Metode Fuzzy Subtractive Clustering Dalam Pengelompokkan Penggunaan Energi Listrik Rumah Tangga

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Nurul Ramadhanti Hikmiyah
Riki Ruli A. Siregar
Budi Prayitno
Dine Tiara Kusuma
Novi Gusti Pahiyanti


The use of electricity in household sector has increased, especially during the Covid-19 pandemic. The large number of activities carried out in home such as Work from Home, online schools, and online businesses caused difficulty to monitor the electricity consumption. The absence of electricity usage provisions affects the electricity monitoring process. Hence it takes a real time monitoring application of electricity consumption. Fuzzy subtractive clustering is an unsupervised method to form the number and center of clusters according to data conditions. This method serves to classify the household electricity users with the parameters used, is the amount of usage in rupiah and electric power. The grouping results from this method help users to monitoring electricity consumption in real time. The output describes the level of high, medium and low user electricity consumption. Based on the test results, the best Silhouette Coefficient value is 0.8322535 and three clusters are formed, with an accept ratio is 0.5, a reject ratio of 0.15, a radius of 1.7 and a squash factor of 0.5 hence a high level of use is obtained with an average value of the number of uses in IDR 655,993, power 2757 VA, medium level 240,553, 1071 VA and low level 46,479, 675 VA


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Hikmiyah, N., Siregar, R. R., Prayitno, B., Kusuma, D., & Pahiyanti, N. (2021). Metode Fuzzy Subtractive Clustering Dalam Pengelompokkan Penggunaan Energi Listrik Rumah Tangga. Petir, 14(2), 269 - 279.


[1] J. Lloret, J. Tomas, A. Canovas, and L. Parra, “An Integrated IoT Architecture for Smart Metering,” IEEE Commun. Mag., vol. 54, no. 12, pp. 50–57, Dec. 2016, doi: 10.1109/MCOM.2016.1600647CM.
[2] “KWh Meter Smart Card Model Token For Electrical Energy Monitoring,” MATEC Web Conf., vol. 218, p. 03002, 2018, doi: 10.1051/matecconf/201821803002.
[3] R. R. A. Siregar, Y. K. Ningsih, P. Palupiningsih, and B. Prayitno, “Smart kWh Meter Model with Energy Control and Monitoring on Low Voltage Electricity,” pp. 227–232, Dec. 2020, doi: 10.2991/AER.K.201221.039.
[4] R. F. Ningrum, R. R. A. Siregar, and D. Rusjdi, “Fuzzy mamdani logic inference model in the loading of distribution substation transformer SCADA system,” IAES Int. J. Artif. Intell., vol. 10, no. 2, pp. 298–305, Jun. 2021, doi: 10.11591/IJAI.V10.I2.PP298-305.
[5] Samudin Harsanto, “Statistik Listrik ‘Electric Statistic’ 2013-2018,” BPS-Statistic Indonesia, 2018.
[6] O. : Akhyar and Zaini, “Building Automation System (BAS) Menggunakan Smart Metering Dan Koneksi Internet,” J. Teknoif, vol. 6, no. 2, 2018, doi: 10.21063/JTIF.2018.V6.2.
[7] P. Catur Siswipraptini, R. Nur Aziza, M. Asura, R. R. A. Siregar, and M. Abdul Jabar, “K-Means Clustering Algorithm for Smart Home Automation,” 2020 8th Int. Conf. Control. Mechatronics Autom. ICCMA 2020, pp. 207–211, Nov. 2020, doi: 10.1109/ICCMA51325.2020.9301506.
[8] R. Pereira, A. Fagundes, R. Melício, V. M. F. Mendes, J. Figueiredo, and J. C. Quadrado, “Fuzzy Subtractive Clustering Technique Applied to Demand Response in a Smart Grid Scope,” Procedia Technol., vol. 17, pp. 478–486, 2014, doi: 10.1016/j.protcy.2014.10.256.
[9] M. Abbas and D. Zhang, “A smart fault detection approach for PV modules using Adaptive Neuro-Fuzzy Inference framework,” Energy Reports, vol. 7, pp. 2962–2975, 2021, doi: 10.1016/j.egyr.2021.04.059.
[10] U. Mohan Rao, Y. R. Sood, and R. K. Jarial, “Subtractive clustering fuzzy expert system for engineering applications,” Procedia Comput. Sci., vol. 48, no. C, pp. 77–83, 2015, doi: 10.1016/j.procs.2015.04.153.
[11] G. U. Kaya, O. Erkaymaz, and Z. Sarac, “Optimization of digital holographic setup by a fuzzy logic prediction system,” Expert Syst. Appl., vol. 56, 2016, doi: 10.1016/j.eswa.2016.03.019.
[12] R. F. Ningrum, R. R. A. Siregar, and D. Rusjdi, “Penerapan Sistem SCADA Dalam Perancangan Model Inferensi Logika Fuzzy Mamdani Pada Pembebanan Trafo Gardu Distribusi,” Petir J. Pengkaj. dan Penerapan Tek. Inform., vol. 13, no. 2, pp. 110–118, Sep. 2020, doi: 10.33322/PETIR.V13I2.1001.
[13] “Implementasi Metode Fuzzy Subtractive Clustering Untuk Pengelompokan Data Potensi Kebakaran Hutan/Lahan | Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer.” [Online]. Available: [Accessed: 11-Sep-2021].
[14] Y. Arvio, I. B. Sangadji, H. Sikumbang, and M. D. Anjarwati, “Pendekatan Implementasi Model Substractive Clustering Dalam Memetakan Dan Klasifikasi Data Perilaku Konsumen Listrik Tegangan Rendah Studi Kasus : Pelanggan PT PLN (Persero) UP3 Cengkareng,” Petir J. Pengkaj. dan Penerapan Tek. Inform., vol. 12, no. 2, pp. 251–261, Sep. 2019, doi: 10.33322/PETIR.V12I2.553.
[15] P. C. Siswipraptini, R. Nur Aziza, I. B. M. Sangadji, I. Indrianto, and R. R. A. Siregar, “Automated Smart Home Controller Based on Adaptive Linear Neural Network,” 2019 IEEE 7th Int. Conf. Control. Mechatronics Autom. ICCMA 2019, pp. 423–427, 2019, doi: 10.1109/ICCMA46720.2019.8988733.

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