Pemilihan Model Arsitektur Terbaik dengan metode backpropagation Dalam Menganalis Produksi Perikanan Laut
-
Ranjani
*
STIKOM Tunas Bangsa, Pematangsiantar, Indonesia,
Indonesia
- Suci Cahaya Mita STIKOM Tunas Bangsa, Pematangsiantar, Indonesia, Indonesia
- Indah Anggriyani STIKOM Tunas Bangsa, Pematangsiantar, Indonesia, Indonesia
- Poningsih STIKOM Tunas Bangsa, Pematangsiantar, Indonesia, Indonesia
Abstract
The purpose of this study is the use of artificial intelligence to analyze the develop[pment of fishery production at TPI (Fish Auction Places) in Indonesia. Indonesia Logistic center throught the website. By using the Backpropagation method is a supervised artificial neural network where this method can assess the errors of each neuron after a set of data is processed. Tis also the goal of modifying the weights to train the neural network so that it can involve arbitrary input to output correctly. This research has several stages, namely training and testing wich will then be converted to several models to get the best results where the results found will later become the conclusions of the research.
References
E. Rumapea, “Analisis Jaringan Syaraf Tiruan Dalam Memprediksi Data Ekspor Ikan Tongkol / Tuna Menurut Negara Tujuan Utama,” pp. 232–235, 2020.
A. Azis, “Identifikasi Jenis Ikan Menggunakan Model Hybrid Deep Learning Dan Algoritma Klasifikasi,” Sebatik, vol. 24, no. 2, pp. 201–206, 2020, doi: 10.46984/sebatik.v24i2.1057.
H. Honainah, F. F. Romadhoni, and A. Ato’illah, “Klasifikasi Kesegaran Ikan Tongkol Berdasarkan Warna Mata Menggunakan Metode Backpropagation,” J. Penelit. Inov., vol. 2, no. 2, pp. 405–414, 2022, doi: 10.54082/jupin.90.
H. U. Sari, A. P. Windarto, and D. Hartama, “Analisa Metode Data Mining Pada Produksi Perikanan Laut Yang Dijual Di Tempat Perikanan Ikan (Tpi),” KOMIK (Konferensi Nas. Teknol. Inf. dan Komputer), vol. 3, no. 1, pp. 630–636, 2019, doi: 10.30865/komik.v3i1.1671.
Z. I. Ali, I. M. Nur, and F. Fauzi, “Artificial Neural Network Untuk Memprediksi Curah Hujan Di Kota Padang Dengan Metode Backpropagation dan Adaline,” pp. 1–7, 2020.
N. Nurhidayana, I. S. Damanik, and R. Dewi, “Jaringan Saraf Tiruan Backpropagation Untuk Memprediksi Jumlah Ekspor Buah-Buahan Menurut Negara Tujuan,” J. Inf. Syst. Res., vol. 2, no. 2, pp. 163–168, 2021.
A. Anisa, B. S. Ginting, and R. Buaton, “Jaringan Syaraf Tiruan Memprediksi Pernikahan Di Kementerian Agama Kota Binjai Dengan Menggunakan Metode Backpropagation,” JTIK (Jurnal Tek. …, vol. 6, no. 2, pp. 466–477, 2022, [Online]. Available: http://jurnal.kaputama.ac.id/index.php/JTIK/article/view/853
Z. Zulfikar, A. Wanto, and Z. M. Nasution, “Analisis dalam Melihat Perkembangan Indeks Harga Perdagangan Besar Menurut Sektor di Indonesia Menggunakan Algoritma Backpropagation,” Pros. Semin. Nas. Ris. Inf. Sci., vol. 1, no. September, p. 359, 2019, doi: 10.30645/senaris.v1i0.41.
B. S. Mózo, “Transaksi,” J. Chem. Inf. Model., vol. 53, no. 9, pp. 1689–1699, 2017, [Online]. Available: file:///C:/Users/User/Downloads/fvm939e.pdf
M. Fajar, Sumarno, and I. Gunawan, “Penerapan Jaringan Syaraf Tiruan Dengan Metode Backpropagation Untuk Memprediksi Penjualan Sepeda Motor Yamaha Di Asli Motor Siantar,” KLIK Kaji. Ilm. Inform. dan Komput., vol. 1, no. 4, pp. 180–186, 2021, [Online]. Available: http://djournals.com/klik/article/view/151
E. Adriono, M. Somantri, and C. A. Suryono, “Model Prediksi Jumlah Pakan menggunakan Algoritma Evolusi Pikiran - Jaringan Syaraf Tiruan Rambatan Balik untuk Budidaya Udang,” J. Kelaut. Trop., vol. 25, no. 2, pp. 266–278, 2022, doi: 10.14710/jkt.v25i2.14256.
N. L. Br Sitepu, “Jaringan Saraf Tiruan Memprediksi Nilai Pemelajaran Siswa Dengan Metode Backpropagation ( Studi kasus : SMP Negeri 1 Salapian),” J. Inf. Technol., vol. 1, no. 2, pp. 54–58, 2021, doi: 10.32938/jitu.v1i2.1006.
A. A. Mahmudi, “Optimasi Conjugate Gradient Pada Backpropagation Neural Network Untuk Prediksi Hasil Tangkap Ikan,” Saintekbu, vol. 12, no. 2, pp. 29–39, 2020, doi: 10.32764/saintekbu.v12i2.1031.
F. Zola, “Jaringan Syaraf Tiruan Menggunakan Algoritma Backpropagation Untuk Memprediksi Prestasi Siswa,” J. Teknol. Dan Open Source, vol. 1, no. 1, pp. 58–72, 2018, doi: 10.36378/jtos.v1i1.12.
Copyright (c) 2022 Ranjani, Suci Cahaya Mita, Indah Anggriyani, P Poningsih
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
.png)
.png)
