Prediksi Penggunaan Bahan Bakar pada PLTGU menggunakan Metode Support Vector Regression (SVR)

Authors

  • Fahriza Novianti UIN Sunan Ampel Surabaya
  • Nurissaidah Ulinnuha UIN Sunan Ampel Surabaya
  • Moh. Hafiyusholeh UIN Sunan Ampel Surabaya
  • Agus Arianto Indonesia Power Grati POMU

DOI:

https://doi.org/10.33633/tc.v21i2.5712

Keywords:

Prediksi, Support Vector Regression (SVR), Bahan Bakar Gas, Kernel

Abstract

Bahan bakar merupakan komponen utama dalam pembangkitan energi listrik. Penggunaan bahan bakar harus disesuaikan dengan kebutuhan beban yang diperlukan agar dalam proses pembangkitan tidak terjadi kekurangan bahan bakar dan tidak ada energi yang terbuang. Tujuan penelitian ini adalah melakukan prediksi penggunaan bahan bakar dengan menggunakan data penggunaan bahan bakar gas PT. Indonesia Power pada tanggal 21 Agustus 2020 hingga 31 Agustus 2021. Data tersebut dianalisis dengan metode Support Vector Regression (SVR). Metode SVR merupakan pengembangan dari metode Support Vector Machine (SVM) yang digunakan untuk permasalahan regresi. Dalam melakuakan prediksi dengan SVR diperlukan pengujian dengan menggunakan tiga kernel yaitu kernel linear, polynomial dan radial. Hasil Prediksi terbaik yaitu dengan menggunakan kernel polynomial dengan nilai e yaitu 0.0266, nilai b yaitu 0.0285 dan diperoleh MAPE sebesar 7.7513%.

References

D. R. Baskoro, “Peramalan Jangka Pendek Beban Tenaga Listrik Pada PT. PJB UP Gresik Menggunakan Metode Constructive Backpropagation Neural Network Serta Prediksi Kebutuhan Bahan Bakar Gas Pembangkitan,” pp. 9–35, 2017, [Online]. Available: http://repository.unimus.ac.id/411/.

N. Ekasari, N. L. P. S. P. Paramita, and M. Mashuri, “Pengendalian Kualitas Bahan Bakar Gas PT Indonesia Power UPJP-PGT Pasuruan Peta Kendali Univariate Berbasis Model Time Series,” J. Sains dan Seni ITS, vol. 7, no. 2, 2019, doi: 10.12962/j23373520.v7i2.36311.

D. I. Purnama and S. Setianingsih, “Support vector regression (SVR) model for forecasting number of passengers on domestic flights at Sultan Hasanudin airport Makassar,” J. Mat. Stat. dan Komputasi, vol. 16, no. 3, p. 391, 2020, doi: 10.20956/jmsk.v16i3.9176.

R. E. Cahyono, J. P. Sugiono, and S. Tjandra, “Analisis Kinerja Metode Support Vector Regression (SVR) dalam Memprediksi Indeks Harga Konsumen,” JTIM J. Teknol. Inf. dan Multimed., vol. 1, no. 2, pp. 106–116, 2019, doi: 10.35746/jtim.v1i2.22.

R. A. Putri, W. S. Winahju, and M. Mashuri, “Penerapan Metode Ridge Regression dan Support Vector Regression (SVR) untuk Prediksi Indeks Batubara di PT XYZ,” J. Sains dan Seni ITS, vol. 9, no. 1, pp. 64–71, 2020, doi: 10.12962/j23373520.v9i1.51021.

K. Dewi, P. P. Adikara, and S. Adinugroho, “Prediksi Indeks Harga Konsumen ( IHK ) Kelompok Perumahan , Air , Listrik , Gas Dan Bahan Bakar Menggunakan Metode Support Vector Regression,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 10, pp. 3856–3862, 2018.

E. Fatchurin, A. Fanani, and M. Hafiyusholeh, Peramalan Penggunaan Bahan Bakar Pada Pembangkit Listrik Tenaga Gas Uap Menggunakan Metode Backpropagation Neural Network, vol. 4, no. 2. 2020.

M. S. M. R, “Analisa Performa Heat Recovery Steam Generator Sebelum Dan Sesudah Cleaning Di Pt Indonesia Power Tambak Lorok Semarang Menggunakan Software Matlab R.12,” Anal. Performa Heat Recover. Steam Gener. Sebelum Dan Sesudah Clean. Di Pt Indones. Power Tambak Lorok Semarang Menggunakan Softw. Matlab R.12, vol. 16, no. 1, pp. 1–12, 2018, doi: 10.15294/sainteknol.v16i1.14044.

G. Aksu, C. O. Güzeller, and M. T. Eser, “The Effect of the Normalization Method Used in Different Sample Sizes on the Success of Artificial Neural Network Model,” Int. J. Assess. Tools Educ., vol. 6, no. 2, pp. 170–192, 2019, doi: 10.21449/ijate.479404.

