Komparasi Pengaruh Model Klasifikasi Naive Bayes dan Support Vector Machine Pada Analisis Data Sentimen  Di Bidang Pendidikan

Riri Fajriah; Denni Kurniawan

doi:10.30998/faktorexacta.v17i2.22342

Komparasi Pengaruh Model Klasifikasi Naive Bayes dan Support Vector Machine Pada Analisis Data Sentimen Di Bidang Pendidikan

Riri Fajriah^(1*), Denni Kurniawan⁽²⁾

(1) Universitas Budi Luhur
(2) Universitas Budi Luhur
(*) Corresponding Author

Abstract

Penerapan data mining dalam text mining processing banyak dimanfaatkan dalam penelitian analisis sentimen. Beberapa penelitian analisis sentimen menggunakan model klasifikasi supervised machine learning seperti Naive Bayes dan Support Vector Machine. Tujuan penelitian adalah mengevaluasi bagaimana pengaruh model klasifikasi Naive Bayes dan Support Vector Machine pada analisis sentimen, khususnya dibidang pendidikan. Beberapa penelitian terdahulu banyak mengambil objek penelitian analisis sentimen pada bidang pemasaran, sosial, ekonomi, politik, sehingga analisa penelitian akan membantu memberikan strategi pengembangan penelitian analisis sentimen dibidang pendidikan. Pada bidang bidang pendidikan sumber data yang digunakan misalnya dari opini siswa dan guru terkait capaian pembelajaran. Hasil penelitian menunjukkan model klasifikasi Naive Bayes dan Support Vector Machine dapat memberikan nilai akurasi yang baik dalam penelitian analisis sentimen, namun penggabungan kedua model dengan pendekatan ensemble lebih meningkatkan capaian akurasi. Untuk penelitian anaisis sentimen dibidang pendidikan ada beberapa faktor penting yang perlu diperhatikan seperti kontribusi penelitian, metode implementasi data mining, parameter yang mempengaruhi, evaluasi data dan resiko kegagalan. Semua faktor tersebut diharapkan dapat diperhatikan sebagai conceptual framework yang akan mendukung keberhasilan dalam penelitian analisis sentimen di bidang pendidikan bagi penelitian yang dilakukan di masa mendatang.

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DOI: http://dx.doi.org/10.30998/faktorexacta.v17i2.22342