PENERAPAN METODE EXPONENTIAL MOVING AVERAGE PADA PERAMALAN PENGGUNAAN AIR DI PDAM KOTA GORONTALO

WA SALMI, ISMAIL DJAKARIA, RESMAWAN RESMAWAN

Abstract


Facing the dry season, it is probable that there is a lack of water or excess distribution at one point during distribution to every house that uses PDAM water every day. This will result in community instability in using water and inaccurate users. Therefore, forecasting of the amount of water used in PDAM Kota Gorontalo for the next period. The method used to forecast is the Exponential Moving Average method. Criteria in determining the best method is based on the value of Mean Absolute Deviation and Mean Absolute Percentage Error. After forecasting each smoothing constant is compared, the best model. in predicting the amount of water use in PDAM Kota Gorontalo is an Exponential Moving Average with a smoothing constant of 0.15 because it has the smallest MAD and MAPE values.

Keywords


Exponential Moving Average;Mean Absolute Deviation; MAPE

Full Text:

PDF

References


Anggono, A. H. (2019), “Investment Strategy Based on Exponential Moving Average and Count Back Line,” 8, 153–161.

Heizer, Jay. Render, Barry. Munson, C. (2017), Operations management: sustainability and supply chain management, Journal of purchasing and supply management.

Kurniawati, E. (2009), “Analisis Rasio Keuangan untuk Menilai Kinerja Perusahaan Daerah Air Minum,” Buletin Pascasarjana Universitas Hasanuddin, 6, 112–122.

Makridakis, S. G., Wheelwright, S. C., and Hyndman, R. J. (1997), Forecasting: Methods and Applications, Wiley.

Prapcoyo, H. (2018), “PERAMALAN JUMLAH MAHASISWA MENGGUNAKAN MOVING AVERAGE,” Telematika, 15, 67. https://doi.org/10.31315/telematika.v15i1.3069.

Putro, B., Furqon, M. T., and Wijoyo, S. H. (2018), “Prediksi Jumlah Kebutuhan Pemakaian Air Menggunakan Metode Exponential Smoothing (Studi Kasus : PDAM Kota Malang),” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2, 4679–4686.

Setiawan, D. A., Wahyuningsih, S., and Goejantoro, R. (2019), “Peramalan Produksi Kelapa Sawit Menggunakan Winter’s dan Pegel’s Exponential Smoothing dengan Pemantauan Tracking Signal,” Jambura Journal of Mathematics, 2, 1–14. https://doi.org/10.34312/jjom.v2i1.2320.

Sucipto, L., and Syaharuddin, S. (2018), “Konstruksi Forecasting System Multi-Model untuk pemodelan matematika pada peramalan Indeks Pembangunan Manusia Provinsi Nusa Tenggara Barat,” Register: Jurnal Ilmiah Teknologi Sistem Informasi, 4, 114. https://doi.org/10.26594/register.v4i2.1263.

Sundari, S. S., Susanto, W., and Revianti (2019), “Sistem Peramalan Persediaan Barang Dengan Weight Moving Average Di Toko The Kids 24,” in Konferensi Nasional Sistem & Informatika 2015, STMIK STIKOM.

Widodo, D., and Hansun, S. (2016), “Implementasi Simple Moving Average dan Exponential Moving Average dalam Menentukan Tren Harga Saham Perusahaan,” Jurnal ULTIMATICS, 7, 113–124. https://doi.org/10.31937/ti.v7i2.354.

Yaffee, R. A., and McGee, M. (2000), An Introduction to Time Series Analysis and Forecasting: With Applications of SAS® and SPSS®, Academic Press.




DOI: https://doi.org/10.34312/jjps.v1i2.7152

Refbacks

  • There are currently no refbacks.


Copyright (c) 2020 Jambura Journal of Probability and Statistics

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

Editorial Office of Jambura Journal of Probability and Statistics:
 
Department of Statistics, 3rd Floor Faculty of Mathematics and Natural Sciences, Universitas Negeri Gorontalo
Jl. Prof. Dr. Ing. B.J Habibie, Tilongkabila Kabupaten Bone Bolango, 96119
Telp: +6285398740008 (Call/SMS/WA)
E-mail: redaksi.jjps@ung.ac.id