Jurnal Poros Teknik Mesin Unsrat
Vol. 5 No. 2 (2016): Jurnal Poros Teknik Mesin Unsrat

PREDIKSI KEBUTUHAN ENERGI LISTRIK SULAWESI UTARA MENGGUNAKAN ARTIFICIAL NEURAL NETWORK DAN METODE EXPONENTIAL SMOOTHING

Febry Aprily Hontong (Unknown)
Tritiya Arungpadang (Unknown)
Johan S C Neyland (Unknown)

Article Info

Publish Date
25 Oct 2016

Abstract

To predict the electrical energy need of North Sulawesi for one year ahead requires correct methods. The reliable methods used for the prediction task in this research are Artificial Neural Network and Exponential Smoothing. The prediction results using Artificial Neural Network are 110.38, 112.62, 111.56, 108.05, 107.95, 110.32, 109.90, 110.58, 113.26, 107.11, 115.60, 105.40 GWh. The prediction results using Exponential Smoothing are 112.32, 112.70, 113.07, 113.45, 113.82, 114.19, 114.57, 114.94, 115.32, 115.69, 116.07, 116.44 GWh.   Key words: Artificial Neural Network, Exponential Smoothing, Prediction, Electrical Energy Need.

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Journal Info
Jurnal Poros Teknik Mesin Unsrat
Website

Abbrev

poros

Publisher

Universitas Sam Ratulangi

Subject

Automotive Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering

Description

Jurnal Poros Teknik Mesin Unsrat (JPTMU) merupakan jurnal yang diterbitkan oleh Jurusan Teknik Mesin Fakultas Teknik Universitas Sam Ratulangi dengan periode penulisan dua terbitan per tahun pada bulan Juli dan November. Artikel yang diterbitkan mencakup bidang Teknik Mesin dan Teknik ...