A Comparative Study of Naïve Bayes and K-Nearest Neighbors (KNN) Algorithms in Sentiment Analysis of ChatGPT Usage Among Students
DOI:
https://doi.org/10.30871/jaee.v9i2.11464Kata Kunci:
ChatGPT, Classification, K-Nearest Neighbors, Naïve Bayes, Sentiment Analysis, StudentsAbstrak
Penelitian ini membandingkan kinerja algoritma Naïve Bayes dan K-Nearest Neighbors (KNN) dalam analisis sentimen mahasiswa Politeknik Negeri Lhokseumawe terhadap penggunaan ChatGPT. Perbandingan ini dilakukan karena penelitian sebelumnya menunjukkan hasil yang bervariasi, di mana efektivitas kedua algoritma sangat dipengaruhi oleh konteks dan jenis data. Model dikembangkan menggunakan 9.800 data eksternal dari Twitter dan Google Play Store yang diproses melalui tahapan praproses teks dan transformasi TF-IDF, kemudian diuji pada 237 data kuesioner mahasiswa sebagai studi kasus. Evaluasi awal memperlihatkan bahwa Naïve Bayes memperoleh akurasi 88% dengan waktu prediksi 0,0063 detik, sedangkan KNN mencatatkan akurasi 83% dengan waktu prediksi 0,4760 detik. Pada pengujian dengan data kuesioner, Naïve Bayes kembali unggul dengan akurasi 79,75% dibandingkan KNN yang hanya 49,37%. Temuan ini menegaskan bahwa Naïve Bayes lebih optimal untuk klasifikasi teks berbasis opini dalam konteks ini, serta dapat dijadikan pertimbangan dalam pengembangan kebijakan akademik terkait pemanfaatan kecerdasan buatan di pendidikan tinggi.
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