Autoregressive Integrated Moving Average Untuk Memprediksi Kebutuhan Daya Listrik Kabupaten Lumajang

Fery Agung Prastyo, Moh Ahsan, Danang Aditya Nugraha

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).


Keywords


Forcasting; ARIMA; daya listrik; PLN Kabupaten Lumajang; electric power; PLN Lumajang Regency

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), 138146.

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), 110

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), 91104. 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




DOI: http://dx.doi.org/10.51213/jimp.v6i2.323

Copyright (c) 2022 J I M P - Jurnal Informatika Merdeka Pasuruan

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.



Link Nonton GratisTonton GratisUnited GamingSV388Scatter HitamBonanza CandylandFundacion RapalaFlowin77Link Alternatif Flowin77flowin77 deposit qrisDunia FaunaFakta SehariTren HarapanGadgetkanGosipliciousiNewsComplexiNewsFootballPollux TierFoomer OfficialCommon SightJurnal TempoRuang MistisiNews CombatSitus Slot DemoLove Food Ready MealsPetite PaulinaLink Scatter HitamLink Slot Gacor ThailandBonanza CandylandSV388Bandar TogelLive Casino Baccarat