Implementation of Resilient Methods to Predict Open Unemployment in Indonesia According to Higher Education Completed

Widodo Saputra(1*), Jaya Tata Hardinata(2), Anjar Wanto(3),


(1) AMIK Tunas Bangsa
(2) STIKOM Tunas Bangsa
(3) STIKOM Tunas Bangsa
(*) Corresponding Author

Abstract


Unemployment is a big problem faced by the Indonesian people from year to year besides poverty. Therefore it is necessary to predict the level of open unemployment in Indonesia so that later the government and private parties have the right references and references to work together to overcome this problem. The prediction method used is Resilient Backpropagation which is one method of Artificial Neural Networks which is often used for data prediction. The research data used is open unemployment data according to the highest education completed in 2005-2018 based on the semester obtained from the website of the Indonesian Central Bureau of Statistics. Based on this data a network architecture model will be formed and determined, including 12-6-2, 12-12-2, 12-18-2, 12-24-2, 12-12-12-2, 12-12-18 -2, 12-18-18-2 and 12-18-24-2. From these 8 models after training and testing, the results show that the best architectural model is 12-18-2 (12 is the input layer, 18 is the number of hidden neurons and 2 is the output layer). The accuracy of the architectural model for semester 1 and semester 2 is 75% with an MSE value of 0.0022135087 and 0.0044974696

Keywords


Implementation, Resilient, Prediction, Open Unemployment, Highest Education

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References


Andriani, Y., Silitonga, H., & Wanto, A. (2018). Analisis Jaringan Syaraf Tiruan untuk prediksi volume ekspor dan impor migas di Indonesia. Register - Jurnal Ilmiah Teknologi Sistem Informasi, 4(1), 30–40.

Apriliyah, & M, Wayan Firdaus, A. W. W. (2008). Perkiraan Penjualan Beban Listrik Menggunakan Jaringan Syaraf Tiruan Resilent Backpropogation (RPROP). Jurnal Kursor, 4(2), 41–47. https://doi.org/10.1089/fpd.2015.2079

BPS. (2018). Pengangguran Terbuka Menurut Pendidikan Tertinggi yang Ditamatkan 2005 - 2018. Retrieved from https://www.bps.go.id/statictable/2009/04/16/972/pengangguran-terbuka-menurut-pendidikan-tertinggi-yang-ditamatkan-1986---2017.html

Fardhani, A. A., Insani, D., Simanjuntak, N., & Wanto, A. (2018). Prediksi Harga Eceran Beras Di Pasar Tradisional Di 33 Kota Di Indonesia Menggunakan Algoritma Backpropagation. Jurnal Infomedia, 3(1), 25–30.

Febriadi, B., Zamzami, Z., Yunefri, Y., & Wanto, A. (2018). Bipolar function in backpropagation algorithm in predicting Indonesia’s coal exports by major destination countries. IOP Conference Series: Materials Science and Engineering, 420(12089), 1–9. https://doi.org/10.1088/1757-899X/420/1/012087

Hartono, B., & Hapsari, R. (2018). Kajian Metode Small Area Estimation Untuk Menduga Tingkat Pengangguran Terbuka. Jurnal Litbang Sukowati, 1(2), 95–106.

Hutabarat, M. A. P., Julham, M., & Wanto, A. (2018). Penerapan Algoritma Backpropagation Dalam Memprediksi Produksi Tanaman Padi Sawah Menurut Kabupaten/Kota di Sumatera Utara. Jurnal semanTIK, 4(1), 77–86.

Nasution, N., Zamsuri, A., Lisnawita, L., & Wanto, A. (2018). Polak-Ribiere updates analysis with binary and linear function in determining coffee exports in Indonesia. IOP Conference Series: Materials Science and Engineering, 420(12089), 1–9. https://doi.org/10.1088/1757-899X/420/1/012088

Pranata, R. E., Sinaga, S. P., & Wanto, A. (2018). Estimasi Wisatawan Mancanegara Yang Datang ke Sumatera Utara Menggunakan Jaringan Saraf. Jurnal semanTIK, 4(1), 97–102.

