ANALISIS SPASIAL POTENSI ENERGI SURYA MENGGUNAKAN MACHINE LEARNING DAN SISTEM INFORMASI GEOGRAFIS
(1) * Rini Anggraini Pakpahan Mail (Universitas Graha Nusantara Padangsidimpuan, Indonesia)
(2) Sri Utami Kholila Mora Siregar Mail (Universitas Graha Nusantara Padangsidimpuan, Indonesia)
(3) Erwina Azizah Hasibuan Mail (Universitas Graha Nusantara Padangsidimpuan, Indonesia)
(4) Suryanti Suraja Pulungan Mail (Universitas Graha Nusantara Padangsidimpuan, Indonesia)
*corresponding author

Abstract


Provinsi Sumatera Utara memiliki potensi energi surya yang signifikan, yang dapat membantu kemajuan sumber energi terbarukan; namun, untuk memanfaatkan potensi ini sepenuhnya, penting untuk melakukan analisis spasial yang tepat untuk menentukan lokasi ideal bagi proyek energi surya. Penelitian ini bertujuan untuk mengevaluasi potensi energi surya dengan menggabungkan teknik Machine Learning dengan SIG. Data yang digunakan meliputi GHI, suhu, curah hujan, tutupan awan, elevasi topografi, kemiringan, dan pemanfaatan lahan, yang bersumber dari NASA POWER, CHIRPS, SRTM, ESA WorldCover, dan BIG. Pendekatan yang diambil dalam penelitian ini melibatkan pra-pemrosesan data, analisis spasial berbasis SIG, pembuatan model Random Forest, dan penilaian model melalui metrik seperti RMSE, MAE, MAPE, dan R², diikuti dengan pemetaan potensi energi surya menggunakan teknik klasifikasi Jenks. Hasil penelitian menunjukkan bahwa model Random Forest memberikan akurasi prediksi yang patut dipuji, mencapai R² sebesar 0,89, RMSE sebesar 0,24, MAE sebesar 0,18, dan MAPE sebesar 4,82%. Analisis pentingnya fitur mengungkapkan bahwa GHI, tutupan awan, dan curah hujan merupakan faktor utama yang memengaruhi kemampuan energi surya. Penilaian spasial menunjukkan bahwa wilayah pesisir timur Sumatera Utara memiliki potensi energi surya yang relatif lebih tinggi dibandingkan wilayah pegunungan Bukit Barisan. Hasil ini menunjukkan bahwa kombinasi Random Forest dan SIG menawarkan wawasan spasial yang berharga dan dapat secara efektif membantu dalam pengambilan keputusan yang tepat terkait perencanaan dan pembangunan PLTS.

Keywords


Energi surya; Machine Learning; Random Forest; Sistem Informasi Geografis; Potensi spasial

   

DOI

https://doi.org/10.31604/eksakta.v11i2.243–255 		      

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