Analisis Konsumsi Energi Listrik Pelanggan Dan Biaya Pokok Produksi Penyediaan Energi Listrik dengan Machine Learning

Raditya Hari Nugraha(1*), Eko Yuwono(2), Latif Prasetyohadi(3), Yanuardhi Arief B(4), Harry Patria(5),

(1) Institut Teknologi Sepuluh Nopember
(2) Institut Teknologi Sepuluh Nopember
(3) Institut Teknologi Sepuluh Nopember
(4) Institut Teknologi Sepuluh Nopember
(5) Institut Teknologi Sepuluh Nopember
(*) Corresponding Author

Abstract


PT PLN (Persero) during the Covid-19 pandemic was one of the companies whose sales growth was affected by the decline in electricity consumption in several sectors. Another condition is that several power plant and substation construction projects have fulfilled the realization commitment to the RUPTL from PT PLN (Persero). This has resulted in PT PLN (Persero) being faced with an over supply condition between power capacity and customer usage load. Realization of sales growth until July 2021 was 4.44% (144,788 TWh). Energy consumption in July 2021 was 20.55 TWh where the growth of kWh sales in July 2021 comparing with July 2020 began to show a recovery of +1.82%. The factor that most affected business and industrial growth was the manufacturing sector in Indonesia experiencing a slowdown/contraction as reflected in the PMI (Purchasing Managers Index) which decreased from 53.5 to 40.1. Growth is strongly influenced by consumer behavior in responding to government regulations, especially related to controlling the spread of Covid-19 in Indonesia in the form of restrictions on social activities (PSBB, PPKM, or Lockdown) which have been effectively implemented since April 2020 until now. Based on the analysis of the customer's electrical energy consumption data per industrial sector, as well as using technical data on the availability of power per electrical sub-system and the cost of producing electrical energy in an area, an evaluation model will be obtained that can be used in selecting the criteria for prospective customers who will be given program offers "SEMAKIN PRODUKTIF". By using "SEMAKIN PRODUKTIF" program data modeling, it is hoped that prospective customers will be given program offers so that they can be an opportunity to increase sales growth of electrical energy which is targeted to grow 6% in December 2021

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References


. Gurenescu, 2011, Data mining : Concept and Techniques. Verlag berlin Heidelberg: Springer.

. Ian H. Witten, frank Eibe, and Mark A. Hall, Data Mining: Practical Machine Learning Tools and Techniques, 3rd ed., Asma Stephan and Burlington, Eds. United States

. Liao. 2007. Recent Advances in Data Mining of Enterprise Data: Algorithms and Application . Singapore: World Scientific Publishing

. Wu, Xindong & Kumar, Vipin. 2009. The Top Ten Algorithms in Data Mining. Boca Raton: CRC Press

. Zhang, Guazhen, Zhou, faming, etl., 2008, Knowledge creation in marketing based on data mining, Intelligent Computation Technology and Automation (ICICTA), 2008 International Conference on Page(s): 782 – 786

. Vercellis, C. 2009. Business Intelligent: Data Mining and Optimization for Decision Making. Southern Gate: John Willey & Sons Inc.

. Shukla, A. Tiwari, R., & Kala, R. 2010. Real Life Application of Soft Computing. Taylor and Francis Groups, LLC.

. Diyah, Puspitaningrum. 2006. Pengantar Jaringan Syaraf Tiruan, Penerbit Andi, Yogyakarta.

. M. DASH and H. LIU, “Feature selection for classification,” Intell. Data Anal., vol. 1, no. 1–4, pp. 131–156, 1997.

. M. Wibowo, F. Noviyanto, S. Sulaiman, and S. M. Shamsuddin, “Machine Learning Technique For Enhancing Classification Performance In Data Summarization Using Rough Set And Genetic Algorithm,” Int. J. Sci. Technol. Res., vol. 8, no. 10, pp. 1108–1119, 2019.

. A. Luque, A. Carrasco, A. Martín, and A. de las Heras, “The impact of class imbalance in classification performance metrics based on the binary confusion matrix,” Pattern Recognit., vol. 91, pp. 216–231, Jul. 2019.

. D. Kurniawan, A. Anggrawan, and H. Hairani, “Graduation Prediction System On Students Using C4.5 Algorithm,” MATRIK J. Manajemen, Tek. Inform. dan Rekayasa Komput., vol. 19, no. 2, pp. 358–365, 2020.

. S. Helal et al., “Predicting academic performance by considering student heterogeneity,” Knowledge-Based Syst., vol. 161, pp. 134–146, Dec. 2018.

. A. Khan and S. K. Ghosh, “Student performance analysis and prediction in classroom learning: A review of educational data mining studies,” Educ. Inf. Technol., vol. 26, no. 1, pp. 205–240, Jan. 2021.

. A. Namoun and A. Alshanqiti, “Predicting Student Performance Using Data Mining and Learning Analytics Techniques: A Systematic Literature Review,” Appl. Sci., vol. 11, no. 1, p. 237, Dec. 2020.

. D. H. Kamagi and S. Hansun, “Implementasi Data Mining dengan Algoritma C4.5 untuk Memprediksi Tingkat Kelulusan Mahasiswa,” J. Ultim., vol. 6, no. 1, pp. 15–20, 2014.

. X. Xu, J. Wang, H. Peng, and R. Wu, “Prediction of academic performance associated with internet usage behaviors using machine learning algorithms,” Comput. Human Behav., vol. 98, pp. 166–173, Sep. 2019.




DOI: http://dx.doi.org/10.30645/j-sakti.v6i1.424

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