Analisis Sentimen Berbasis Aspek pada EDOM Pembelajaran Menggunakan Metode CNN dan Word2vec
DOI:
https://doi.org/10.26418/justin.v12i3.75610Keywords:
Deep Learning, Neural Network, OversamplingAbstract
Penelitian ini secara khusus mengeksplorasi analisis sentimen berbasis aspek pada Evaluasi Dosen Oleh Mahasiswa (EDOM) di Institut Teknologi Telkom Purwokerto (ITTP) dengan jumlah dataset sebanyak 5116. Dengan menerapkan metode Convolutional Neural Network (CNN) dan Word2Vec, tujuan utama penelitian adalah mengidentifikasi aspek-aspek yang muncul dalam sentimen opini mahasiswa terkait EDOM ITTP. Selain itu, penelitian ini berupaya mengevaluasi akurasi model klasifikasi sentimen berbasis aspek menggunakan kombinasi CNN dan Word2Vec, confussion matrix digunakan untuk mengukur tingkat akurasi model. Proses penelitian melibatkan penerapan teknik oversampling untuk mengatasi ketidakseimbangan data pada kelas sentimen dengan jumlah data. Dalam menanggulangi permasalahan tersebut, variasi metode oversampling, seperti SMOTE, Random Oversampling, ADASYN, SMOTE-NC, dan Borderline SMOTE, diimplementasikan. Hasil penelitian menunjukkan peningkatan signifikan dalam akurasi model CNN setelah menerapkan algoritma oversampling, mengukuhkan keberhasilan implementasi sentimen berbasis aspek pada EDOM ITTP. Penelitian ini memberikan kontribusi berharga dalam pemahaman dan pengembangan analisis sentimen, terutama dalam konteks pembelajaran, dengan mempertimbangkan aspek-aspek spesifik dalam opini mahasiswa. Temuan ini dapat menjadi dasar bagi perkembangan lebih lanjut dalam meningkatkan pengalaman evaluasi dosen oleh mahasiswa di lingkungan pendidikan tinggi.References
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