Klasifikasi Ulasan Pelanggan pada Aplikasi MitraShopee Menggunakan Metode Support Vector Machine dan Naïve Bayes
https://doi.org/10.26594/teknologi.v15i1.5577
Abstract
Penelitian ini bertujuan untuk menghasilkan ulasan pelanggan aplikasi MitraSHopee ke dalam tiga kategori, yaitu, kritik, saran, dan pertanyaan, menggunakan algoritma Naïve Bayes dan Support Vector Machine (SVM). Sebanyak 6.000 ulasan diambil dari Google Play Store dan diproses melalui tahapan pembersihan data seperti case folding, tokenizing, stopword removal, dan normalisasi menggunakan Python di Google Colab. Data yang telah dibersihkan kemudian dianalisis dengan TF-IDF dan dilabeli berdasarkan pendekatan berbasis atiran. Hasil evaluasi menujukkan bahwa algoritma SVM memiliki performa lebih unggul dengan akurasi sebesar 81%, sedangkan Naibe Bayes mencatatkan akurasi 56%. SVM menunjukkan keseimbangan yang baik antara precision dan recall, khususnya dalam mendeteksi ulasan saran dan pertanyaan. Sementara itu, Naïve Bayes cenderung bias terhadap satu kelas dan kurang mampu mengenali kritik dengan baik. Penelitian ini menunjukkan pentingnya pemilihan algoritma dan tahap preprocessing dalam klasifikasi teks. Hasil yang diperoleh diharapkan dapat membantu pengembang aplikasi dalam memahami kebutuhan pengguna secara lebih cepat dan tepat sasaran melalui system klasifikasi otomatis.
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