Published: 2024-01-01

Penerapan Metode Extreme Learning Machine (ELM) untuk Memprediksi Hasil Sensor EWS Trafo

DOI: 10.35870/jtik.v8i1.1243

Rolisa Apalem
  • Rolisa Apalem: Satya Wacana Christian University , Indonesia . image/svg+xml
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Abstract

The Early Warning System (EWS) Trafo is a continuous monitoring tool for transformers that provides warnings when anomalies are detected, aiming to prevent explosions. This device applies artificial intelligence and machine learning technologies to monitor and predict the real-time condition of transformers using sensor data collected by the tool. This research aims to predict the condition of transformers based on the EWS Trafo sensor results using the Extreme Learning Machine (ELM) method. The study investigates the effectiveness of the ELM method in predicting transformer conditions. Based on the research results obtained from several combinations of data training: testing with different numbers of hidden layers, the lowest Mean Absolute Percentage Error (MAPE) value was found in the combination of 40% training data and 60% testing data, out of a total of 470 data points, with 20 hidden layers, at 23.1125%. Thus, it can be concluded that the Extreme Learning Machine (ELM) method is effective in predicting the condition of transformers.

Keywords

Early Warning System Trafo (EWS Trafo) ; Extreme Learning Machine (ELM) ; Distribution Transformer ; Machine Learning ; Prediction

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Apalem, R. (2024). Penerapan Metode Extreme Learning Machine (ELM) untuk Memprediksi Hasil Sensor EWS Trafo. Jurnal JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 8(1), 30-39. https://doi.org/10.35870/jtik.v8i1.1243
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This article has been peer-reviewed and published in the Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi). The content is available under the terms of the Creative Commons Attribution 4.0 International License.

  • Issue: Vol. 8 No. 1 (2024)

  • Section: Computer & Communication Science

  • Published: %750 %e, %2024

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Author Biographies
Rolisa Apalem
Google Scholar ResearchGate ORCID

Program Studi Teknik Informatika, Fakultas Teknologi Informasi, Universitas Kristen Satya Wacana, Kota Salatiga, Jawa Tengah, Indonesia

References
  • Statistik PLN 2021. 2021. PT PLN (Persero), Jakarta. Available at: https://web.pln.co.id/statics/uploads/2022/03/Statistik-PLN-2021-Unaudited-21.2.22.pdf.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Soedjarwanto, N. and Nama, G.F., 2019. Monitoring Arus, Tegangan dan Daya pada Transformator Distribusi 20 KV Menggunakan Teknologi Internet of Things. Jurnal EECCIS, 13(3). pp. 128–133. Available: https://jurnaleeccis.ub.ac.id/.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Nugraha, I.M.A. and Desnanjaya, I.G.M.N., 2021. Penempatan dan Pemilihan Kapasitas Transformator Distribusi Secara Optimal Pada Penyulang Perumnas. Jurnal RESISTOR (Rekayasa Sistem Komputer), 4(1), pp.33-44. DOI: https://doi.org/10.31598/jurnalresistor.v4i1.722.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Ir Denny Richard Pattiapon, M.T., 2017. TINJAUAN PENGAMAN GARDU DISTRIBUSI 37A TERHADAP LEDAKAN TRAFO DI SKIP DALAM PALDAM. JURNAL SIMETRIK, 7(2), pp. 31–37 DOI: https://doi.org/10.31959/js.v7i2.47.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • de Fretes, R., 2022. Analisis Penyebab Kerusakan Transformator menggunakan Metode RCA (Fishbone diagram and 5-Why Analysis) di PT. PLN (Persero) Kantor Pelayanan Kiandarat. ARIKA, 16(2), pp.117-124. DOI: https://doi.org/10.30598/arika.2022.16.2.117 .
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Zebua, O., Endah, K., Syaiful, A. and Aldiansyah, A., 2021. Rancang Bangun Alat Monitoring Ketidakseimbangan Beban Transformator Distribusi Berbasis Internet of Things. Electrician–Jurnal Rekayasa dan Teknologi Elektro, 15(2), pp.146-152. .
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Madjid, A.R. and Suprianto, B., 2019. Prototype Monitoring Arus, dan Suhu pada Transformator Distribusi Berbasis Internet Of Things (IoT). Jurnal Teknik Elektro, 8(1), pp. 111–119.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Prasetyo, B.E., Putra, W.H.N., Syauqy, D., Bhawiyuga, A., Wibowo, S.S., Ronilaya, F., Siradjuddin, I. and Adhisuwignjo, S., 2020. Sistem Monitoring Trafo Distribusi PT. PLN (Persero) berbasis IoT. Jurnal Teknologi Informasi dan Ilmu Komputer, 7(1), pp.205-210. DOI: https://doi.org/10.25126/jtiik.202071951.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Id, I.D., 2021. Machine Learning: Teori, Studi Kasus dan Implementasi Menggunakan Python (Vol. 1). Unri Press.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Faizal, R., Setiawan, B.D. and Cholisoddin, I., 2019. Prediksi Nilai Cryptocurrency Bitcoin menggunakan Algoritme Extreme Learning Machine (ELM). Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 3(5), pp.4226-4233.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Huang, G.B., Zhu, Q.Y. and Siew, C.K., 2004, July. Extreme learning machine: a new learning scheme of feedforward neural networks. In 2004 IEEE international joint conference on neural networks (IEEE Cat. No. 04CH37541) (Vol. 2, pp. 985-990). IEEE. DOI: https://doi.org/10.1109/IJCNN.2004.1380068.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Huang, G.B., Zhu, Q.Y. and Siew, C.K., 2006. Extreme learning machine: theory and applications. Neurocomputing, 70(1-3), pp.489-501. DOI: https://doi.org/10.1016/j.neucom.2005.12.126.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Huang, G.B., Zhou, H., Ding, X. and Zhang, R., 2011. Extreme learning machine for regression and multiclass classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 42(2), pp.513-529. DOI: https://doi.org/10.1109/TSMCB.2011.2168604.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Adiatmaja, P.B., Setiawan, B.D. and Wihandika, R.C., 2019. Peramalan Harga Cabai Merah Besar Wilayah Jawa Timur Menggunakan Metode Extreme Learning Machine. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 3(6), pp.5444-5449.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Izati, N.A., Warsito, B. and Widiharih, T., 2019. Prediksi Harga Emas Menggunakan Feed Forward Neural Network Dengan Metode Extreme Learning Machine. Jurnal Gaussian, 8(2), pp.171-183. DOI: https://doi.org/10.14710/j.gauss.8.2.171-183.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Kertayuga, D., Santoso, E. and Hidayat, N., 2021. Prediksi Nilai Ekspor Impor Migas Dan Non-Migas Indonesia Menggunakan Extreme Learning Machine (ELM). Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 5(6), pp.2792-2800.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
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