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All Journal Journal of Computer Networks, Architecture and High Performance Computing Jurnal Riset Mahasiswa Matematika
Panjaitan, Septhia Eka Nurviranthy
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Computer Science & IT Education Mathematics

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Optimization of Electric Power Flow Analysis Using the Gauss-Seidel Method in a Numerical Approach E, Erwin; Arifin, Ilham; Panjaitan, Septhia Eka Nurviranthy; Manik, Graceya Zagita; Marsela, Wiwi; Manurung, Janter Ricardo
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 1 (2025): Article Research January 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i1.5382

Abstract

The availability of electrical energy is a fundamental requirement in modern society, supporting both daily life and industrial activities. To ensure efficient and reliable energy distribution, power flow analysis is critical. This analysis is grounded in Kirchhoff's laws, which serve as the foundation for understanding electrical circuits. Kirchhoff's Current Law (KCL) states that "the sum of electric currents entering and leaving a branch point is zero," while Kirchhoff's Voltage Law (KVL) asserts that "the sum of electromotive forces and potential drops in a closed circuit must equal zero." These laws guide the formulation and solution of equations describing power flow in electrical networks. To manage the complexity of these systems, the Gauss-Seidel method has emerged as an effective iterative technique for solving large systems of linear equations. In the context of power flow analysis, it calculates busbar voltages, active and reactive power flows, and other parameters, refining the results through successive approximations until convergence is achieved. Python is widely recognized as an ideal platform for implementing the Gauss-Seidel method due to its syntactic simplicity, flexibility, and extensive computational libraries. By leveraging Python, engineers can streamline computations and enhance the accuracy and reliability of power flow analyses. This combination of mathematical rigor and computational power not only ensures precise results but also facilitates the efficient management of complex electrical systems in modern power grids.
Optimization of Electric Power Flow Analysis Using the Gauss-Seidel Method in a Numerical Approach E, Erwin; Arifin, Ilham; Panjaitan, Septhia Eka Nurviranthy; Manik, Graceya Zagita; Marsela, Wiwi; Manurung, Janter Ricardo
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 1 (2025): Article Research January 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i1.5382

Abstract

The availability of electrical energy is a fundamental requirement in modern society, supporting both daily life and industrial activities. To ensure efficient and reliable energy distribution, power flow analysis is critical. This analysis is grounded in Kirchhoff's laws, which serve as the foundation for understanding electrical circuits. Kirchhoff's Current Law (KCL) states that "the sum of electric currents entering and leaving a branch point is zero," while Kirchhoff's Voltage Law (KVL) asserts that "the sum of electromotive forces and potential drops in a closed circuit must equal zero." These laws guide the formulation and solution of equations describing power flow in electrical networks. To manage the complexity of these systems, the Gauss-Seidel method has emerged as an effective iterative technique for solving large systems of linear equations. In the context of power flow analysis, it calculates busbar voltages, active and reactive power flows, and other parameters, refining the results through successive approximations until convergence is achieved. Python is widely recognized as an ideal platform for implementing the Gauss-Seidel method due to its syntactic simplicity, flexibility, and extensive computational libraries. By leveraging Python, engineers can streamline computations and enhance the accuracy and reliability of power flow analyses. This combination of mathematical rigor and computational power not only ensures precise results but also facilitates the efficient management of complex electrical systems in modern power grids.
Pemeringkatan Pembangunan Manusia Antarprovinsi di Indonesia Tahun 2023 Menggunakan Metode Entropy-TOPSIS Panjaitan, Septhia Eka Nurviranthy; Zifany, Syahara; Zagita Manik, Graceya; Nainggolan, Sillin; Polgasep Hutauruk, Weerstand
Jurnal Riset Mahasiswa Matematika Vol 5, No 4 (2026): Jurnal Riset Mahasiswa Matematika
Publisher : Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/jrmm.v5i4.37540

Abstract

AbstrakPenelitian ini bertujuan menentukan peringkat pembangunan manusia pada 34 provinsi di Indonesia menggunakan metode Entropy–TOPSIS berdasarkan tujuh kriteria: UHH, HLS, RLS, PPK, PPM, TPT, dan GR. Metode Entropy digunakan untuk memperoleh bobot kriteria secara objektif, sedangkan TOPSIS digunakan untuk menentukan peringkat alternatif. Hasil menunjukkan bahwa Provinsi Bali (A17) menempati peringkat tertinggi dengan nilai preferensi 0.9623, sedangkan Provinsi Papua (A34) berada pada peringkat terendah dengan nilai 0.2186. Bobot kriteria didominasi oleh indikator ekonomi, khususnya PPM (0.59735) dan TPT (0.23498), diikuti PPK (0.09210). Hal ini menunjukkan bahwa variasi kemiskinan dan pengangguran menjadi faktor utama dalam membedakan tingkat pembangunan antarprovinsi. Analisis sensitivitas menunjukkan bahwa hasil pemeringkatan relatif stabil terhadap perubahan bobot kriteria terutama pada peringkat atas dan bawah. Temuan ini menegaskan bahwa faktor ekonomi menjadi penentu utama dalam pembangunan manusia antarprovinsi di Indonesia.
Co-Authors Erwin E Ilham Arifin Manik, Graceya Zagita Manurung, Janter Ricardo Marsela, Wiwi Nainggolan, Sillin Polgasep Hutauruk, Weerstand Zagita Manik, Graceya Zifany, Syahara