Prediksi Pembelian Berdasarkan Click Through Rate Iklan Digital Menggunakan Algoritma Random Forest
DOI:
https://doi.org/10.30736/jti.v10i2.1435Keywords:
Iklan Digital, Click Through Rate, Random forestAbstract
Perkembangan teknologi digital telah mengubah strategi pemasaran, menjadikan iklan digital sebagai sarana utama untuk menjangkau konsumen secara lebih tepat sasaran. Namun, keberhasilan kampanye iklan tidak hanya bergantung pada tingkat klik (Click Through Rate/CTR), melainkan juga pada kemampuan sistem dalam mengidentifikasi pengguna yang berpotensi melakukan pembelian. Penelitian ini bertujuan untuk membangun model prediksi perilaku pembelian berdasarkan CTR dengan algoritma Random forest dan pendekatan CRISP-DM. Dataset yang digunakan berasal dari Social Network Ads dan terdiri dari 400 entri dengan atribut demografis seperti usia, jenis kelamin, dan estimasi gaji. Model dibangun dalam dua tahap, yaitu baseline dan hasil tuning. Evaluasi dilakukan menggunakan metrik klasifikasi, dan model hasil tuning berhasil mencapai akurasi sebesar 93%, recall 98%, dan F1-score 92%, menunjukkan performa yang unggul dalam mengenali kelas pembelian. Hasil ini menunjukkan bahwa Random forest dengan tuning hyperparameter dan class weight dapat menjadi solusi yang efektif dalam klasifikasi pengguna iklan digital dan mendukung pengambilan keputusan pemasaran yang lebih efisien.
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