cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Muhammad Hasanuddin
Contact Email
cvraskhamediagroup@gmail.com
Phone
+6282362440765
Journal Mail Official
ejournaljoeer@gmail.com
Editorial Address
Jalan Gurilla No. 2 Sidorejo, Kec. Medan Tembung 20222
Location
Kota medan,
Sumatera utara
INDONESIA
Journal of Electrical Engineering Research
Published by CV. Raskha Media Group
ISSN : -     EISSN : 3110489     DOI : https://doi.org/10.64803/joeer
Core Subject : Science, Social, Engineering,
Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Social Sciences
The Journal of Electrical Engineering Research is a peer-reviewed academic publication dedicated to the advancement of knowledge in the field of electrical engineering with ISSN: 3110-4894 (online media). It serves as a platform for high-quality research contributions addressing various aspects of electrical engineering, ranging from theoretical concepts to practical applications. The journal covers a wide array of topics including but not limited to circuit design, power systems, communications, control systems, electronics, signal processing, and renewable energy technologies. It aims to facilitate the exchange of innovative ideas and methodologies, bridging the gap between academia, industry, and research.
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 5 Documents
 1 
Search results for , issue "Vol. 1 No. 3 (2025): September 2025" : 5 Documents clear
Analisis Efisiensi Sistem Kendali Otomatis pada Jaringan Distribusi Listrik Modern Cynthia, Eka Pandu; Cynthia, Maulidania Mediawati; Cynthia, Dessy Nia
Journal of Electrical Engineering Research Vol. 1 No. 3 (2025): September 2025
Publisher : CV. Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/joeer.v1i3.18

Abstract

Penelitian ini membahas analisis efisiensi sistem kendali otomatis pada jaringan distribusi listrik modern dalam menghadapi peningkatan beban dan integrasi sumber energi terdistribusi, khususnya pembangkit fotovoltaik. Seiring meningkatnya kebutuhan energi listrik, jaringan distribusi dituntut untuk beroperasi secara lebih efisien, andal, dan responsif terhadap perubahan kondisi sistem. Metode penelitian yang digunakan adalah pendekatan kuantitatif berbasis simulasi dengan memanfaatkan perangkat lunak Electrical Transient Analyzer Program (ETAP). Model jaringan distribusi dirancang untuk merepresentasikan kondisi operasi sebelum dan sesudah penerapan sistem kendali otomatis dengan beberapa skenario, termasuk variasi beban dan penetrasi energi terbarukan. Parameter kinerja yang dianalisis meliputi rugi daya, profil tegangan, stabilitas frekuensi, serta waktu pemulihan gangguan. Hasil penelitian menunjukkan bahwa penerapan sistem kendali otomatis mampu menurunkan rugi daya secara signifikan, memperbaiki profil tegangan pada seluruh bus jaringan, serta meningkatkan keandalan sistem melalui percepatan pemulihan gangguan. Selain itu, sistem kendali otomatis terbukti efektif dalam mengelola fluktuasi daya akibat integrasi pembangkit fotovoltaik sehingga stabilitas sistem tetap terjaga. Temuan ini menunjukkan bahwa sistem kendali otomatis merupakan solusi strategis dalam pengembangan jaringan distribusi listrik yang efisien, andal, dan berkelanjutan.
Analisis Penerapan Machine Learning dalam Sistem Prediksi dan Pengambilan Keputusan Hasanuddin, Muhammad; Khodijah, Siti; Atika Rizki, Cindy; Alwi prayoga, Abil
Journal of Electrical Engineering Research Vol. 1 No. 3 (2025): September 2025
Publisher : CV. Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/joeer.v1i3.19

Abstract

Perkembangan teknologi machine learning telah mendorong pemanfaatannya secara luas dalam sistem prediksi dan pengambilan keputusan di berbagai sektor. Kemampuan machine learning dalam mengolah data berukuran besar dan kompleks memungkinkan sistem menghasilkan prediksi yang lebih akurat dan mendukung keputusan yang berbasis data. Namun, penerapan teknologi ini masih menghadapi berbagai tantangan, khususnya terkait integrasi hasil prediksi ke dalam proses pengambilan keputusan, interpretabilitas model, serta dampaknya terhadap kualitas keputusan yang dihasilkan. Penelitian ini bertujuan untuk menganalisis penerapan machine learning dalam sistem prediksi dan pengambilan keputusan secara komprehensif. Metode penelitian yang digunakan adalah pendekatan kualitatif-deskriptif melalui kajian literatur ilmiah dan analisis konseptual terhadap struktur sistem, algoritma yang digunakan, serta mekanisme integrasi keputusan. Hasil penelitian menunjukkan bahwa keberhasilan penerapan machine learning tidak hanya ditentukan oleh akurasi prediksi, tetapi juga oleh kualitas data, pemilihan model, dan peran sistem pendukung keputusan dalam menjembatani hasil prediksi dengan pengambil keputusan. Penelitian ini menegaskan pentingnya pendekatan holistik yang menempatkan machine learning sebagai alat pendukung keputusan untuk menghasilkan keputusan yang lebih efektif, transparan, dan dapat dipertanggungjawabkan.
Analisis Kinerja Algoritma Deep Learning pada Pengolahan Data Kompleks Khodijah, Siti; Rizki, Cindy Atika
Journal of Electrical Engineering Research Vol. 1 No. 3 (2025): September 2025
Publisher : CV. Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/joeer.v1i3.20

