Optimasi Waktu Akuisisi Data Satelit Noaa18 Menggunakan Jaringan Syaraf Tiruan Backpropagation

Abstract

Earth stations are built to monitor the presence of satellites starting from satellite data, monitoring satellites, and carry out orders and corrections if needed. On the earth station there is a satellite data receiving antenna, the more elevation angle of the current satellite data receiver antenna can affect the time duration of the satellite data. The purpose of this research is to apply the Artificial Neural Network (ANN) method to design a time optimization system for satellite data at the LAPAN Pekayon earth station, East Jakarta. The data used as input is the elevation angle. The benefit of this research is expected to make it easier for operators and technicians to measure the time optimization of satellite data at earth stations. The best training results with learning rate = 0.2, error = 0.0001, max. epoch = 100000, neuron hidden layer = 15. The MSE value obtained is 0.0001 reaching the goal at epoch 68810. Regret the training / training reverse sequence reaches 0.99878. The best test result is to use learning speed 0.2 hidden layer neurons 15 comparison of training data = 54 and test data = 18. The accurate result is exactly the same as the specified error, namely 0.0001. The difference in the average target duration is 3 seconds compared to the ANN target. Artificial Neural Network (ANN) with the back propagation method of training function gradient descent (traingd), was successfully used to an optimization system for satellite data acquisition time at earth stations.

Keywords

Elevation angle, Backpropagation, Time optimization, Satellite data acquisition

Full Text:

PDF

References

LAPAN. (2017). Sistem stasiun bumi Penerima Data Inderaja Pare-pare, Rumpin dan Pekayon. Pusat Teknologi dan Data Penginderaan Jauh Kedeputian Bidang Penginderaan Jauh LAPAN.

Hidayat, A.N., Fatkhurrozi, B., & Nawawi, I. (2020). Implementasi Logika Fuzzy pada Kekuatan Sinyal yang Diterima Antena Viasat X-Band. Jurnal AVITEC, 2(2), 91-102.

Pratama, E. R. (2019). Optimasi Waktu dan Biaya Proyek Pembangunan Gedung Royal Sentul Park Menggunakan Metode Time Cost Trade Off. Repository Universitas Gadjah Mada, 12-14.

Munir, Muhammad. F. S. (2018). Prediksi Beban Generator Menggunakan Jaringan Saraf Tiruan. Dspace Universitas Islam Indonesia, 2-11.

Sharma, A.A. (2016). Univariate short term forecasting of solar irradiance using modified online backpropagation through time. IEEE, 978-984.

Masrizal dan Hadiansa, A. (2017). Prediksi Jumlah Lulusan Mahasiswa Stmik Dumai Menggunakan Jaringan Syaraf Tiruan. Jurnal Informatika, Manajemen dan Komputer, 9(2), 9-14.

Naibaho, P. M. (2007). Penerapan Jaringan Syaraf Tiruan Untuk Pembuatan Sistem Pengenalan Tanda Tangan. Universitas Sanata Dharma, 8-11.

Saputra, I. (2019). Implementasi Backpropagation Momentum untuk Diagnosa Anxiety Disorder. Universitas Islam Negeri Sultan Syarif Kasim Riau, 30-45.

Nurhayati. (2018). Jaringan Saraf Tiruan Backpropagation Untuk Menentukan Tingkat Pencemaran Air. E-JURNAL JUSITI: Jurnal Sistem Informasi dan Teknologi Informasi, 4(2), 124-131.

Hasim, A. (2008). Prakiraan Beban Listrik Kota Pontianak Dengan Jaringan Syaraf Tiruan (Artificial Neural Network). Sekolah Pasca Sarjana Institut Pertanian Bogor, 20-23.

http://dx.doi.org/10.28989/avitec.v3i1.786

Article Metrics

Abstract view: 499 times
Download     : 367   times

This work is licensed under a Creative Commons Attribution 4.0 International License.

Refbacks

• There are currently no refbacks.