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Performance Enhancement of Distribution System using Grey Wolf Optimizer for Capacitor Placement and Sizing Considering Load Variations Sahat Siagian; Yoakim Simamora; Desman Jonto Sinaga; Mas Aly Afandi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): June 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.7482

Abstract

This research introduces a Grey Wolf Optimizer (GWO) strategy for determining the optimal placement and sizing of shunt capacitors in radial distribution networks under different load scenarios. The task is structured as a multi-scenario optimization model aimed at reducing active power loss, voltage deviation, and annual compensation expenses, all while adhering to bus voltage and line current limitations. Unlike traditional methods that focus on capacitor allocation for a single operating condition, this approach explicitly accounts for light, normal, and heavy load conditions to ensure solutions are effective throughout daily demand fluctuations. The method is tested on the IEEE 33-bus distribution system and benchmarked against several existing metaheuristic techniques. Findings reveal that GWO consistently provides viable capacitor configurations across all load conditions and offers enhanced technical and economic outcomes. During normal load conditions, the method decreases active power loss from 202.69 kW to 131.83 kW, and under heavy load, it achieves the highest annual net savings of $97,588. In summary, the results suggest that GWO is a reliable and economical solution for reactive power compensation planning in distribution systems with variable loads.
Classification of Teacher Certification Eligibility Using the C4.5 Algorithm Agnes Irene Silitonga; Mismauli Nainggolan; Tasya Arcinta; Yoakim Simamora; Ferry Indra Sakti H Sinaga
International Journal of Information System and Innovative Technology Vol. 5 No. 1 (2026): June
Publisher : Geviva Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63322/2ar4tf74

Abstract

Determining teacher certification eligibility is a crucial process in improving the quality of education. The C4.5 algorithm is a decision tree-based machine learning algorithm. This algorithm offers a systematic approach to data analysis and provides accurate results for decision-making. This study aims to develop a predictive model using the C4.5 algorithm to assess teacher certification eligibility based on relevant data such as teaching experience, education, and competency exam results. This study reveals that the C4.5 algorithm is capable of producing transparent decision rules and enabling clear interpretation of the results. This research is expected to make a significant contribution to supporting a more objective and efficient teacher certification policy.
Analisis Algoritma J48 Pada Pengambilan Keputusan Pemberian Pinjaman Kepada Calon Nasabah Agnes Irene Silitonga; Lukas Ginting; Enjelina Sinaga; Elson Zega; Samuel Sembiring; Yoakim Simamora
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 8 No. 2 (2024): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol8No2.pp281-293

Abstract

This research aims to analyze the stages of decision making for granting loans to prospective customers using the J48 Algorithm. Using the "Loan-Approval-Prediction-Dataset" dataset obtained from Kaggle, this research will build a decision tree model that can provide insight into the key factors that influence the decision. It is hoped that the results of this research can contribute to financial institutions in increasing accuracy, efficiency and objectivity in the credit evaluation process, as well as helping prospective customers understand the factors that need to be considered to increase their chances of loan approval.