Analisis Intensitas Radiasi Matahari terhadap Efisiensi Panel Surya menggunakan Algoritma Support Vector Regression (SVR) dan Naïve Bayes
DOI:
https://doi.org/10.24252/jft.v12i1.56806Keywords:
Machine Learning, Naïve Bayes, Solar Panel Efficiency, Solar Radiation Intensity, Support Vector Regression (SVR)Abstract
Solar radiation is an alternative energy in the form of heat from electromagnetic waves consisting of electric and magnetic fields. The utilization of solar energy to be converted into electrical energy can be done through solar panels with the photovoltaic effect mechanism. This research aims to analyze the effect of solar radiation intensity on solar panel efficiency using the Support Vector Regression (SVR) and Naïve Bayes algorithms. The research method used is a computational method with Machine Learning techniques. The algorithms used are the Support Vector Regression algorithm and Naïve Bayes. The data used comes from the Jambi Province Meteorology, Climatology, Geophysics (BMKG) Agency. The performance of each model was then evaluated using accuracy metrics to determine the accuracy comparison. The results showed a very strong and positive relationship between solar radiation intensity and solar panel efficiency by 97%. Based on the results, the Naïve Bayes algorithm achieved an accuracy of 96.71%, which shows the model is capable in capturing the relationship between radiation intensity and panel efficiency. Meanwhile, the Support Vector Regression algorithm obtained an accuracy of 80.00 %.
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[1] Mahdie, M. F., Rianawati, F., Nisa, K., & Fitriani, A. (2022). Klimatologi Hutan. Banjarbaru: CV Banyubening Cipta Sejahtera.
[2] Alim, M. S., Thamrin, S., & Laksmono, R. W. (2023). Pemanfaatan Pembangkit Listrik Tenaga Surya sebagai Alternatif Ketahanan Energi Nasional Masa Depan. Jurnal Pengabdian kepada Masyarakat Nusantara (JPkMN), 4(3), e-ISSN: 2745-4053. Doi: https://doi.org/10.55338/jpkmn.v4i2.1480
[3] Kharisma, A., Pinandita, S., & Jayanti, A. E. (2024). Literature Review: Kajian Potensi Energi Surya Alternatif Energi Listrik. Jurnal Energi Baru dan Terbarukan, 5(2), e-ISSN:145-154. https://doi.org/10.14710/jebt.2024.23956
[4] Jacobus, L., Setyowati, E., Patty, E. N. S., & Bokol, F. (2023). Desain Sistem Pompa Air Tenaga Surya. Elektriese: Jurnal Sains dan Teknologi Elektro, 13(1), e-ISSN: 2830-3512X. Doi: https://doi.org/10.47709/elektriese.v13i1.2283
[5] Putra, D. A. M., Paradita, Y., Ridwang, & Adriani. (2024). Pengembangan Prototype Sistem Pengusir Hama Menggunakan Smart Panel Surya, Lampu UV dan Gelombang Ultrasonik Pada Perkebunan. Vertex: Jurnal Ilmiah Teknik Elektro, Teknik Informatika, dan Sistem Informasi, 16(2), e-ISSN: 2714-7487. Doi: https://journal.unismuh.ac.id/index.php/vertex/article/view/15707
[6] Saputra, S., Widyastuti, E. A., Riantiarna, R., & Kurniawati, W. (2024). Efektivitas Panel Surya Sebagai Cadangan Pengganti Energi Listrik Skala Rumahan. TEKTONIK: Jurnal Ilmu Teknik, 1(2), 256–260, e-ISSN: 3026-4103. https://doi.org/10.62017/tektonik.
[7] Rio, A. R., Siahaan, B. M., & Gani, E. (2024). Pemetaan Potensi Energi Matahari di Sulawesi Utara menggunakan Machine Learning K-Means. Jurnal MIPA, 14(2), 1–5. Doi: https://doi.org/10.35799/jm.v14i2.57778.
