Pengembangan Sistem Prediksi Harga Mobil Bekas Di Pasar India Menggunakan Algoritma XGBoost dan NLP
DOI:
https://doi.org/10.47701/w6511j19Keywords:
Prediksi Harga, Mobil Bekas, pasar India, XGBoost, NLP, Machine LearningAbstract
Perkembangan teknologi kecerdasan buatan telah membuka peluang baru dalam pengambilan keputusan berbasis data, termasuk dalam penentuan harga mobil bekas yang kompleks. Penelitian ini bertujuan mengembangkan sistem prediksi harga mobil bekas di pasar India menggunakan algoritma Extreme Gradient Boosting (XGBoost) yang dikombinasikan dengan Natural Language Processing (NLP) untuk menganalisis fitur tekstual detail kendaraan. Model dibangun menggunakan bahasa pemrograman Python dan diuji dalam lingkungan lokal. Hasil pengujian pada data yang belum pernah dilihat sebelumnya menunjukkan model terbaik yang dikembangkan berhasil mencapai tingkat akurasi dengan nilai R- squared (R2) sebesar 75.18%. Angka ini merepresentasikan kemampuan model dalam menjelaskan sebagian besar variasi harga. Secara praktis, rata-rata kesalahan absolut (Mean Absolute Error atau MAE) tercatat sebesar 87.071 Rupee India, yang membuktikan efektivitas pendekatan yang diusulkan. Sistem ini berpotensi untuk diintegrasikan ke dalam platform e- commerce otomotif sebagai fitur penentu harga otomatis.
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