Analisis Penerapan Neural Network dalam Memprediksi Produksi Bijih Nikel di Indonesia

Muhammad Edya Rosadi(1*), Dian Agustini(2), Muthia Farida(3), Dila Dwi Anjani(4),

(1) Universitas Islam Kalimantan Muhammad Arsyad Al Banjari Banjarmasin, Indonesia
(2) Universitas Islam Kalimantan Muhammad Arsyad Al Banjari Banjarmasin, Indonesia
(3) Universitas Islam Kalimantan Muhammad Arsyad Al Banjari Banjarmasin, Indonesia
(4) STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
(*) Corresponding Author

Abstract


Nickel ore is one of the exports from the mining subsector. About 72% of the world's nickel resources are found in lateritic nickel deposits, with approximately 15.8% of these deposits located in Indonesia. Nickel is currently one of the most discussed subjects in the world. As an essential component in the creation of batteries for electric vehicles, nickel is pushing changes in energy consumption. Managing nickel ore output in Indonesia is prudent in light of the government's efforts to increase national development, investment, employment, mining downstream, and export demands. To satisfy domestic and international demand, it is essential to examine nickel ore output. Consequently, an investigation is required to forecast nickel ore production. The dataset utilized is from the Central Bureau of Statistics's Publication of Non-Oil and Gas Mining Statistics for 2017-2020. This study employs a backpropagation network with an artificial neural network. The procedure is carried out by separating training data and testing data to choose the most accurate architectural model, which is subsequently utilized as a predictive model. The architectural models to be utilized with Matlab 6.1 are 2-45-1; 2-60-1; 2-75-80-1; 2-85-1; and 2-100-1. From a series of tests, it was determined that the best architectural model was 2-45-1 with a Mean Square Error of 0.00099549, epoch 335, and an accuracy of one hundred percent. This model was then utilized to create predictions

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References


S. Alfarisi, “Sistem Prediksi Penjualan Gamis Toko QITAZ Menggunakan Metode Single Exponential Smoothing,” JABE (Journal Appl. Bus. Econ., vol. 4, no. 1, p. 80, 2017, doi: 10.30998/jabe.v4i1.1908.

J. Andriano Frans, M. Orisa, and S. Adi Wibowo, “Prediksi Penjualan Kayu Lapis Di Cv Diato Wood Sejahtera Dengan Metode Trend Moment Berbasis Web,” JATI (Jurnal Mhs. Tek. Inform., vol. 4, no. 2, pp. 183–190, 2020, doi: 10.36040/jati.v4i2.2719.

A. Revi, S. Solikhun, and M. Safii, “Jaringan Saraf Tiruan Dalam Memprediksi Jumlah Produksi Daging Sapi Berdasarkan Provinsi,” KOMIK (Konferensi Nas. Teknol. Inf. dan Komputer), vol. 2, no. 1, pp. 297–304, 2018, doi: 10.30865/komik.v2i1.941.

K. F. Irnanda, A. P. Windarto, and I. S. Damanik, “Optimasi Particle Swarm Optimization Pada Peningkatan Prediksi dengan Metode Backpropagation Menggunakan Software RapidMiner,” J. Ris. Komput., vol. 9, no. 1, pp. 122–130, 2022, doi: 10.30865/jurikom.v9i1.3836.

Z. Fitri, “Analisis Error dan Epoch dengan Pengembangan Adaptive Learning Rate dan Parameter Momentum pada Metode Backpropagation,” vol. 3, no. 2, 2018.

P. Penjualan, S. Menggunakan, B. Neural, N. Dan, and R. Neural, “Backpropagation Neural Network Dan Recurrent Neural,” vol. 9, no. 1, pp. 6–21, 2020.

S. Wahyuni, “Jaringan Saraf Tiruan Memprediksi Kendaraan Masuk Pada Pengujian Kir Menggunakan Metode Backpropagation (Studi Kasus: Dinas Perhubungan Kota Binjai),” Semin. Nas. Inform. …, 2021, [Online]. Available: https://www.ejournal.pelitaindonesia.ac.id/ojs32/index.php/SENATIKA/article/view/1147

M. Khairani, “Improvisasi Backpropagation menggunakan penerapan adaptive learning rate dan parallel training,” TECHSI - J. Penelit. Tek. Inform., vol. 4, no. 1, pp. 157–172, 2014.

