Analysis of Open Unemployment Rate Prediction Using Backpropagation Method

Penulis

  • P.A.M. Zidane R.W.P.P. Zer STIKOM Tunas Bangsa
  • Dimas Prayogi STIKOM Tunas Bangsa
  • M Arif Y Sinaga STIKOM Tunas Bangsa
  • Olivia Diwani Saragih STIKOM Tunas Bangsa
  • Victor Asido Elyakim P STIKOM Tunas Bangsa

DOI:

https://doi.org/10.55123/jomlai.v4i2.5957

Kata Kunci:

Optimization , ANN , Prediction , Machine Learning , Covid-19

Abstrak

The open unemployment rate (TPT) is one of the important indicators in assessing the economic health of a region. This study aims to develop an accurate prediction model for the open unemployment rate using the backpropagation algorithm, as well as to evaluate the factors that influence the prediction. Accurate TPT prediction can help the government and policy makers in designing strategies to alleviate unemployment based on the results of the analysis of the developed model. This study aims to analyze and predict the Open Unemployment Rate (TPT) in various provinces in Indonesia in 2024 to 2026 using an artificial neural network model with the Backpropagation algorithm. Based on the test results, the 3-6-1 architecture model showed a prediction ability with 100% accuracy, while other architectures also gave very good results, with 100% accuracy for the 3-3-1 model and 97.06% for the 3-12-1 model. The TPT prediction results show that the unemployment rate is predicted to continue to increase from year to year, indicating the potential for an increase in the number of unemployed in the future. On the other hand, the accuracy analysis shows that each architecture produces different results, with the 3-6-1 architecture producing a longer time for the testing process, but still providing optimal accuracy. This finding illustrates that choosing the right architecture greatly affects the accuracy and efficiency in predicting TPT, which can be an important basis in formulating policies to eradicate unemployment in Indonesia.

Referensi

M. R. Azhar and I. Gunawan, “Implementasi Algoritma Backpropagation Dalam Memprediksi Jumlah Penduduk Usia Produktif Pada Kota Pematangsiantar,” vol. 2, no. 2, pp. 199–209, 2021.

E. E. Prayogo, I. Indriati, and C. Dewi, “Klasifikasi Bidang Keunggulan Mahasiswa menggunakan Metode Backpropagation dan Seleksi Fitur Information Gain (Studi Kasus: Departemen Teknik Informatika …,” J. Pengemb. Teknol. …, vol. 7, no. 1, pp. 169–178, 2023.

L. Widyarsi and H. Usman, “Penggunaan Data Google Trends untuk Peramalan Tingkat Pengangguran Terbuka di Tingkat Nasional dan Regional di Provinsi Jawa Barat,” Semin. Nas. Off. Stat., vol. 2021, no. 1, pp. 980–990, 2021, doi: 10.34123/semnasoffstat.v2021i1.842.

Y. A. Lesnussa and E. Risamasu, “Aplikasi Jaringan Syaraf Tiruan Backpropagation Untuk Meramalkan Tingkat Pengangguran Terbuka (TPT) Di Provinsi Maluku,” Sainmatika J. Ilm. Mat. dan Ilmu Pengetah. Alam, vol. 17, no. 2, p. 89, 2020, doi: 10.31851/sainmatika.v17i2.3434.

W. Saputra, J. T. Hardinata, and A. Wanto, “Penerapan Metode Resilient dalam Menentukan Model Arsitektur Terbaik untuk Prediksi Pengangguran Terbuka di Indonesia,” Semin. Nas. Apl. Teknol. Inf., pp. 21–29, 2019.

M. Setiana, B. Rahayudi, and L. Muflikhah, “Metode Backpropogation untuk Memprediksi Tingkat Pengangguran Terbuka ( TPT ) di Provinsi Jawa Barat dengan Optimasi Adam,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 1, no. 1, pp. 1–10, 2017.

R. Yulistiani, N. C. Putra, Q. Said, and I. Ernawati, “Klasifikasi dan Prediksi Tingkat Pengangguran Terbuka di Indonesia Menggunakan Metode Classification and Regression Tree (CART),” Semin. Nas. Mhs. Ilmu Komput. dan Apl., pp. 123–130, 2020.

M. R. Siregar, “Prediksi Angka Harapan Lama Sekolah Provinsi Sumatra Utara Dengan Backpropagation,” J. JPILKOM (Jurnal Penelit. Ilmu Komputer), vol. 2, no. 1, 2024.

W. M. Rahmat, N. Hidayat, and A. A. Soebroto, “Prediksi Penjualan Ponsel Pintar menggunakan Metode Jaringan Syaraf Tiruan Backpropagation Kombinasi Particle Swarm Optimization,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 7, no. 1, pp. 439–445, 2023, [Online]. Available: http://j-ptiik.ub.ac.id

R. Dina, “Optimasi Backpropagation Neural Network Menggunakan Metode Algoritma Genetika Dalam Memprediksi Jumlah Pengangguran,” 2019.

