Penggunaan Jaringan Saraf Tiruan untuk Memperkirakan Tenaga Kerja Berdasarkan Kategori Industri
- Dhini Ariani STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
- Farah Yusni Saragih STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
- Hazha Hikmah Asyifah STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
-
Alisa Putri Amanda Nasution
*
STIKOM Tunas Bangsa, Pematangsiantar,
Indonesia
- Putrama Alkhairi STIKOM Tunas Bangsa, Pematangsiantar, Indonesia (*) Corresponding Author
Abstract
Industrial growth can affect labor mobility both geographically and in terms of professional qualifications, large industries have a strategic role as creators of added value and important job providers in the Region. As an important part of industrial production, it cannot be separated from the demand for labor, but if viewed macro, it can be concluded that the quality of work determines or greatly influences the results of labor productivity itself. The industrial sector plays a significant role in economic growth, because it absorbs labor. Labor growth is much greater than the availability of jobs, thus causing other new problems, namely high unemployment. This study uses the Backpropagation Method to classify special patterns, which reduces the error rate by adjusting the weight based on the difference between output and the desired target. The results of this study are predictions of the level of truth of the Number of Large and Medium Industrial Workers according to Industry Group. Using 5 models, namely 10-10-1, 10-45-1, 10-45-10-1, 10-75-10-1, and 10-100-75-1. From 5 architecture models, 1 best model is produced, namely the 10-75-10-1 model with an accuracy rate of 70% and the smallest epoch with a total of 383.
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References
S. P. Siregar and A. Wanto, “Analysis of Artificial Neural Network Accuracy Using Backpropagation Algorithm In Predicting Process (Forecasting),” IJISTECH (International J. Inf. Syst. Technol., vol. 1, no. 1, p. 34, 2017, doi: 10.30645/ijistech.v1i1.4.
P. Tenaga, K. Di, K. Surabaya, A. A. Ningtyas, and M. Yasin, “Multidisciplinary Science Analisis Industri Besar Dan Sedang Terhadap,” vol. 1, no. 6, pp. 371–377, 2024.
R. D. Zakaria, “Analisis Produktivitas Tenaga Kerja Sektor Industri Besar Dan Sedang Daerah Kabupaten Dan Kota Provinsi Jawa Timur 2015-2019,” J. Ilmu Ekon. JIE, vol. 6, no. 1, pp. 156–167, 2022, doi: 10.22219/jie.v6i1.20343.
N. Soca and N. Woyanti, “Pengaruh Unit Usaha , Nilai Output , Biaya Input , Dan Upah Minimum Terhadap Penyerapan Tenaga Kerja,” Bussiness Econ. Entrep., vol. 4, no. 2, pp. 27–37, 2021.
M. A. R. Pratama and M. Arif, “Analisis Penyerapan Tenaga Kerja Industri Sedang dan Besar di Kabupaten Jepara Tahun 2001-2022,” J. EMT KITA, vol. 8, no. 1, pp. 347–354, 2024, doi: 10.35870/emt.v8i1.2096.
M. Syafiq, D. Hartama, I. O. Kirana, I. Gunawan, and A. Wanto, “Prediksi Jumlah Penjualan Produk di PT Ramayana Pematangsiantar Menggunakan Metode JST Backpropagation,” JURIKOM (Jurnal Ris. Komputer), vol. 7, no. 1, p. 175, 2020, doi: 10.30865/jurikom.v7i1.1963.
B. Salsabila and I. Cholissodin, “Prediksi Permintaan Keripik Buah dengan Metode Jaringan Syaraf Tiruan Backpropagation (Studi Kasus: CV. Arjuna 999),” vol. 4, no. 6, pp. 1667–1674, 2020.
F. Sonata, J. Prayudha, J. Hutagalung, and I. Sipahutar, “Jaringan Saraf Tiruan untuk Memprediksi Jumlah Mahasiswa dengan Metode Perceptron (Studi Kasus : STMIK Triguna Dharma),” J. SAINTIKOM (Jurnal Sains Manaj. Inform. dan Komputer), vol. 22, no. 2, p. 474, 2023, doi: 10.53513/jis.v22i2.9144.
H. U. Sari, A. P. Windarto, and I. S. Damanik, “Analisis Jaringan Saraf Tiruan dengan Backpropagation pada korelasi Matakuliah Pratikum Terhadap Tugas Akhir,” JURIKOM (Jurnal Ris. Komputer), vol. 9, no. 1, p. 115, 2022, doi: 10.30865/jurikom.v9i1.3835.
Muhammad Kurniawansyah, Rafiqotul Husna, Raichan Septiono, Agus Perdana Windarto, and Putrama Alkhairi, “Analisa Metode Backpropagation Dalam Memprediksi Jumlah Perusahaan Konstruksi Berdasarkan Provinsi di Indonesia,” J. Comput. Informatics Res., vol. 3, no. 1, pp. 164–172, 2023, doi: 10.47065/comforch.v3i1.993.
P. Alkhairi, E. R. Batubara, R. Rosnelly, W. Wanayaumini, and H. S. Tambunan, “Effect of Gradient Descent With Momentum Backpropagation Training Function in Detecting Alphabet Letters,” Sinkron, vol. 8, no. 1, pp. 574–583, 2023, doi: 10.33395/sinkron.v8i1.12183.
Putrama Alkhairi and A. P. Windarto, “Classification Analysis of Back propagation-Optimized CNN Performance in Image Processing,” J. Syst. Eng. Inf. Technol., vol. 2, no. 1, pp. 8–15, 2023, doi: 10.29207/joseit.v2i1.5015.
P. Alkhairi and B. H. Hayadi, “Penerapan Algoritma Backpropagation Untuk Mengenali Pola Tulisan Angka Dengan Fungsi Pelatihan Gradient Descent With Momentum Adaptive Lr,” 2022.
B. Satria, “Prediksi Volume Penggunaan Air PDAM Menggunakan Metode Jaringan Syaraf Tiruan Backpropagation,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 2, no. 3, pp. 674–684, 2018, doi: 10.29207/resti.v2i3.575.
T. W. Khusniyah and S. Sutikno, “Prediksi Nilai Tukar Petani Menggunakan Jaringan Syaraf Tiruan Backpropagation,” Sci. J. Informatics, vol. 3, no. 1, pp. 11–18, 2016, doi: 10.15294/sji.v3i1.4970.
E. P. Cynthia and E. Ismanto, “Memprediksi Ketersediaan Komoditi Pangan Provinsi Riau,” J. Teknol. Dan Sist. Inf. Univrab, vol. 2, no. 2, pp. 196–209, 2017.
Y. Apriyani, “Penerapan Jaringan Syaraf Tiruan Backpropagation Untuk Prediksi Nilai UN Siswa SMPN 2 Cihaurbeuti,” IJCIT (Indonesian J. Comput. Inf. Technol., vol. 3, no. 1, pp. 63–70, 2018.
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Copyright (c) 2024 Dhini Ariani, Farah Yusni Saragih, Hazha Hikmah Asyifah, Alisa Putri Amanda Nasution, Putrama Alkhairi
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