Estimasi Kerapatan Energi Surya Berbasis Kelembapan Udara dan Kecepatan Angin Dengan Menggunakan Metode ANFIS Sebagai Sumber Energi Pada Fotovoltaik

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Published: Oct 20, 2026
DOI: https://doi.org/10.34012/jurnalsisteminformasidanilmukomputer.v9i2.7884

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Adolf Patrick Raditya Sitorus Patrick
a:1:{s:5:"en_US";s:27:"UNIVERSITAS PRIMA INDONESIA";}
Muhammad Irwanto
Universitas Prima Indonesia

Abstract

Energi surya merupakan salah satu sumber energi terbarukan yang potensial untuk memenuhi kebutuhan energi listrik melalui sistem fotovoltaik. Namun, variabilitas intensitas radiasi matahari sering menjadi kendala dalam perencanaan dan optimasi sistem tersebut. Penelitian ini bertujuan untuk melakukan estimasi kerapatan energi surya berbasis parameter meteorologis, yaitu kelembapan udara dan kecepatan angin, dengan menggunakan metode Adaptive Neuro-Fuzzy Inference System (ANFIS). Data masukan berupa kelembapan relatif (%) dan kecepatan angin (km/j), sedangkan keluaran berupa estimasi kerapatan energi surya (MJ/𝑚2). Model ANFIS dibangun melalui proses pelatihan menggunakan kombinasi fungsi keanggotaan dan algoritma pembelajaran hibrid untuk memperoleh performa estimasi yang optimal. Hasil penelitian menunjukkan bahwa ANFIS mampu memprediksi kerapatan energi surya dengan tingkat akurasi yang tinggi serta kesalahan prediksi yang rendah, sehingga layak digunakan sebagai alat estimasi dalam perencanaan sistem fotovoltaik. Temuan ini diharapkan dapat mendukung pemanfaatan energi surya yang lebih efisien, khususnya pada wilayah dengan dinamika cuaca yang fluktuatif.
Kata Kunci: energi surya, fotovoltaik, kelembapan udara, kecepatan angin, ANFIS, estimasi kerapatan energi.

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How to Cite
Patrick, A. P. R. S., & Irwanto, M. (2026). Estimasi Kerapatan Energi Surya Berbasis Kelembapan Udara dan Kecepatan Angin Dengan Menggunakan Metode ANFIS Sebagai Sumber Energi Pada Fotovoltaik. Jusikom : Jurnal Sistem Informasi Ilmu Komputer, 9(2), 35–43. https://doi.org/10.34012/jurnalsisteminformasidanilmukomputer.v9i2.7884
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Vol. 9 No. 2 (2026): JUSIKOM: Jurnal Sistem Informasi Ilmu Komputer
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Articles
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References

Fahriannur, A.; Ambas, H.; Rahmanto, D. E. (2024). Rancang Bangun Sistem Monitoring Radiasi Matahari, Kecepatan Angin dan Arah Angin sebagai Studi Potensi Energi Terbarukan. J-TETA: Jurnal Teknik Terapan, 3(1), 10–16.

Al Ghifari, R. M. (tahun tidak disebut). Analisis Intensitas Radiasi Matahari Terhadap Parameter Cuaca dan Potensi Energi Surya. WAFI: Wahana Fisika dan Informasi.

Sagala, M. B., & Ramadhan, A. (2020). Model ANFIS untuk estimasi iradiasi matahari berbasis kelembapan udara dan kecepatan angin sebagai input energi fotovoltaik. Jurnal Energi Terbarukan, 5(1), 25–34.

Rahimi, M., & Kasmaei, H. (2017). Solar irradiance estimation model using neuro-fuzzy inference system under uncertain atmospheric conditions. Energy Procedia, 141, 231–238.

Gani, A., & Shaaban, M. (2019). Neuro-fuzzy modeling of solar radiation influenced by wind speed and humidity for PV system performance assessment. Journal of Cleaner Production, 212, 1302–1315.

Abed, M. F., Abdullah, M. K., & Rahman, H. A. (2019). Solar irradiation prediction using ANFIS based on humidity and wind speed parameters. International Journal of Renewable Energy Research, 9(2), 456–464.

Wijoyo, A. (2018). Studi Perbandingan Peramalan Intensitas Radiasi Matahari di Lingkungan Kota Menggunakan ANFIS dan Regresi Linier Berganda. Universitas Brawijaya.

Jang, J. S. R. (1993). ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665–685. https://doi.org/10.1109/21.256541

Kocer, A. M., & Kocer, C. (2018). Modeling solar energy density using soft computing approaches under dynamic weather conditions. Applied Energy, 210, 432–441. https://doi.org/10.1016/j.apenergy.2017.11.025

Mellit, A., & Kalogirou, S. A. (2008). Artificial intelligence techniques for photovoltaic applications: A review. Progress in Energy and Combustion Science, 34(5), 574–632. https://doi.org/10.1016/j.pecs.2008.01.001

Olatomiwa, L., Mekhilef, S., Shamshirband, S., & Petković, D. (2015). Adaptive neuro-fuzzy inference system (ANFIS) model for estimating global solar radiation using meteorological data. Energy Conversion and Management, 102, 190–199. https://doi.org/10.1016/j.enconman.2015.02.042

Omer, A. M. (2008). Energy, environment and sustainable development. Renewable and Sustainable Energy Reviews, 12(9), 2265–2300. https://doi.org/10.1016/j.rser.2007.05.001

Pande, A. D., & Sahu, B. K. (2019). Solar radiation estimation techniques: A review. Journal of Cleaner Production, 235, 1100–1115. https://doi.org/10.1016/j.jclepro.2019.06.287

Raza, A., & Khahro, S. F. (2022). Solar energy forecasting models: Current trends and future directions. Sustainable Energy Technologies and Assessments, 52, 102110. https://doi.org/10.1016/j.seta.2022.102110

Srivastava, R., & Giri, S. (2021). Solar irradiance estimation using hybrid neuro-fuzzy intelligence techniques. Solar Energy, 214, 112–124. https://doi.org/10.1016/j.solener.2020.11.054

Twidell, J., & Weir, T. (2015). Renewable energy resources (3rd ed.). Routledge. https://doi.org/10.4324/9781315766416

Yadav, A. K., & Chandel, S. S. (2014). Solar radiation prediction using Artificial Neural Network models: A review. Renewable and Sustainable Energy Reviews, 33, 772–781. https://doi.org/10.1016/j.rser.2014.02.016