Knowledge Discovery Berdasarkan Analisis Sentimen Berbasis Aspek pada Ulasan Pengguna myBCA Menggunakan Machine Learning
Knowledge Discovery from Aspect-Based Sentiment Analysis of myBCA User Reviews Using Machine Learning
DOI:
https://doi.org/10.57152/malcom.v6i4.3182Keywords:
Analisis Sentimen Berbasis Aspek, Knowledge Discovery, Machine Learning, MyBCA, Ulasan PenggunaAbstract
Perkembangan ekosistem keuangan berbasis digital mendorong lonjakan ulasan pengguna pada kanal digital seperti Google Play Store, membuka peluang untuk menggali informasi lewat analisis sentimen. Penelitian ini bertujuan mengungkap sentimen pengguna myBCA pada empat aspek yaitu tampilan, fitur dan performa, layanan, serta keamanan, sekaligus membandingkan performa tiga algoritma klasifikasi machine learning. Analisis sentimen berbasis aspek diterapkan pada 8.365 ulasan aplikasi myBCA yang dikumpulkan secara otomatis dari Google Play Store. Setelah melalui tahap pengolahan teks (pembersihan, penyeragaman huruf, pemecahan kata, penyaringan kata umum, penyesuaian kata dasar), ulasan dikelompokkan ke dalam empat aspek menggunakan pencocokan kata kunci, dengan label sentimen ditentukan dari rating (1–3 negatif, 4–5 positif). Fitur direpresentasikan melalui TF-IDF dan diuji dengan Support Vector Machine, Naïve Bayes, dan Random Forest. Support Vector Machine mencatat kinerja terbaik (accuracy 0,85), diikuti Random Forest (0,84) dan Naïve Bayes (0,81). Aspek keamanan mencatat persentase sentimen negatif tertinggi (90,4%), sementara fitur dan performa mencatat volume keluhan terbanyak (2.662 ulasan), didominasi kendala login, gangguan transaksi, dan ketidakstabilan pasca-pembaruan. Temuan ini menjadi landasan bagi pengembang myBCA untuk memprioritaskan stabilitas transaksi dan keamanan dibandingkan penyempurnaan tampilan
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