Prediksi Produksi Minyak Kelapa Sawit Menggunakan Metode Backpropagation Neural Network

Hijratul Aini, Haviluddin Haviluddin, Edy Budiman, Masna Wati, Novianti Puspitasari

Abstract


Artikel ini bertujuan untuk memprediksi produksi minyak kelapa sawit mentah (CPO) di PT. Perkebunan Nusantara (PTPN) XIII, Desa Long Pinang. Kabupaten Paser, Kalimantan Timur dengan menggunakan algoritma cerdas, jaringan saraf tiruan (JST) yang disebut Backpropagation Neural Network (BPNN). Data penelitian berasal dari produksi CPO periode Januari 2015 hingga Januari 2018. Pengukuran akurasi prediksi algoritma BPNN menggunakan metode mean square error (MSE).  Berdasarkan hasil percobaan, metode BPNN dengan parameter arsitektur 5-10-11-12-13-1; fungsi pembelajaran adalah trainlm; fungsi aktivasi adalah logsig dan purelin; laju pembelajaran adalah 0.7 mampu menghasilkan tingkat kesalahan prediksi yang baik dengan nilai MSE sebesar 0.0069. Hasil penelitian menunjukkan bahwa model ini dapat digunakan sebagai alternatif metode dalam memprediksi produksi CPO pada tahun 2019.

Keywords


Minyak Kelapa Sawit, Prediks,i BPNN, MSE,

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References


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DOI: http://dx.doi.org/10.30872/jsakti.v1i1.2261

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