Autoregressive Integrated Moving Average Untuk Memprediksi Kebutuhan Daya Listrik Kabupaten Lumajang

Authors

  • Fery Agung Prastyo Universitas PGRI Kanjuruhan Malang
  • Moh Ahsan Universitas PGRI Kanjuruhan Malang
  • Danang Aditya Nugraha Universitas PGRI Kanjuruhan Malang

DOI:

https://doi.org/10.37438/jimp.v6i2.323

Abstract

The electricity consumption of PLN in Lumajang Regency consists of several types of customers including external customers, internal customers and intermediate customers. Prediction or forecasting in research to forecast the electricity consumption of each type of customer uses the Autoregressive Integrated Moving Average (ARIMA) technique. The research was carried out with data collection, data analysis using Autoregressive Integrated Moving Average. The results of forcasting with the ARIMA technique are based on the results of the smallest MSE and MAPE values. The results of the parameter significance test using the ARIMA model (1,1,0) obtained MSE 23236091976 and MAPE 5.52278%, while for the ARIMA model (0,1,1) MSE 24588319865 and MAPE 6.0376302% and for the ARIMA model (1,1 ,1) obtained MSE 139049864555 and MAPE 14.021832% so that it can be concluded that the ARIMA parameter model (1,1,0).

Keyword — Forcasting, ARIMA, electric power, PLN Lumajang Regency

Author Biographies

Moh Ahsan, Universitas PGRI Kanjuruhan Malang

Teknik Informatika UNIKAMA

Danang Aditya Nugraha, Universitas PGRI Kanjuruhan Malang

TI Unikama

References

Devita P & Iffatul M (2021). Model Autoregressive Integrated Moving Average (Arima) Dalam Peramalan Nilai Harga Saham Penutup Indeks LQ45, Jurnal Ilmiah Informatika Komputer Vol 26 No 1.

Arifai, S. R., & Junaedi, L. (2020). Prediksi Permintaan Barang Bedasarkan Penjualan Menggunakan Metode Arima Box-Jenkins ( Studi Kasus : Pt . Beststamp Indonesia ). Jurnal E-Bis ( Ekonomi-Bisnis ), 4(2), 138–146.

Chang, P.-C., Wang, Y.-W. & Liu, C.-H., 2007. The Development of a Weighted Evolving Fuzzy Neural Network for PCB Sales Forecasting. Expert Systems with Applications, Volume 32, pp. 88 - 89.

Darsyah, M. D. (2016). Model Terbaik ARIMA Dan WINTER Pada Peramalan Data Saham BANK, Statistika, Vol. 4, No. 1, 6-20.

Hartati, H. (2017). Penggunaan Metode Arima Dalam Meramal Pergerakan Inflasi. Jurnal Matematika Sains Dan Teknologi, 18(1), 1–10

Hidayat, R., Suprapto,. 2012., Meminimalisasi nilai error peramalan dengan algortima extreme learning mechine. Jurnal Optimasi Sistem Industri, 11(1), 187-192.

Indra, F, C, S., 2014., Jaringan Syaraf Tiruan Memprediksi Ketersediaan Bahan Bakar Solar dengan Menggunakan Metode Backpropagation., pelita informatika budi darma Volume : VIII, Nomor 1 Desember 2014.

Pradana, M. S., Rahmalia, D., Dwi, E., & Prahastini, A. (2020). Peramalan Nilai Tukar Petani Kabupaten Lamongan dengan Arima. Jurnal Matematika, 10(2), 91–104. https://doi.org/10.24843/JMAT.2020.v10.i02.p126

Ramadanti, L., Lestari, H., Rabbani, S., Ode, L., Azim, L., Model, A., & Ispa, K.(2017). Prediksi Kejadian Penyakit Infeksi Saluran Pernapasan Akut ( ISPA ) Menggunakan Arima Model di Kota Kendari. JURNAL KESEHATAN, 01(04).

Santoso, Singgih, 2009, Business Forecasting: Metode Peramalan Bisnis Masa Kini dengan Minitab & SPSS, Elex Media Komputindo: Jakarta. Santoso, Singgi

https://web.pln.co.id/tentang-kami/profil-perusahaan

Published

2022-01-13

Issue

Section

Fakultas Teknologi Informasi