Abstract

Perkembangan pesat algoritma deep learning telah memberikan kontribusi signifikan dalam pengolahan data kompleks yang memiliki karakteristik spasial dan temporal. Namun, penerapan model deep learning tunggal sering menghadapi keterbatasan dalam menangkap pola data secara menyeluruh, khususnya pada data berdimensi tinggi dan deret waktu. Penelitian ini bertujuan untuk menganalisis efektivitas penggunaan model deep learning hybrid yang mengombinasikan Convolutional Neural Network (CNN) dan Recurrent Neural Network (RNN) dalam meningkatkan kinerja pengolahan data kompleks. Metode penelitian yang digunakan adalah pendekatan kuantitatif eksperimental dengan membandingkan performa model CNN tunggal, RNN tunggal, dan model hybrid CNN–RNN. Dataset yang digunakan merupakan data sekunder dengan karakteristik multivariat dan temporal, yang diproses melalui tahapan pra-pemrosesan, pelatihan, dan evaluasi model. Hasil penelitian menunjukkan bahwa model hybrid CNN–RNN memberikan performa terbaik dibandingkan model tunggal, ditunjukkan oleh peningkatan akurasi, presisi, recall, dan F1-score secara signifikan. Selain itu, analisis kurva loss pelatihan dan validasi menunjukkan proses pembelajaran yang stabil dan kemampuan generalisasi yang baik. Penerapan teknik regularisasi dan attention mechanism juga terbukti mampu mengurangi overfitting serta meningkatkan interpretabilitas model. Dengan demikian, model deep learning hybrid CNN–RNN memiliki potensi besar untuk diterapkan dalam berbagai domain pengolahan data kompleks, seperti analisis sinyal medis, sistem keamanan berbasis IoT, dan analisis aktivitas manusia.
Evaluation of Cloud Computing Technology in Supporting Distributed Information Systems Cynthia, Eka Pandu; Cynthia, Maulidania Mediawati; Cynthia, Dessy Nia
Journal of Electrical Engineering Research Vol. 1 No. 3 (2025): September 2025
Publisher : CV. Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/joeer.v1i3.21

Abstract

The rapid growth of distributed information systems has increased the demand for computing infrastructures that are scalable, reliable, and cost-efficient. Cloud computing has emerged as a prominent technological solution capable of addressing these demands by providing on-demand access to configurable computing resources. This study aims to evaluate the effectiveness of cloud computing technology in supporting distributed information systems by examining its capabilities, benefits, and inherent challenges. The research adopts a qualitative descriptive approach based on a systematic review and analysis of relevant academic literature, technical reports, and authoritative industry sources. The evaluation is conducted across several key dimensions, including scalability, availability and reliability, performance efficiency, security and data management, cost effectiveness, and system integration. The results indicate that cloud computing significantly enhances the operational performance of distributed information systems through elastic resource provisioning, fault tolerance mechanisms, and flexible pricing models. Cloud-based architectures also support improved interoperability and system integration through standardized interfaces and service-oriented designs. However, the findings reveal that challenges related to network latency, data privacy, regulatory compliance, and vendor dependency remain critical issues that must be carefully managed. Overall, this study concludes that cloud computing serves as a strong technological foundation for distributed information systems, provided that appropriate architectural designs, governance strategies, and resource management practices are implemented. The results contribute to a deeper understanding of cloud computing adoption and provide practical insights for organizations and system designers seeking to optimize distributed information system performance.
Integrating Artificial Intelligence in the Development of Modern Information Systems Cynthia, Dessy Nia; Cynthia, Maulidania Mediawati; Cynthia, Eka Pandu
Journal of Electrical Engineering Research Vol. 1 No. 3 (2025): September 2025
Publisher : CV. Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/joeer.v1i3.22

Abstract

The development of modern information systems requires more intelligent, adaptive, and efficient data processing capabilities due to the increasing complexity and volume of data. Artificial Intelligence (AI) has emerged as a strategic solution to enhance the ability of information systems to perform analysis, prediction, and data-driven decision support. This study aims to examine the integration of artificial intelligence in the development of modern information systems from the perspective of electrical engineering and systems engineering. The research adopts an applied research approach using a systems engineering methodology, which includes problem identification, literature review, system architecture design, simulational implementation, and performance testing and evaluation. The results indicate that modular integration of artificial intelligence significantly improves data processing efficiency, analytical accuracy, and system adaptability to changing data patterns. AI-based information systems demonstrate superior performance compared to conventional systems, particularly in supporting proactive and predictive decision-making processes. Furthermore, AI integration contributes positively to computational resource efficiency, which is a critical aspect of sustainable information system development. However, the findings also highlight that data quality and proper system architecture design are decisive factors for successful AI implementation. This research provides both conceptual and technical contributions that can serve as a reference for the development of modern AI-driven information systems and as a foundation for future studies in this field.

Page 1 of 1 | Total Record : 5

 1 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol. 2 No. 1 (2026): Januari 2026 Vol. 1 No. 3 (2025): September 2025 Vol. 1 No. 2 (2025): May 2025 Vol. 1 No. 1 (2025): January 2025