[8] Heryadi, Yaya, and Teguh Wahyono. (2024). Machine Learning: Konsep dan Implementasi. Yogyakarta: Universitas Teknologi Yogyakarta.
[9] Alief, R., & Nurmiati, E. (2022). Penerapan Kecerdasan Buatan Dan Teknologi Informasi Pada Efisiensi Manajemen Pengetahuan. Jurnal Masyarakat Informatika, 13(1), e-ISSN: 2777-0648. Doi: https://journal.lldikti4.id/index.php/jmi/article/view/43760.
[10] Ananda, J. S., Fendriani, Y., & Pebralia, J. (2024). Classification Analysis of Brain Tumor Disease in Radiographic Images Using Support Vector Machines (SVM) with Python. Jurnal JoP, 9(3), 110–115. ISSN: 2502-2016.
https://doi.org/10.22437/jop.v9i3.36270
[11] Pebralia, J. (2022). Analisis Curah Hujan Menggunakan Machine Learning Metode Regresi Linier Berganda Berbasis Python dan Jupyter Notebook. Jurnal Ilmu Fisika dan Pembelajarannya,6(2),23–30.ISSN:2549-6158. http://jurnal.radenfatah.ac.id/index.php/jifp/article/view/15883
[12] Amanda, D. R., Samsidar, & Pebralia, J. (2024). Analysis of Machine Learning Algorithm Performance in Predicting Ultisol Soil Nutrients Based on Impedance Values. JurnalJoP),9(2),94–101.ISSN:2502-2016.
[13] Arumsari, N., & Pamuji, F. A. (2017). Peramalan Irradiance Cahaya Matahari pada Sel Surya untuk Memenuhi Kebutuhan Energi Listrik dengan Metode Support Vector Regression (SVR). Jurnal Nasional Teknik Elektro, 6(1), e-ISSN: 2302-2949. Doi: https://doi.org/10.20449/jnte.v6i1.367.
[14] Kwon, Y., Kwasinski, A., & Kwasinski, A. (2019). Solar Irradiance Forecast Using Naïve Bayes Classifier Based on Publicly Available Weather Forecasting Variables. Jurnal Energi, 12(8), 1529. e-ISSN: 1996-1073.https://doi.org/10.3390/en12081529.
[15] Raza, M. Q., Aslam, M., Ahmad, S., & Nawaz, M. (2016). Energy analysis of photovoltaic thermal (PVT) system and calculation of convection heat losses. Renewable and Sustainable Energy Reviews, 59(1), 1310–1321. ISSN: 1364-0321. https://doi.org/10.1016/j.rser.2016.01.066
[16] Hasrul, Rahmat. (2021). Analisis Efisiensi Panel Surya Sebagai Energi Alternatif. SainETIn: Jurnal Sains, Energi, Teknologi, dan Industri, 5(2), 79-87. ISSN: 2548-6888 DOI:10.23917/emitor.v18i01.6251.
[17] Evita Fitri. (2023). Analisis Perbandingan Metode Regresi Linier, Random Forest Regression dan Gradient Boosted Trees Regression Method untuk Prediksi Harga Rumah. Journal of Applied Computer Science and Technology (JACOST), 4(1), ISSN: 2723-1453. https://doi.org/10.52158/jacost.491.
[18] Hamdanah, F. H., & Fitrianah, D. (2021). Analisis Performansi Algoritma Linear Regression dengan Generalized Linear Model untuk Prediksi Penjualan pada Usaha Mikra, Kecil, dan Menengah. Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI, 10(1), 23-32. DOI: https://doi.org/10.23887/janapati.v10i1.31035
[19] Adinda Inez Sang, Edi Sutoyo, & Irfan Darmawan. (2021). Analisis Data Mining untuk Klasifikasi Data Kualitas Udara DKI Jakarta Menggunakan Algoritma Decision Tree dan Support Vector Machine. e-Proceeding of Engineering, 8(5), ISSN: 2355-9365. https://openlibrarypublications.telkomuniversity.ac.id/index.php/engineering/article/view/15900
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