Klasifikasi Sentimen iPhone Bekas di Tokopedia menggunakan Naïve Bayes dan Support Vector Machine
DOI:
https://doi.org/10.22441/fifo.2025.v17i2.010Keywords:
Analisis Sentimen, iPhone Bekas, Tokopedia, Naive Bayes, Support Vector MachineAbstract
Kenaikan harga iPhone baru mendorong meningkatnya pembelian iPhone second di platform e-commerce seperti Tokopedia. Namun, konsumen masih menghadapi berbagai risiko terkait kondisi perangkat, performa komponen, dan keaslian yang umumnya teridentifikasi melalui ulasan pengguna. Penelitian ini bertujuan menganalisis sentimen dari 1.863 ulasan iPhone second untuk memperoleh gambaran objektif mengenai pengalaman konsumen. Teks ulasan diproses menggunakan TF-IDF sebagai representasi fitur dan SMOTE untuk mengatasi ketidakseimbangan kelas. Dua algoritma Naive Bayes dan Support Vector Machine (SVM) dibandingkan untuk menilai efektivitas klasifikasi. Hasil pengujian menunjukkan bahwa SVM memberikan performa terbaik dengan akurasi 96%, melampaui Naive Bayes yang mencapai 93%. Analisis lebih lanjut menemukan bahwa ulasan positif umumnya berkaitan dengan kualitas fisik dan kecepatan pengiriman, sedangkan ulasan negatif banyak menyoroti isu teknis serta keaslian perangkat. Penelitian ini berkontribusi pada penguatan literatur analisis sentimen e-commerce melalui evaluasi komprehensif terhadap kombinasi TF-IDF + SMOTE serta perbandingan performa Naive Bayes dan SVM dalam klasifikasi opini konsumen. Temuan ini menyediakan dasar empiris untuk penelitian lanjutan mengenai penilaian kualitas produk bekas berbasis ulasan daring.
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