T. Brian, “Analisis Learning Rates Pada Algoritma Backpropagation Untuk Klasifikasi Penyakit Diabetes,” Edutic - Sci. J. Informatics Educ., vol. 3, no. 1, pp. 21–27, 2017, doi: 10.21107/edutic.v3i1.2557.

F. Jefansa, “Penerapan Jaringan Saraf Tiruan dalam Meramalkan Produksi Kopi Berdasarkan Provinsi,” vol. 7, no. 1, pp. 1–7, 2022.

A. P. Windarto, M. R. Lubis, and S. Solikhun, “Implementasi JST pada Prediksi Total Laba Rugi Komprehensif Bank Umum dan Konvensional dengan Backpropagation,” J. Teknol. Inf. dan Ilmu Komput., vol. 5, no. 4, p. 411, 2018, doi: 10.25126/jtiik.201854767.

M. D. Wuryandari and I. Afrianto, “Perbandingan Metode Jaringan Saraf Tiruan Backpropagation Dan Learning Vector Quantization Pada Pengenalan Wajah,” Komputa, vol. 1, no. 1, pp. 45–51, 2012.

A. P. Windarto, “Implementasi Jst Dalam Menentukan Kelayakan Nasabah Pinjaman Kur Pada Bank Mandiri Mikro Serbelawan Dengan Metode Backpropogation,” J-SAKTI (Jurnal Sains Komput. dan Inform., vol. 1, no. 1, pp. 12–23, 2017.

S. H. Putri, Y. Yuhandri, and G. W. Nurcahyo, “Prediksi Pencapaian Target Peserta Keluarga Berencana Pasca Persalinan menggunakan Algoritma Backpropagation,” J. Sistim Inf. dan Teknol., vol. 3, pp. 176–182, 2021, doi: 10.37034/jsisfotek.v3i3.62.

R. M. Firzatullah, “Menggunakan Sistem Pendukung Keputusan Penentuan Uang Kuliah Tunggal Universitas XYZ Menggunakan Algoritma Backpropagation,” Petir, vol. 14, no. 2, pp. 170–180, 2021, doi: 10.33322/petir.v14i2.996.

A. Zulhamsyah, S. Saifullah, and M. R. Lubis, “Penerapan Backpropagation Dalam Memprediksi Produksi Kelapa Sawit Unit Kebun Marjandi,” KOMIK (Konferensi Nas. Teknol. Inf. dan Komputer), vol. 3, no. 1, pp. 779–787, 2019, doi: 10.30865/komik.v3i1.1693.

G. Guntoro, L. Costaner, and L. Lisnawita, “Prediksi Jumlah Kendaraan di Provinsi Riau Menggunakan Metode Backpropagation,” Inform. Mulawarman J. Ilm. Ilmu Komput., vol. 14, no. 1, p. 50, 2019, doi: 10.30872/jim.v14i1.1745.

S. Irwanda, J. T. Hardinata, and I. S. Damanik, “Jaringan Saraf Tiruan Backpropogation dalam Memprediksi Jumlah Tilang di Kejaksaan Negeri Simalungun,” Pros. Semin. Nas. Ris. Inf. Sci., vol. 1, no. September, p. 697, 2019, doi: 10.30645/senaris.v1i0.76.

A. Wanto and A. P. Windarto, “Analisis Prediksi Indeks Harga Konsumen Berdasarkan Kelompok Kesehatan Dengan Menggunakan Metode Backpropagation,” J. Penelit. Tek. Inform. Sink., vol. 2, no. 2, pp. 37–43, 2017, [Online]. Available: https://zenodo.org/record/1009223#.Wd7norlTbhQ




DOI: https://doi.org/10.30645/brahmana.v4i1.108

DOI (PDF): https://doi.org/10.30645/brahmana.v4i1.108.g105

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