini bertujuan untuk mengevaluasi kesiapan mahasiswa dalam pembelajaran daring dengan memanfaatkan metode K-Means clustering untuk mengelompokkan hasil survei berdasarkan kemiripan data. K-Means, sebagai metode unsupervised learning, berfungsi mengklasifikasikan data ke dalam beberapa cluster, sehingga pola tersembunyi yang relevan dengan kesiapan pembelajaran daring dapat diidentifikasi. Dalam penelitian ini, data teks yang diperoleh dari survei mahasiswa diproses melalui tahap pre-processing untuk meningkatkan akurasi dan efisiensi algoritma dalam mengelompokkan data. Selain itu, cosine similarity diterapkan untuk mengukur tingkat kemiripan antar teks, sehingga mempermudah dalam mengidentifikasi kesamaan kendala yang dihadapi mahasiswa selama proses pembelajaran daring. Analisis clustering menunjukkan bahwa beberapa kendala utama yang dihadapi mahasiswa dalam pembelajaran daring meliputi sinyal internet yang tidak memadai, lingkungan belajar yang kurang mendukung, kesulitan dalam memahami materi, serta hambatan komunikasi baik dengan dosen maupun dalam kolaborasi kelompok. Hasil ini memberikan wawasan bagi pihak universitas untuk mengembangkan strategi pembelajaran daring yang lebih adaptif, dengan upaya mengatasi kendala-kendala spesifik yang dihadapi mahasiswa. Dengan demikian, kualitas dan efektivitas proses belajar-mengajar dapat ditingkatkan secara signifikan, membantu mahasiswa dalam mengoptimalkan pengalaman belajar daring.
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