Komparasi Kinerja Support Vector Machine dan Naive Bayes Teroptimasi pada Sentimen Ulasan Blu by BCA

Fathony Mursyid; Marcellino Andelta Pinem; Abraham Imanuel Sinaga; Haichal Ryan Saputra; Eldika Rubiana; Fuad Nur Hasan

doi:10.55338/jumin.v6i6.7665

Authors

Fathony Mursyid, Universitas Bina Sarana Informatika, Indonesia
Marcellino Andelta Pinem, Universitas Bina Sarana Informatika, Indonesia
Abraham Imanuel Sinaga, Universitas Bina Sarana Informatika, Indonesia
Haichal Ryan Saputra, Universitas Bina Sarana Informatika, Indonesia
Eldika Rubiana, Universitas Bina Sarana Informatika, Indonesia
Fuad Nur Hasan, Universitas Bina Sarana Informatika, Indonesia

DOI:

https://doi.org/10.55338/jumin.v6i6.7665

Keywords:

Analisis Sentimen,, Support Vector Machine,, Naive Bayes,, Particle Swarm Optimization, Fintech.

Abstract

− Pertumbuhan pesat aplikasi perbankan digital Blu by BCA Digital menghasilkan volume ulasan pengguna yang besar dan tidak terstruktur. Penelitian ini bertujuan untuk menentukan model klasifikasi sentimen biner yang optimal dengan membandingkan kinerja algoritma Support Vector Machine (SVM) dan Naive Bayes (NB). Kedua algoritma dioptimasi menggunakan Particle Swarm Optimization (PSO) untuk memperoleh hiperparameter terbaik. Optimasi ini dilakukan guna meningkatkan performa klasifikasi pada data teks berbahasa Indonesia yang bersifat informal. Evaluasi model dilakukan menggunakan metrik Akurasi dan F1-Score. Hasil pengujian menunjukkan bahwa model PSO-SVM memiliki kinerja yang lebih unggul dengan akurasi 86,01% dan F1-Score (Negatif) 76,66%. Sebagai perbandingan, model PSO-NB menghasilkan akurasi 85,03% dan F1-Score (Negatif) 74,70%. Berdasarkan hasil tersebut, PSO-SVM direkomendasikan sebagai model yang paling andal untuk sistem pemantauan sentimen otomatis, khususnya dalam mendeteksi keluhan pelanggan pada industri Fintech

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