Implementasi Deep Learning untuk Klasifikasi dan Prediksi Data pada Sistem Rekomendasi Berbasis Machine Learning
Kata Kunci:
Deep Learning, Machine Learning, Convolutional Neural Network, Sistem Rekomendasi, rekomendasi konten, Collaborative Filtering, Prediksi Data.Abstrak
Perkembangan pesat teknologi komputasi telah mendorong adopsi luas sistem rekomendasi berbasis machine learning dalam berbagai domain industri, namun masih terdapat tantangan pada sistem rekomendasi konten yang umumnya bersifat konvensional dan berbasis collaborative filtering sehingga membatasi akurasi dan relevansi rekomendasi. Penelitian ini bertujuan untuk mengimplementasikan Deep Learning (DL), khususnya arsitektur Convolutional Neural Network dan Recurrent Neural Network (CNN-RNN), dalam menganalisis pola data pengguna guna menghasilkan rekomendasi yang lebih akurat dan personal. Metode yang digunakan meliputi integrasi model deep learning untuk feature extraction (ekstraksi fitur) dan collaborative prediction (prediksi kolaboratif) agar sistem mampu memahami preferensi serta pola perilaku pengguna secara mendalam. Pengembangan sistem dilakukan menggunakan pendekatan Cross-Industry Standard Process for Data Mining (CRISP-DM) dan diimplementasikan dalam bentuk prototipe sistem rekomendasi, dilengkapi dengan evaluasi performa model menggunakan metrik (Mean Absolute Error) dan (Root Mean Square Error) pada arsitektur cloud computing. Hasil pengujian menunjukkan bahwa model deep learning mampu memprediksi preferensi pengguna dengan tingkat akurasi yang tinggi, serta proses pelatihan dan inferensi model dapat berjalan secara efisien dan stabil, sehingga sistem mampu memberikan rekomendasi yang relevan, personal, dan sesuai konteks kebutuhan pengguna. Dengan demikian, penerapan deep learning dalam analisis data terbukti efektif dalam mengatasi keterbatasan sistem rekomendasi konvensional, meningkatkan kepuasan pengguna, serta menciptakan pengalaman yang lebih adaptif dalam ekosistem digital.
Unduhan
Referensi
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