INTEGRASI NATURAL LANGUAGE PROCESSING DAN K-MEANS CLUSTERING PADA SISTEM EVALUASI PEMBELAJARAN BERBASIS REAL-TIME
DOI:
https://doi.org/10.55123/storage.v5i3.8667Keywords:
NLP, K-Means Clustering, evaluasi pembelajaran, TF-IDF, Cosine SimilarityAbstract
Penelitian ini bertujuan mengembangkan sistem evaluasi pembelajaran berbasis web yang mengintegrasikan Natural Language Processing dan K-Means Clustering secara real-time. Permasalahan utama yang diangkat adalah koreksi esai manual yang membutuhkan waktu lama, potensi subjektivitas penilaian, serta belum tersedianya sistem yang mampu memetakan performa akademik siswa secara otomatis. Sistem dikembangkan menggunakan arsitektur hibrida dengan Laravel sebagai aplikasi utama dan Python Flask sebagai layanan komputasi kecerdasan buatan. Penilaian esai dilakukan menggunakan TF-IDF dan Cosine Similarity untuk menghitung kemiripan jawaban siswa dengan kunci jawaban guru, sedangkan K-Means Clustering digunakan untuk mengelompokkan siswa berdasarkan nilai akhir, nilai pilihan ganda, dan nilai esai. Hasil implementasi menunjukkan bahwa sistem mampu menampilkan nilai ujian, status kelulusan, dan kategori performa siswa secara real-time. Sistem ini dapat membantu guru mempercepat proses evaluasi dan mendukung pengambilan keputusan pembelajaran berbasis data.
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