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All Journal Traksi : Majalah Ilmiah Teknik Mesin Bulletin of Electrical Engineering and Informatics Rekayasa Mesin Jurnal Rekayasa Mesin Dinamisia: Jurnal Pengabdian Kepada Masyarakat Indonesian Journal of Electrical Engineering and Computer Science G-Tech : Jurnal Teknologi Terapan Jurnal VORTEKS Jurnal Derma Pengabdian Dosen Perguruan Tinggi (Jurnal DEPUTI) Elektriese: Jurnal Sains dan Teknologi Elektro Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering) Impression : Jurnal Teknologi dan Informasi JRTI (Jurnal Riset Tindakan Indonesia)
Mawardi
Program Studi Teknik Mesin, Fakultas Teknik, Universitas Al Azhar
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Prakiraan Produksi Energi Listrik PLTA di Sumatera Utara Menggunakan Adaptive Neuro-Fuzzy Inference System (ANFIS) Wisnu Alqadri Wardana; Mawardi; Yoga Tri Nugraha; Sari Novalianda
Impression : Jurnal Teknologi dan Informasi Vol. 4 No. 3 (2025): November 2025
Publisher : Lembaga Riset Ilmiah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59086/jti.v4i3.1269

Abstract

Kebutuhan energi listrik di Sumatera Utara terus mengalami fluktuasi seiring pertumbuhan penduduk dan perkembangan industri, sehingga perencanaan konsumsi listrik yang akurat sangat diperlukan untuk menjaga keandalan pasokan. Penelitian ini menerapkan Adaptive Neuro-Fuzzy Inference System (ANFIS) untuk memprediksi konsumsi energi listrik di wilayah Sumatera Utara dengan menggunakan data historis periode 2018–2024. Data aktual dibandingkan dengan hasil prediksi model untuk mengevaluasi akurasi menggunakan indikator Mean Absolute Error (MAE), Root Mean Square Error (RMSE), dan Mean Absolute Percentage Error (MAPE). Hasil evaluasi menunjukkan nilai MAE sebesar 0,00057, RMSE sebesar 0,00139, dan MAPE hanya 0,00012%, yang menandakan tingkat kesalahan prediksi sangat kecil. Proyeksi hingga tahun 2029 memperlihatkan tren konsumsi listrik yang cenderung meningkat kembali setelah mengalami penurunan pada tahun 2022–2023, dengan lonjakan signifikan pada tahun 2024. Hal ini menunjukkan bahwa ANFIS mampu mengikuti pola historis dengan baik serta memberikan hasil prakiraan yang presisi dan dapat diandalkan. Dengan demikian, metode ANFIS dapat digunakan sebagai dasar perencanaan energi jangka pendek untuk mendukung strategi penyediaan listrik yang efisien dan berkelanjutan di Sumatera Utara.   The electricity demand in North Sumatra continues to fluctuate along with population growth and industrial development, so accurate electricity consumption planning is essential to maintain supply reliability. This study applies the Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict electricity consumption in the North Sumatra region using historical data for the 2018–2024 period. Actual data is compared with the model's prediction results to evaluate accuracy using the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) indicators. The evaluation results show an MAE value of 0.00057, RMSE of 0.00139, and MAPE of only 0.00012%, indicating a very small level of prediction error. Projections until 2029 show a trend of electricity consumption that tends to increase again after experiencing a decline in 2022–2023, with a significant spike in 2024. This indicates that ANFIS is able to follow historical patterns well and provide precise and reliable forecast results. Thus, the ANFIS method can be used as a basis for short-term energy planning to support efficient and sustainable electricity supply strategies in North Sumatra.
Co-Authors Abdul Latif S Abdullah, Ilmi Adam Pangestu Adi Widarma Ali Aqil Ari Beni Santoso Bustami Syam Diana Suita Harahap Dwiyanto . Edy Syahputra Saragih Fahri , Tengku Muhammad Indriyani Indriyani Irwanto, Muhammad Jeki Armando Simanjorang Kurniawan, Rendy Mona Vionita Muammar Alfin Sagala Muhammad Fauzi Muhammad Irwanto Muhammad Irwanto Muharnif M Muzammil Jusoh Nabila Yudisha Panangian Mahadi Sihombing Prinsi Rigitta Prinsi Rigitta Rafiqah Humaira Ramadhan, Andri Rida, Rizkha Safriadi Gultom Sari Novalianda Simon Petrus Simorangkir Siregar, Masdania Zurairah Siregar, Zufri Hasrudy Sri Indah Rezkika Suherman, Suherman Sumita Wardani T. Jukdin Saktisahdan Tengku Jukdin Saktisahdan Wisnu Alqadri Wardana Yoga Tri Nugraha