Prediksi Perkembangan Jumlah Pelanggan Listrik Menurut Pelanggan Area Menggunakan Algoritma Backpropagation


  • Irfan Christian Saragih STIKOM Tunas Bangsa, Pematangsiantar
  • Dedy Hartama STIKOM Tunas Bangsa, Pematangsiantar
  • Anjar Wanto STIKOM Tunas Bangsa, Pematangsiantar
Keywords: Prediction, Electricity Customers, Area, ANN, Backpropagation

Abstract

Electricity is one of the vital needs of humanity. Without electricity, it is certain that the wheels of the economy will not be able to run properly. So that electricity customers are increasingly increasing, as they increase the needs and population of the community. Therefore this study aims to determine the development of the number of electricity customers using the backpropagation algorithm. The research data used was electricity customer data by area (customer) in North Sumatra in 2013-2017, obtained from the Central Statistics Agency of North Sumatra. This study uses 5 architectural models, namely 4-2-1, 4-3-1, 4-4-1, 4-5-1, and 4-6-1. Of the five architectural models used, one of the best architectural models is obtained 4-4-1 with an accuracy rate of 88%, epoch 716 iterations in a short amount of time, 15 seconds, with MSE Training 0,00099763 and MSE testing 0.00109935. Based on the best architectural model, this will be used to predict the Development of Electricity Customers by Area Customers in North Sumatra from 2018 to 2020

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References

B. Fachri, A. P. Windarto, and I. Parinduri, “Penerapan Backpropagation dan Analisis Sensitivitas pada Prediksi Indikator Terpenting Perusahaan Listrik,” Jurnal Edukasi dan Penelitian Informatika (JEPIN), vol. 5, no. 2, p. 202, 2019.

N. Doda and H. Mohammad, “Analisis Potensi Pengembangan Pembangkit Listrik Tenaga Mikrohidro di Kabupaten Bone Bolango,” Gorontalo Journal of Infrastructure & Science Engineering, vol. 1, no. 1, pp. 1–10, 2018.

D. Almanda and B. Kusuma, “Audit Energi Listrik Pabrik,” RESISTOR (elektRonika kEndali telekomunikaSI tenaga liSTrik kOmputeR), vol. 1, no. 1, pp. 27–36, 2018.

S. Bandri, “Prediksi Perkembangan Kebutuhan Energi Listrik di Unit PLN Kayu Aro,” Jurnal Menara Ilmu, vol. XIII, no. 6, pp. 187–205, 2020.

M. N. H. Siregar, “Model Arsitektur Artificial Neural Network pada Pelanggan Listrik Negara (PLN),” InfoTekJar (Jurnal Nasional Informatika dan Teknologi Jaringan), vol. 3, no. 1, pp. 1–5, 2018.

D. Atika, “Implementasi Algoritma Spritz dan Algoritma RC4A Dalam Skema Three-Pass Protocol Untuk Pengamanan Data,” 2018.

G. W. Bhawika et al., “Implementation of ANN for Predicting the Percentage of Illiteracy in Indonesia by Age Group,” Journal of Physics: Conference Series, vol. 1255, no. 1, pp. 1–6, 2019.

S. Setti, A. Wanto, M. Syafiq, A. Andriano, and B. K. Sihotang, “Analysis of Backpropagation Algorithms in Predicting World Internet Users,” Journal of Physics: Conference Series, vol. 1255, no. 1, pp. 1–6, 2019.

A. Wanto et al., “Model of Artificial Neural Networks in Predictions of Corn Productivity in an Effort to Overcome Imports in Indonesia,” Journal of Physics: Conference Series, vol. 1339, no. 1, pp. 1–6, 2019.

W. Saputra, P. Poningsih, M. R. Lubis, S. R. Andani, I. S. Damanik, and A. Wanto, “Analysis of Artificial Neural Network in Predicting the Fuel Consumption by Type of Power Plant,” Journal of Physics: Conference Series, vol. 1255, no. 1, pp. 1–5, 2019.

I. Parlina, A. Wanto, and A. P. Windarto, “Artificial Neural Network Pada Industri Non Migas Sebagai Langkah Menuju Revolusi Industri 4.0,” InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan, vol. 4, no. 1, pp. 155–160, 2019.

M. R. Lubis, W. Saputra, A. Wanto, S. R. Andani, and P. Poningsih, “Analysis of Artificial Neural Networks Method Backpropagation to Improve the Understanding Student in Algorithm and Programming,” Journal of Physics: Conference Series, vol. 1255, no. 1, pp. 1–6, 2019.

