Komparasi Algoritma Klasifikasi untuk Analisis Sentimen Kinerja Dosen
DOI:
https://doi.org/10.57203/session.v4i01.2025.08-15Keywords:
klasifikasi, Analisa Sentimen, kinerja dosen, support vector machine, random forest, naive bayesAbstract
Dalam era digital, data teks yang berasal dari ulasan, komentar, dan feedback online mahasiswa dapat menjadi sumber informasi berharga untuk memahami persepsi positif dan negatif terhadap kinerja dosen. Penelitian ini bertujuan untuk melakukan komparasi algoritma klasifikasi untuk analisis sentimen opini atau persepsi mahasiswa terhadap kinerja dosen di pendidikan tinggi. Dalam penelitian ini, dilakukan perbandingan tiga algoritma klasifikasi yaitu Naïve Bayes, Support Vector Machine, dan Random Forest. Datasets yang digunakan berupa ulasan mahasiswa terhadap kinerja dosen dari berbagai mata kuliah dan dosen. Datasets terdiri dari 1254 data komentar kritik saran mahasiswa. Data tersebut terdiri dari 839 komentar positif dan 415 komentar negatif. Selanjutnya dilakukan klasifikasi menggunakan algoritma Naïve Bayes, Support Vector Machine, serta Random Forest. Dari hasil klasifikasi diketahui bahwa algoritma Support Vector Machine memberikan hasil yang paling baik kemudian disusul dengan algoritma Random Forest dan yang terakhir algoritma Naïve Bayes. Diketahui Algoritma Support Vector Machine berhasil mendapatkan nilai accuracy sebesar 82,24%, dengan nilai precission sebesar 84,66%, nilai recall sebesar 65,99% dan nilai F1 score sebesar 79,81%.
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