Prediksi dan Klasifikasi Buku Menggunakan Metode Backpropagation
Article Sidebar
Main Article Content
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.
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
[2] Siahaan, A. T. A. A. (2018). Analisa Metode Perceptron Prediksi Penyediaan Buku Perpustakaan Sesuai Kebutuhan Mahasiswa (Studi Kasus di Universitas Islam Negeri Sumatera Utara Medan). Algoritma : Jurnal Ilmu Komputer dan Informatika, 2(2).
[3] Rahayu, R. Wihandika, R. C. & Perdana, R. S. (2018). Implementasi Metode Backpropagation Untuk Klasifikasi Kenaikan Harga Minyak Kelapa Sawit. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2(4) 1547–1552.
[4] Ismanto, E., Effendi, N., & Cynthia, E. P. (2018). Implementation of Backpropagation Artificial Neural Networks to Predict Palm Oil Price Fresh Fruit Bunches. IJISTECH (International Journal of Information System & Technology), 2(1), 26. DOI: https://doi.org/10.30645/ijistech.v2i1.17 .
[5] Yessa, A. R., & Hardjianto, M. (2020). Prediction of Water Use Using Backpropagation Neural Network Method and Particle Swarm Optimization. Bit-Tech 2(3).
[6] Putri, D. A., Hananto, B., Afrizal, S., & Pangaribuan, A. B. (2019). Prediksi Program Studi Berdasarkan Nilai Siswa dengan Algoritma Backpropagation (Studi Kasus SMAN 6 Depok Jurusan IPS). Informatik Jurnal Ilmu Komputer, 15(2).
[7] Windarto, A. P., Lubis, M. R., & Solikhun, S. (2018). Model Arsitektur Neural Network dengan Backpropogation Pada Prediksi Total Laba Rugi Komprehensif Bank Umum Konvensional. Klik Jurnal Ilmiah Kumpulan Jurnal Ilmu Komputer, 5(2), 147. DOI: https://doi.org/10.20527/klik.v5i2.148
[8] Norhikmah, N., & Rumini, R. (2020). Klasifikasi Peminjaman Buku Menggunakan Neural Network Backpropagation. Jurnal SISTEMASI, 9(1). DOI: https://doi.org/10.32520/stmsi.v9i1.562 .
[9] Yalidhan, M. D. (2018). Implementasi Algoritma Backpropagation Untuk Memprediksi Kelulusan Mahasiswa. Jurnal Ilmiah Klik Kumpulan Jurnal Ilmu Komputer, 5(2). DOI: https://doi.org/10.20527/klik.v5i2.152 .
[10] Setti, S., Wanto, A., Syafiq, M., Andriano, A., & Sihotang, B. K. (2019). Analysis of Backpropagation Algorithms In Predicting World Internet Users. Journal of Physics: Conference Series, 1255. DOI: https://doi.org/10.1088/1742-6596/1255/1/012018 .
[11] Saragih, J. R., Saragih, M. B. S., & Wanto, A. (2018). Analisis Algoritma Backpropagation dalam Prediksi Nilai Ekspor (Juta USD). Jurnal Pendidikan Teknologi dan Kejuruan, 15(2). https://doi.org/10.23887/jptk-undiksha.v15i2.14362 .
[12] Syofneri, N., Defit, S., & Sumijan. (2019). Implementasi Metode Backpropagation untuk Memprediksi Tingkat Kelulusan Uji Kopetensi Siswa. Jurnal Informasi dan Teknologi, 1(4), 12-17. https://doi.org/10.37034/jidt.v1i4.13 .
[13] Siregar, S. P., & Wanto, A. (2017). Analysis of Artificial Neural Network Accuracy Using Backpropagation Algorithm In Predicting Process (Forecasting). IJISTECH (International Journal Of Information System & Technology), 1(1), 34. DOI: https://doi.org/10.30645/ijistech.v1i1.4 .
[14] Hasan, N. F., Kusrini, K., & Fatta, H. A. (2019). Analisis Arsitektur Jaringan Syaraf Tiruan Untuk Peramalan Penjualan air Minum dalam Kemasan. Jurnal Rekayasa Teknologi Informasi (JURTI), 3(1). DOI: https://doi.org/10.30872/jurti.v3i1.2290 .
[15] Sovia, R., Yanto, M., & Melati, P. (2020). Prediksi Jumlah Kunjungan Wisata Mancanegara dengan Algoritma Backpropagation. Jurnal Media Informasi Budidarma, 4(2). DOI: http://doi.org/10.30865/mib.v4i2.2048 .