J. R. Saragih, D. Hartama, and A. Wanto, “Prediksi Produksi Susu Segar Di Indonesia Menggunakan Algoritma Backpropagation,” Jurnal Ilmiah Informatika, vol. 08, no. 01, pp. 58–65, 2020.

M. Situmorang, A. Wanto, and Z. M. Nasution, “Architectural Model of Backpropagation ANN for Prediction of Population-Based on Sub-Districts in Pematangsiantar City,” International Journal of Information System & Technology, vol. 3, no. 1, pp. 98–106, 2019.

N. Z. Purba, A. Wanto, and I. O. Kirana, “Implementation of ANN for Prediction of Unemployment Rate Based on Urban Village in 3 Sub-Districts of Pematangsiantar,” International Journal of Information System & Technology, vol. 3, no. 1, pp. 107–116, 2019.

BPS, “Perkembangan Pelanggan Listrik menurut Area (pelanggan), 2013 - 2017,” Badan Pusat Statistik, 2020. [Online]. Available: https://sumut.bps.go.id/statictable/2018/11/27/1266/perkembangan-pelanggan-listrik-menurut-area-pelanggan-2013---2017.html. [Accessed: 17-Jun-2020].

A. Wanto and J. T. Hardinata, “Estimasi Penduduk Miskin di Indonesia Sebagai Upaya Pengentasan Kemiskinan dalam Menghadapi Revolusi Industri 4.0,” CESS (Journal of Computer Engineering System and Science), vol. 4, no. 2, pp. 198–207, 2019.

I. S. Purba et al., “Accuracy Level of Backpropagation Algorithm to Predict Livestock Population of Simalungun Regency in Indonesia Accuracy Level of Backpropagation Algorithm to Predict Livestock Population of Simalungun Regency in Indonesia,” Journal of Physics: Conference Series, vol. 1255, no. 1, pp. 1–6, 2019.

A. Wanto et al., “Analysis of the Backpropagation Algorithm in Viewing Import Value Development Levels Based on Main Country of Origin,” Journal of Physics: Conference Series, vol. 1255, no. 1, pp. 1–6, 2019.

E. Siregar, H. Mawengkang, E. B. Nababan, and A. Wanto, “Analysis of Backpropagation Method with Sigmoid Bipolar and Linear Function in Prediction of Population Growth,” Journal of Physics: Conference Series, vol. 1255, no. 1, pp. 1–6, 2019.

S. P. Sinaga, A. Wanto, and S. Solikhun, “Implementasi Jaringan Syaraf Tiruan Resilient Backpropagation dalam Memprediksi Angka Harapan Hidup Masyarakat Sumatera Utara,” Infomedia, vol. 4, no. 2, 2019.

A. P. Windarto et al., Jaringan Saraf Tiruan: Algoritma Prediksi dan Implementasi. 2020.

A. Wanto and J. T. Hardinata, “Estimations of Indonesian poor people as poverty reduction efforts facing industrial revolution 4 . 0,” IOP Conference Series: Materials Science and Engineering, vol. 725, no. 1, pp. 1–8, 2020.

W. Saputra, J. T. Hardinata, and A. Wanto, “Resilient method in determining the best architectural model for predicting open unemployment in Indonesia,” IOP Conference Series: Materials Science and Engineering, vol. 725, no. 1, pp. 1–7, 2020.

P. Parulian et al., “Analysis of Sequential Order Incremental Methods in Predicting the Number of Victims Affected by Disasters,” Journal of Physics: Conference Series, vol. 1255, no. 1, pp. 1–6, 2019.

A. Wanto et al., “Analysis of the Accuracy Batch Training Method in Viewing Indonesian Fisheries Cultivation Company Development,” Journal of Physics: Conference Series, vol. 1255, no. 1, pp. 1–6, 2019.

T. Afriliansyah et al., “Implementation of Bayesian Regulation Algorithm for Estimation of Production Index Level Micro and Small Industry,” Journal of Physics: Conference Series, vol. 1255, no. 1, pp. 1–6, 2019.

M. K. Z. Sormin, P. Sihombing, A. Amalia, A. Wanto, D. Hartama, and D. M. Chan, “Predictions of World Population Life Expectancy Using Cyclical Order Weight / Bias,” Journal of Physics: Conference Series, vol. 1255, no. 1, pp. 1–6, 2019.


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Published
2020-06-30
How to Cite
Saragih, I., Hartama, D., & Wanto, A. (2020). Prediksi Perkembangan Jumlah Pelanggan Listrik Menurut Pelanggan Area Menggunakan Algoritma Backpropagation. Building of Informatics, Technology and Science (BITS), 2(1), 48-53. Retrieved from https://ejurnal.seminar-id.com/index.php/bits/article/view/341
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Articles