<![CDATA[AbstrakSistem Rekomendasi dapat merekomendasikan buku pada user tertentu berdasarkan prediksi rating, isikonten buku, ataupun metode lainnya. Banyak metode recommendation system yang digunakan sepertiProbabilistic Matrix Factorization, dimana konten yang sudah diberi rating akan seringdirekomendasikan. Namun pada Probabilistic Matrix Factorization memiliki kekurangan yaitu dalammengatasi data yang memiliki nilai rating yang jarang. Maka diperlukan suatu metode yang digunakanuntuk memahami konteks isi dari buku sehingga tidak hanya melihat dari rating saja namun dilihat jugadari review suatu buku. Untuk mempelajari review maka diigunakan suatu metode yaitu ConvolutionalNeural Network dengan cara memberikan suatu nilai vektor yang mengarah terhadap konteks buku kepada Probabilistic Matrix Factorization suatu recommender system. Berdasarkan hasil pengujiannya,metode tersebut dapat meningkatkan keakuratan data dengan MAE = 3,0114707. Sedangkan untukProbabilistic Matrix Factorization nilai MAE = 4,0185377. Dari nilai tersebut dapat dijelaskan bahwametode Convolutional Neural Network dan Probabilistic Matrix Factorization bekerja cukup baik untuk data yang jarang memiliki rating..Kata kunci : recommender system, Convolutional Neural Network, Probabilistic Matrix FactorizationAbstractThe Recommendation System can recommend books to certain users based on rating predictions, bookcontent, or other methods. Many system recommendation methods are used such as Probabilistic MatrixFactorization, where content that has been rated will often be recommended. However, the ProbabilisticMatrix Factorization has the disadvantage of overcoming data that has a rare rating value. So we need amethod used to understand the context of the contents of the book so that it is not only seen from therating but also seen from a book review. To study the review, a method called Convolutional NeuralNetwork is used by giving a vector value that leads to the context of the book to the Probabilistic MatrixFactorization of a recommender system. Based on the test results, this method can improve the accuracy ofthe data with MAE = 3.0114707. As for the Probabilistic Matrix Factorization the MAE= 4.0185377. Fromthese values it can be explained that the Convolutional Neural Network and Probabilistic MatrixFactorization methods work well enough for data that rarely has a rating.Keywords: Recommender system, Probabilistic Matrix Factorization, Convolutional Neural Network]]>