D. Singh and B. Singh, “Investigating the impact of data normalization on classification performance,” Appl. Soft Comput. J., no. xxxx, p. 105524, 2019, doi: 10.1016/j.asoc.2019.105524.

I. I. Zulfa, D. Candra, R. Novitasari, F. Setiawan, A. Fanani, and M. Hafiyusholeh, “Prediction of Sea Surface Current Velocity and Direction Using LSTM,” Indones. J. Electron. Instrum. Syst., vol. 11, no. 1, pp. 93–102, 2021, doi: 10.22146/ijeis.63669.

A. Asyiva, “Prediksi laju inflasi Indonesia menggunakan metode support vector regression dengan kernel radial basis function,” Repository.Uinjkt.Ac.Id, 2019, [Online].Available:http://repository.uinjkt.ac.id/dspace/bitstream/123456789/47435/1/AULIA ASYIVA-FST.pdf.

N. D. Maulana, B. D. Setiawan, and C. Dewi, “Implementasi Metode Support Vector Regression ( SVR ) Dalam Peramalan Penjualan Roti ( Studi Kasus : Harum Bakery ),” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 3, no. 3, pp. 2986–2995, 2019.

N. P. N. Hendayanti and M. Nurhidayati, “Perbandingan Metode Seasonal Autoregressive Integrated Moving Average (SARIMA) dengan Support Vector Regression (SVR) dalam Memprediksi Jumlah Kunjungan Wisatawan Mancanegara ke Bali,” J. Varian, vol. 3, no. 2, pp. 149–162, 2020, doi: 10.30812/varian.v3i2.668.

T. Mar, D. C. R. Novitasari, F. Setiawan, and N. Ulinnuha, “Tide Prediction in Prigi Beach using Support Vector Regression ( SVR ) Method,” vol. 8, no. 2, pp. 194–201, 2021, doi: 10.15294/sji.v8i2.28906.

M. Ghazali and R. Purnamasari, “PENCARIAN KERNEL TERBAIK SUPPORT VECTOR REGRESSION PADA KASUS DATA KEMISKINAN DI INDONESIA DENGAN USER INTERFACE ( GUI ) MATLAB,” vol. 9, no. 1, 2021.

D. Sepri, A. Fauzi, R. Wandira, O. S. Riza, and Y. Fitri, “Prediksi Harga Cabai Merah Menggunakan Support Vector Regression,” Ejournal.Upbatam.Ac.Id, no. October, pp. 0–5, 2020, [Online]. Available: http://ejournal.upbatam.ac.id/index.php/cbis/article/view/1921.

A. Botchkarev, “Performance Metrics (Error Measures) in Machine Learning Regression, Forecasting and Prognostics: Properties and Typology,” no. 2018, pp. 1–37, 2018, [Online]. Available: http://arxiv.org/abs/1809.03006.

A. Arfan and L. ETP, “Perbandingan Algoritma Long Short-Term Memory dengan SVR Pada Prediksi Harga Saham di Indonesia,” Petir, vol. 13, no. 1, pp. 33–43, 2020, doi: 10.33322/petir.v13i1.858

Downloads

Published

2022-05-27

Issue

Vol. 21 No. 2 (2022): Mei 2022

Section

Articles

License

Copyright (c) 2022 Fahriza Novianti, Nurissaidah Ulinnuha, Moh. Hafiyusholeh, Agus Arianto

Creative Commons License

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

 

License Terms

All articles published in Techno.COM Journal are licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). This means:

1. Attribution

Readers and users are free to:

  • Share – Copy and redistribute the material in any medium or format.

  • Adapt – Remix, transform, and build upon the material.

As long as proper credit is given to the original work by citing the author(s) and the journal.

2. Non-Commercial Use

  • The material cannot be used for commercial purposes.

  • Commercial use includes selling the content, using it in commercial advertising, or integrating it into products/services for profit.

3. Rights of Authors

  • Authors retain copyright and grant Techno.COM Journal the right to publish the article.

  • Authors can distribute their work (e.g., in institutional repositories or personal websites) with proper acknowledgment of the journal.

4. No Additional Restrictions

  • The journal cannot apply legal terms or technological measures that restrict others from using the material in ways allowed by the license.

5. Disclaimer

  • The journal is not responsible for how the published content is used by third parties.

  • The opinions expressed in the articles are solely those of the authors.

For more details, visit the Creative Commons License Page:
? https://creativecommons.org/licenses/by-nc/4.0/

 

Most read articles by the same author(s)

Obs.: This plugin requires at least one statistics/report plugin to be enabled. If your statistics plugins provide more than one metric then please also select a main metric on the admin's site settings page and/or on the journal manager's settings pages.