Prediksi dan Klasifikasi Buku Menggunakan Metode Backpropagation

Abstract

Students of SMP Negeri 2 Lengayang have different interests in determining the books they are interested in, so that the library often has difficulty determining the books that are most entered by students, this is because they have not used the right system in determining the type and number of books, only based on the estimated number. Students and subjects only, as a result school students stock books of the books they want to borrow. Based on the above, a method is needed to predict and classify the amount of book stock in the future. The data used is a recap of monthly book lending, from 2018 to 2020 in the third month, with a total of 1653 transactions and 5 types of books processed, then the data is analyzed using the Backpropogation method. The results obtained are using a 5-3-1 pattern with a learning rate of 0.01, a goal of 0.01, the number of input units for the Weapon layer 5, the number of units in the hidden layer and the number of output layer units that are placed on 1 layer, and to carry out training using two phases namely feedforward and backpropagation phases. It is removed from this research that the backpropagation method can provide a classification prediction of the number of books that must be provided in the following year based on the number of data entered or the number of data entered.

Application of the Learning Vector Quantization Algorithm for Classification of Students with the Potential to Drop Out

“…90% of the training data can provide enough data to train a classification model. The more data used for training, the better the model will likely learn the patterns in that data (Rahmawati 2019). Meanwhile, the test data in this study used 768 data, as shown inTable 4.…”

“…This research aims to utilize student data at universities to classify student dropouts using the learning vector quantization (LVQ) algorithm. LVQ is one of the classification methods of artificial neural networks that applies the principle of competition (Rahmawati 2019). LVQ is a grouping method where the labels for each class have been determined (supervised learning).…”

Peramalan Jumlah Permintaan Produksi Menggunakan Jaringan Saraf Tiruan Algoritma Backpropagation

“…Jumlah epoch pelatihan adalah 4595 dan pencapaian MSE (Mean Squared Error) pada pengujian adalah 0,001 serta hasil akurasi yang didapatkan adalah 99,99900000% [11]. Selain itu juga penelitian tentang prediksi dan klasifikasi buku menggunakan metode backpropagation [12]. Penelitian lainnya yang melibatkan Algoritma Backpropagation adalah dalam menentukan klasifikasi 5 varietas padi melalui citra scan laser tiga dimensi (3D) [13], penentuan tingkat kematangan sawit pada 116 citra tandan buah segar [14].…”