A. Wanto, “Prediksi Produktivitas Jagung Di Indonesia Sebagai Upaya Antisipasi Impor Menggunakan Jaringan Saraf Tiruan Backpropagation,” SINTECH (Science Inf. Technol. J., vol. 2, no. 1, pp. 53–62, 2019, doi: 10.31598/sintechjournal.v2i1.355.

Y. F. Arifin, D. E. Ratnawati, and P. P. Adikara, “Implementasi Gabungan Metode Bayesian dan Backpropagation untuk Peramalan Jumlah Pengangguran Terbuka di Indonesia,” J. Pengemb. Teknol. Inf. Dan Ilmu Komput., vol. 1, no. 4, pp. 330–340, 2017.

G. Pratama, S. Adinugroho, and B. Rahayudi, “Penggunaan Fungsi Aktivasi Linier dan Logarithmic Normalization pada Metode Backpropagation untuk Peramalan Luas Kebakaran Hutan,” J. Pengemb. …, vol. 3, no. 12, pp. 11003–11008, 2020, [Online]. Available: http://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/6839

D. E. Ratnawati, D. P. Adwandhal, Y. F. Arifin, and Machsus, “Perbandingan Antara Metode Bayesian-Backpropagation dan Genetic -Backpropagation Pada Prediksi Jumlah Pengangguran Terbuka di Indonesia,” Pros. Semin. Nas. Penelit. Pengabdi. Pada Masy., 2016.

T. Kurniawan, R. Maulana, and E. Setiawan, “Sistem Pendeteksi Premature Ventricular Contraction Berdasarkan Fitur Geometri Segitiga Dan Amplitudo R Menggunakan Metode Jaringan Syaraf Tiruan,” … Inf. dan Ilmu Komput. e-ISSN, vol. 5, no. 6, pp. 2418–2425, 2021, [Online]. Available: http://j-ptiik.ub.ac.id

Unduhan

Diterbitkan

2025-06-20

Cara Mengutip

P.A.M. Zidane R.W.P.P. Zer, Dimas Prayogi, M Arif Y Sinaga, Olivia Diwani Saragih, & Victor Asido Elyakim P. (2025). Analysis of Open Unemployment Rate Prediction Using Backpropagation Method. JOMLAI: Journal of Machine Learning and Artificial Intelligence, 4(2), 61–68. https://doi.org/10.55123/jomlai.v4i2.5957

Terbitan

Vol 4 No 2 (2025): Juni 2025

Bagian

Articles

Lisensi

Hak Cipta (c) 2025 P.A.M. Zidane R.W.P.P. Zer, Dimas Prayogi, M Arif Y Sinaga, Olivia Diwani Saragih, Victor Asido Elyakim P

Creative Commons License

Artikel ini berlisensiCreative Commons Attribution-ShareAlike 4.0 International License.

Copyright (c) 2022 The authors. Published by Yayasan Literasi Indonesia
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License

The author(s) whose article is published in the JOMLAI journal attain the copyright for their article and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. By submitting the manuscript to JOMLAI, the author(s) agree with this policy. No special document approval is required.

The author(s) guarantee that:their article is original, written by the mentioned author(s),

  • has never been published before,
  • does not contain statements that violate the law, and
  • does not violate the rights of others, is subject to copyright held exclusively by the author(s), and is free from the rights of third parties, and that the necessary written permission to quote from other sources has been obtained by the author(s).

The author(s) retain all rights to the published work, such as (but not limited to) the following rights:

  • Copyright and other proprietary rights related to the article, such as patents,
  • The right to use the substance of the article in its own future works, including lectures and books,
  • The right to reproduce the article for its own purposes,
  • The right to archive all versions of the article in any repository, and
  • The right to enter into separate additional contractual arrangements for the non-exclusive distribution of published versions of the article (for example, posting them to institutional repositories or publishing them in a book), acknowledging its initial publication in this journal (JOMLAI: Journal of Machine Learning and Artificial Intelligence).

Suppose the article was prepared jointly by more than one author. Each author submitting the manuscript warrants that all co-authors have given their permission to agree to copyright and license notices (agreements) on their behalf and notify co-authors of the terms of this policy. JOMLAI will not be held responsible for anything that may arise because of the writer's internal dispute. JOMLAI will only communicate with correspondence authors.

Authors should also understand that their articles (and any additional files, including data sets, and analysis/computation data) will become publicly available once published. The license of published articles (and additional data) will be governed by a Creative Commons Attribution-ShareAlike 4.0 International License. JOMLAI allows users to copy, distribute, display and perform work under license. Users need to attribute the author(s) and JOMLAI to distribute works in journals and other publication media. Unless otherwise stated, the author(s) is a public entity as soon as the article is published