Universitas Diponegoro, Indonesia
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@article{JMASIF41017, author = {Andika Pratama and Sukmawati Endah}, title = {Prediksi Beban Listrik PT. PLN (Persero) Area Semarang Menggunakan Metode Support Vector Regression}, journal = {Jurnal Masyarakat Informatika}, volume = {12}, number = {1}, year = {2021}, keywords = {Prediksi, Beban Listrik, Support Vector Regression}, abstract = {Tren kenaikan konsumsi listrik dan tidak stabilnya beban listrik puncak bulanan membuat PT. PLN (Persero) sebagai penyedia layanan listrik perlu melakukan perencanaan produksi yang matang agar dapat melakukan penjadwalan perawatan sistem tenaga listrik serta penyediaan cadangan bahan bakar untuk menjaga keberlangsungan produksi listrik. Perencanaan produksi listrik untuk keperluan penjadwalan perawatan sistem dan penyediaan cadangan bahan bakar dilakukan dengan melakukan prediksi beban listrik jangka menengah. Penelitian ini menyajikan hasil prediksi beban listrik menggunakan metode Support Vector Regression dengan menggunakan fitur prediktor yang terdiri dari beban listrik, daya tersambung, jumlah pelanggan listrik, dan PDRB-ADHB. Data yang digunakan berasal dari PT. PLN (Persero) Area Semarang sejumlah 75 data (Juni 2011 - Desember 2017) dan data dari BPS Kota Semarang sejumlah 7 data (2010 – 2016). Hasil penelitian menunjukkan nilai error menggunakan MAPE yang diperoleh sebesar 4,03 % untuk nilai parameter terbaik C = 108, ɛ = 106, dan fungsi Kernel Linear, dengan fitur prediktor terbaik adalah daya tersambung dan jumlah pelanggan listrik. Data prediksi bulan Oktober – Desember 2017 didapatkan hasil nilai error MAPE sebesar 3,0384 %.}, issn = {2777-0648}, pages = {1--9} doi = {10.14710/jmasif.12.1.41017}, url = {https://ejournal.undip.ac.id/index.php/jmasif/article/view/41017} }
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