Implementation of One-step Secant Algorithm for Forecasting Open Unemployment by Highest Educational Graduate

Authors

  • Ismi Azhami STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
  • Eka Irawan STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
  • Dedi Suhendro STIKOM Tunas Bangsa, Pematangsiantar, Indonesia

DOI:

https://doi.org/10.55123/jomlai.v1i3.946

Keywords:

Artificial Neural Networks, One-step secant, Forecasting, Open Unemployment, Higher Education

Abstract

Based on data, the open unemployment rate according to the highest education graduate in Indonesia shows the number of semester unemployment which has an unstable value, sometimes up and sometimes down. This study aims to implement the ability and performance of one of the training functions on the backpropagation algorithm, namely one-step secant, which can later be used as a reference in terms of data forecasting. The one-step secant algorithm is an algorithm that is able to train any network as long as the input, weight and transfer functions have derivative functions and this algorithm is able to make training more efficient because it does not require a very long time. The data used in this study is open unemployment data according to the highest education completed in Indonesia in 2006-2021 based on semester, which is sourced from the Indonesian Central Statistics Agency. Based on this data, a network architecture model will be formed and determined using the One-step secant method, including 14-13-2, 14-16-2, 14-19-2, 14-55-2, and 14-77- 2. From these 5 models, after training and testing, the results show that the best architectural model is 14-19-2 (14 is the input layer, 19 is the number of neurons in the hidden layer and 2 is the output layer). The accuracy level of the architectural model for semester 1 and semester 2 is 75% with MSE values of 0.00130797 and 0.00388535.

References

R. Maiyuriska, “Penerapan Jaringan Syaraf Tiruan dengan Algoritma Backpropagation dalam Memprediksi Hasil Panen Gabah Padi,” Jurnal Informatika Ekonomi Bisnis, vol. 4, pp. 28–33, 2022.

E. Irawan, M. Zarlis, and E. B. Nababan, “Analisis Penambahan Nilai Momentum Pada Prediksi Produktivitas Kelapa Sawit Menggunakan Backpropagation,” InfoTekJar (Jurnal Nasional Informatika dan Teknologi Jaringan), vol. 1, no. 2, pp. 84–89, 2017.

N. L. W. S. R. Ginantra et al., “Performance One-step secant Training Method for Forecasting Cases,” Journal of Physics: Conference Series, vol. 1933, no. 1, pp. 1–8, 2021.

M. Najwa, B. Warsito, and D. Ispriyanti, “Pemodelan Jaringan syaraf Tiruan Dengan Algoritma One Step Secant Backpropagation Dalam Return Kurs Rupiah Terhadap Dolar Amerika Aerikat,” Jurnal Gaussian, vol. 6, no. 1, pp. 61–70, 2017.

C. S. T. Columns, “A Novel Hybrid Model Based on a Feedforward Neural Network and One Step Secant Algorithm for Prediction of Load-Bearing Capacity of Rectangular.”

Badan Pusat Statistik Indonesia, “Pengangguran Terbuka Menurut Pendidikan Tertinggi yang Ditamatkan (Orang),” www.bps.go.id, 2021. .

S. Dewi, “Pengangguran Terbuka : Kasus Di Indonesia,” Jurnal Mitra Manajemen, vol. 9, no. 1, pp. 43–46, 2017.

R. C. Rambe, P. H. Prihanto, and H. Hardiani, “Analisis faktor-faktor yang mempengaruhi pengangguran terbuka di Provinsi Jambi,” e-Jurnal Ekonomi Sumberdaya dan Lingkungan, vol. 8, no. 1, pp. 54–67, 2019.

G. Dainty, J. Roring, A. G. Kumenaung, A. L. C. P. Lapian, and U. S. Ratulangi, “PENGARUH PERTUMBUHAN EKONOMI DAN PENDIDIKAN TERHADAP TINGKAT PENGANGGURAN TERBUKA ( TPT ) 4 KOTA DI PROVINSI SULAWESI UTARA PENDAHULUAN Latar Belakang Pengangguran merupakan masalah yang kompleks karena mempengaruhi dan juga dipengaruhi oleh banyak faktor,” vol. 20, no. 4, pp. 70–87, 2020.

W. Saputra, J. T. Hardinata, and A. Wanto, “Penerapan Metode Resilient dalam Menentukan Model Arsitektur Terbaik untuk Prediksi Pengangguran Terbuka di Indonesia,” Seminar Nasional Aplikasi Teknologi Informasi (SNATi), pp. 21–29, 2019.

G. W. Bhawika et al., “Implementation of ANN for Predicting the Percentage of Illiteracy in Indonesia by Age Group,” Journal of Physics: Conference Series, vol. 1255, no. 1, pp. 1–6, 2019.

A. Wanto et al., “Analysis of the Backpropagation Algorithm in Viewing Import Value Development Levels Based on Main Country of Origin,” Journal of Physics: Conference Series, vol. 1255, no. 1, pp. 1–6, 2019.

E. Siregar, H. Mawengkang, E. B. Nababan, and A. Wanto, “Analysis of Backpropagation Method with Sigmoid Bipolar and Linear Function in Prediction of Population Growth,” Journal of Physics: Conference Series, vol. 1255, no. 1, pp. 1–6, 2019.

M. K. Z. Sormin, P. Sihombing, A. Amalia, A. Wanto, D. Hartama, and D. M. Chan, “Predictions of World Population Life Expectancy Using Cyclical Order Weight / Bias,” Journal of Physics: Conference Series, vol. 1255, no. 1, pp. 1–6, 2019.

A. Wanto et al., “Analysis of the Accuracy Batch Training Method in Viewing Indonesian Fisheries Cultivation Company Development,” Journal of Physics: Conference Series, vol. 1255, no. 1, pp. 1–6, 2019.

Y. Andriani, H. Silitonga, and A. Wanto, “Analisis Jaringan Syaraf Tiruan untuk prediksi volume ekspor dan impor migas di Indonesia,” Register: Jurnal Ilmiah Teknologi Sistem Informasi, vol. 4, no. 1, pp. 30–40, 2018.

W. Saputra, J. T. Hardinata, and A. Wanto, “Implementation of Resilient Methods to Predict Open Unemployment in Indonesia According to Higher Education Completed,” JITE (Journal of Informatics and Telecommunication Engineering), vol. 3, no. 1, pp. 163–174, 2019.

S. Setti and A. Wanto, “Analysis of Backpropagation Algorithm in Predicting the Most Number of Internet Users in the World,” JOIN (Jurnal Online Informatika), vol. 3, no. 2, pp. 110–115, 2018.

Downloads

Published

2022-10-18

How to Cite

Azhami, I., Irawan, E., & Suhendro, D. (2022). Implementation of One-step Secant Algorithm for Forecasting Open Unemployment by Highest Educational Graduate. JOMLAI: Journal of Machine Learning and Artificial Intelligence, 1(3), 219–228. https://doi.org/10.55123/jomlai.v1i3.946

Issue

Vol. 1 No. 3 (2022): September

Section

Articles

License

Copyright (c) 2022 Ismi Azhami, Eka Irawan, Dedi Suhendro

Creative Commons License

This work is licensed under a Creative 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