Riedmiller, M., & Braun, H. (1992). RPROP - A Fast Adaptive Learning Algorithm. The International Symposium on Computer and Information Science VII, 1(4), 4–10. https://doi.org/10.1007/978-1-4419-1603-7_12

Saputra, W., Tulus, T., Zarlis, M., Sembiring, R. W., & Hartama, D. (2017). Analysis Resilient Algorithm on Artificial Neural Network Backpropagation. Journal of Physics: Conference Series, 930(1), 1–7. https://doi.org/10.1088/1742-6596/930/1/012035

Setti, S., & Wanto, A. (2018). Analysis of Backpropagation Algorithm in Predicting the Most Number of Internet Users in the World. JOIN (Jurnal Online Informatika), 3(2), 110–115. https://doi.org/10.15575/join.

Sihotang, B. K., & Wanto, A. (2018). Analisis Jaringan Syaraf Tiruan Dalam Memprediksi Jumlah Tamu Pada Hotel Non Bintang. Jurnal Teknologi Informasi Techno, 17(4), 333–346.

Simbolon, I. A. R., Yatussa’ada, F., & Wanto, A. (2018). Penerapan Algoritma Backpropagation dalam Memprediksi Persentase Penduduk Buta Huruf di Indonesia. Jurnal Informatika Upgris, 4(2), 163–169.

Siregar, S. P., Wanto, A., & Nasution, Z. M. (2018). Analisis Akurasi Arsitektur JST Berdasarkan Jumlah Penduduk Pada Kabupaten / Kota di Sumatera Utara. In Seminar Nasional Sains & Teknologi Informasi (SENSASI) (pp. 526–536).

Soleh, A. (2017). Masalah Ketenagakerjaan Dan Pengangguran Di Indonesia. Jurnal Ilmiah Cano Ekonomos, 6(2), 83–92.

Syarun, M. M. (2016). Inflasi, Pengangguran Dan Pertumbuhan Ekonomi Di Negara-Negara Islam. Jurnal Ekonomi Islam, 7(2), 27–44.

Wahyuni, J., Paranthy, Y. W., & Wanto, A. (2018). Analisis Jaringan Saraf Dalam Estimasi Tingkat Pengangguran Terbuka Penduduk Sumatera Utara. Jurnal Infomedia, 3(1), 18–24.

Wanto, A. (2018a). Optimasi Prediksi Dengan Algoritma Backpropagation Dan Conjugate Gradient Beale-Powell Restarts. Jurnal Teknologi Dan Sistem Informasi, 3(3), 370–380. Retrieved from http://teknosi.fti.unand.ac.id/index.php/teknosi/article/view/439

Wanto, A. (2018b). Penerapan Jaringan Saraf Tiruan Dalam Memprediksi Jumlah Kemiskinan Pada Kabupaten/Kota Di Provinsi Riau. Kumpulan jurnaL Ilmu Komputer (KLIK), 5(1), 61–74.

Wanto, A. (2019a). Prediksi Angka Partisipasi Sekolah dengan Fungsi Pelatihan Gradient Descent With Momentum & Adaptive LR. Jurnal Ilmu Komputer Dan Informatika (ALGORITMA), 3(1), 9–20.

Wanto, A. (2019b). Prediksi Produktivitas Jagung Indonesia Tahun 2019-2020 Sebagai Upaya Antisipasi Impor Menggunakan Jaringan Saraf Tiruan Backpropagation. SINTECH (Science and Information Technology), 1(1), 53–62.

Wanto, A., Zarlis, M., Sawaluddin, & Hartama, D. (2017). Analysis of Artificial Neural Network Backpropagation Using Conjugate Gradient Fletcher Reeves in the Predicting Process. Journal of Physics: Conference Series, 930(1), 1–7.




DOI: http://dx.doi.org/10.31289/jite.v3i1.2704

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