Combination of Backpropagation Neural Network and Particle Swarm Optimization for Water Production Prediction in Municipal Waterworks
(1) Institut Teknologi Telkom Purwokerto
(2) Institut Teknologi Telkom Purwokerto
(3) Institut Teknologi Telkom Purwokerto
Abstract
Abstract.
Purpose: As the population grows, the need for clean water also increases. Municipal Waterworks (PDAM) is an institution that regulates and manages the procurement of clean water for the community. So, the amount of water produced and distributed should be adjusted to the demand for water. Predictions on PDAM water production need to be done as planning and better preparation and facilitating and assisting in decision-making.
Methods: The study used the Neural Network backpropagation algorithm combined with Particle Swarm Optimization (PSO) to predict the amount of water PDAM should produce. Backpropagation has a good ability to make predictions. But backpropagation has a weakness that causes it to get stuck at a local minimum. This is influenced by the determination of weights that are not optimal. In this study, PSO had a role in optimizing error values on the network to gain optimal weight.
Result: This study obtained MSE values in the training and testing process of 0.00179 and 0.00081 from the combination model of backpropagation ANN and PSO. It is smaller than the ANN model without using an optimization algorithm.
Novelty: The combination of JST backpropagation and PSO can improve predictions' accuracy and produce optimum weights.Keywords
Full Text:
PDFReferences
D. Lestari and Y. Yaddarabullah, “Perancangan Alat Pembacaan Meter Air PDAM Menggunakan Arduino Uno,” Al-Fiziya J. Mater. Sci. Geophys. Instrum. Theor. Phys., vol. 1, no. 2, pp. 36–41, 2019.
D. Lestari and Yaddarabullah, “Perancangan Alat Pembacaan Meter Air PDAM Menggunakan Arduino Uno,” Al-Fiziya J. Mater. Sci. Geophys. Instrumentation, Theor. Phys., vol. I, no. 2, 2018.
Bahar and S. A. Yahya, “Penerapan Algoritma Backpropagation Untuk Prediksi Kebutuhan Air Bersih pada PDAM Intan Banjar,” Progresif J. Ilm. Komput., vol. 15, no. 1, p. 51, 2019.
Brahmanja, A. Ariyanto, and K. Fahmi, “Prediksi Jumlah Kebutuhan Air Bersih Bpab Unit Dalu - Dalu 5 Tahun Mendatang (2018) Kecamatan Tambusai Kab Rokan Hulu,” J. Mhs. Tek. UPP, vol. 1, no. 1, 2013.
A. P. Widodo, E. A. Sarwoko, and Z. Firdaus, “Akurasi Model Prediksi Metode Backpropagation Menggunakan Kombinasi Hidden Neuron Dengan Alpha,” Matematika, vol. 2, no. 20, pp. 79–84, 2017.
N. A. Fitri and I. Taufik, “Perbandingan JST Metode Backpropagation dan Metode Radial Basis dalam Memprediksi Curah Hujan Harian Bandara Internasional Minangkabau,” J. Fis. Unand, vol. 9, no. 2, pp. 217–223, 2020.
B. K. Sihotang and A. Wanto, “Analisis Jaringan Syaraf Tiruan Dalam Memprediksi Jumlah Tamu Pada Hotel Non Bintang,” Techno.COM, vol. 17, no. 4, pp. 333–346, 2018.
N. F. Hasan, K. Kusrini, and H. Al Fatta, “Peramalan Jumlah Penjualan Menggunakan Jaringan Syaraf Tiruan Backpropagation Pada Perusahaan Air Minum Dalam Kemasan,” J. Tek. Inform. dan Sist. Inf., vol. 5, no. 2, pp. 179–188, 2019.
I. P. Sutawinaya, I. N. G. A. Astawa, and N. K. D. Hariyanti, “Perbandingan Metode Jaringan Saraf Tiruan Pada Peramalan Curah Hujan,” Logic, vol. 17, no. 2, pp. 92–97, 2017.
N. Nurkholiq, T. Sukmadi, and A. Nugroho, “Analisis Perbandingan Metode Logika Fuzzy Dengan Jaringan Syaraf Tiruan Backpropagation Pada Peramalan Kebutuhan Energi Listrik Jangka Panjang Di Indonesia Sampai Tahun 2022,” Transient, vol. 3, no. 2, 2014.
C. D. Suhendra and R. Wardoyo, “Penentuan Arsitektur Jaringan Syaraf Tiruan Backpropagation (Bobot Awal dan Bias Awal) Menggunakan Algoritma Genetika,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 9, no. 1, p. 77, 2015.
C. A. Caesar, L. Hanum, and I. Cholissodin, “Perbandingan Metode ANN-PSO Dan ANN-GA Dalam Pemodelan Komposisi Pakan Kambing Peranakan Etawa (PE) Untuk Optimasi Kandungan Gizi,” J. Teknol. Inf. dan Ilmu Komput., vol. 3, no. 3, pp. 216–225, 2016.
J. Widagdo, “Perbandingan Metode ANN-PSO dan ANN-GA untuk Peningkatan Akurasi Prediksi Harga Emas ANTAM,” J. Disprotek, vol. 10, no. 2, pp. 101–106, 2019.
K. L. Du and M. N. S. Swamy, “Particle Swarm Optimization,” in Search and Optimization by Metaheuristics: Techniques and Algorithms Inspired by Nature, Cham: Birkhauser, pp. 153–173, 2016.
N. Nikentari, H. Kurniawan, N. Ritha, and D. Kurniawan, “Optimasi Jaringan Syaraf Tiruan Backpropagation Dengan Particle Swarm Optimization Untuk Prediksi Pasang Surut Air Laut,” J. Teknol. Inf. dan Ilmu Komput., vol. 5, no. 5, pp. 605–612, 2018.
A. P. Windarto et al., Jaringan Saraf Tiruan: Algoritma Prediksi dan Implementasi. Yayasan Kita Menulis, 2020.
N. P. Sakinah, I. Cholissodin, and A. W. Widodo, “Prediksi Jumlah Permintaan Koran Menggunakan Metode Jaringan Syaraf Tiruan Backpropagation,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 7, pp. 2612–2618, 2018.
Y. P. Sugandhi, B. Warsito, and A. R. Hakim, “Prediksi Harga Saham Harian Menggunakan Cascade Forward Neural Network (CFNN) Dengan Particle Swarm Optimization (PSO),” Stat. J. Theor. Stat. Its Appl., vol. 19, no. 2, pp. 71–82, 2019.
Suyanto, Algoritma Optimasi Deterministik atau Probabilistik. Yogyakarta: Graha Ilmu, 2014.
D. P. Rini and S. M. Shamsuddin, “Particle Swarm Optimization: Technique, System and Challenges,” Int. J. Appl. Inf. Syst., vol. 14, no. 1, pp. 19–27, 2011.
J. C. Bansal, “Particle Swarm Optimization,” in Evolutionary and Swarm Intelligence Algorithms, Cham: Springer International Publishing, pp. 11–23, 2018.
Refbacks
- There are currently no refbacks.
Scientific Journal of Informatics (SJI)
p-ISSN 2407-7658 | e-ISSN 2460-0040
Published By Department of Computer Science Universitas Negeri Semarang
Website: https://journal.unnes.ac.id/nju/index.php/sji
Email: [email protected]
This work is licensed under a Creative Commons Attribution 4.0 International License.