Journal of Informatics, Electrical and Electronics Engineering
Fokus kajian Journal of Informatics, Electrical and Electronics Engineering, yaitu: 1. Control System, 2. Artificial Intelligence, 3. Informatics Engineering, 4. Electronics, 5. Advanced energy material, 6. Automatic power control, 7. Battery technology, 8. Distributed generation, 9. Distribution system, 10. Electric power generation, 11. Electric vehicle, 12. Electrical machine, 13. Energy optimization, 14. Energy conversion, 15. Energy efficiency, 16. Energy exploitation, 17. Energy exploration, 18. Energy management, 19. Energy mitigation, 20. Energy storage, 21. Energy system, 22. Fault diagnostics, 23. Green energy, 24. Green technology, 25. High voltage, 26. Insulation technology, 27. Intelligent power optimization, 28. Monitoring operation, 29. Motor drives, 30. Natural energy source, 31. Power control, 32. Power data transaction, 33. Power economic, 34. Power electronics, 35. Power engineering, 36. Power generation, 37. Power optimization, 38. Power quality, 39. Power system analysis, 40. Power system information, 41. Power system optimization, 42. Protection system, 43. Renewable energy, 44. SCADA, 45. Security operation, 46. Smart grid, 47. Stability system, 48. Storage system, and 49. Transmission system
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<![CDATA[Implementasi Metode OCRA Penentuan Dosen Terbaik Dimasa Pandemi Covid-19 ]]>
<![CDATA[Tanjung, Dewi Maulida Sari]]>;
<![CDATA[Al-Adawiyah, Robiah]]>;
<![CDATA[Saputra, Imam]]>
<![CDATA[ Journal of Informatics, Electrical and Electronics Engineering Vol. 3 No. 1 (2023): September 2023 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi (FKPT) ]]>
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DOI: 10.47065/jieee.v3i1.1607
<![CDATA[Each lecturer has a different way of teaching, some are preferred by students and some are not liked by students. A lecturer is required to improve his performance in teaching and imparting the knowledge he has to his students, so there are several requirements that are used as criteria in determining the best lecturers including Doc Scopus, Position, Competence, Age, and Warning Letters. So that in this study a money system was made using a decision support system that could determine the best lecturers during the covid-19 pandemic. In its determination, data such as criteria, weights and alternatives are needed. So, in this study using the OCRA (Optional Competitiveness Rating Analysis) method to determine the Best Lecturer through data such as alternatives and criteria, it can be concluded that the second alternative (A2) is Kennedi as the best Lecturer with the best preference value of 3,837.]]>
<![CDATA[Implementasi Metode MOORA Dalam Pemilihan Mahasiswa Berprestasi Dimasa Pandemi Covid-19 ]]>
<![CDATA[Halawa, Advent]]>;
<![CDATA[Malango, Monika Stefani]]>;
<![CDATA[Syahrizal, Muhammad]]>
<![CDATA[ Journal of Informatics, Electrical and Electronics Engineering Vol. 3 No. 1 (2023): September 2023 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi (FKPT) ]]>
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DOI: 10.47065/jieee.v3i1.1608
<![CDATA[Selection Of Outstanding Students is one of the activities usually carried out by every university or college in order to determine the level of performance of each student. In this case, the write conducts research on the Selection Of Outstanding Students with the aim of giving appreciation to students who have the best grades so that they become hope for future developments. In this study the authors use the MOORA (Multi-Objektive Optimization by Ratio Analysis) method as a Decision Support Sistem (DSS) in order to produce an objektive and accurate decision. This research was carried out during the covid-19 pandemic, which is a virus thet is hitting human health, characterized by coughs, flu, fever, no appetite, and so on. However, this condition does not rule out the possibility of students to exel, some strudents who become alternatives in the selection of outstanding students with criteria including GPA, Active Organization, Englis Ability, Achievement and warning letters. So as to produce the best reference value on alternative A3 on behalf of Viktor Zai with a value of 0.4522.]]>
<![CDATA[Implementasi Metode MAUT Dalam Pemilihan Aplikasi Pembuatan Media Pembelajaran Dimasa Pandemi Covid 19 ]]>
<![CDATA[Pradana, Ari]]>;
<![CDATA[Manik, Lastri]]>;
<![CDATA[Syahrizal, Muhammad]]>
<![CDATA[ Journal of Informatics, Electrical and Electronics Engineering Vol. 3 No. 1 (2023): September 2023 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi (FKPT) ]]>
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DOI: 10.47065/jieee.v3i1.1609
<![CDATA[Corona Virus disiase 2019 (covid 19) is an infectious disease caused by SARS-CoV-2 a type of Corona virus. This virus is transmitted through physical contact and causes symptoms of respiratory problems, colds, fever, dry cough, and attacks the human physical immunity. covid 19 has disrupted the activities of daily human life, including in the field of education. To prevent the spread of covid 19 the government proposes that during the covid 19 pandemic the teaching and learning process takes place online or online. In the online learning process so that students are more active, creative and enthusiastic, a system is needed decision support that can determine and choose the application of learning media during the covid 19 pandemic, a decision support system that can solve the problem using the MAUT (Multi Attribute Utility Theory) method and produce an appropriate decision based on the results of the data obtained such as alternatives and the criteria so as to produce a decision, namely the application of the best learning media in the era of covid 19, namely the Sony Vegas Pro application with a score of 0.9025.]]>
<![CDATA[Analisis Penerapan Jaringan Saraf Tiruan Backpropagation dalam Memprediksi Penjualan Produk Es Kristal ]]>
<![CDATA[Anjani, Dila Dwi]]>;
<![CDATA[Prakasiwi, Cindy]]>;
<![CDATA[Windarto, Agus Perdana]]>
<![CDATA[ Journal of Informatics, Electrical and Electronics Engineering Vol. 3 No. 1 (2023): September 2023 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi (FKPT) ]]>
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DOI: 10.47065/jieee.v3i1.1610
<![CDATA[Ice Crystal Inti’s factory is the only producer of the ice crystals in Pematangsiantar. Untill now, the factory uses a simple system for recording sales, which creates difficulties in predicting sales. Preeiction calculation manually has a fairly high level of risk and hampers the sales performance process. For this reason, the factory need a system that can calculate sales predicting for ice crystal products and reduce that risk of lost. This study aims to make predictions using Artificial Intelligence with the Backpropagation algirithm. The data used is sourced from the Ice Crystal Inti’s Factory in Pematangsiantar for the 2020-2021 period. The process is done by dividing the training data and testing data to obtain the best architectural model. The training architecture model used to predict sales of ice crystal products is : 11-2-1; 11-25-1; 11-50-1; 11-50-75-1; dan 11-100-1. From a series of trials, the best pattern of the backpropagation architecture is 11-2-1 with a Means Square Error of 0.0009997950, an epoch of 414557, and an accuracy of 75% which will then be used to make predictions.]]>
<![CDATA[Model Arsitektur Backpropagation Dalam Meramalkan Jumlah Tindak Pidana Menurut Kepolisian Daerah Sumatera Utara ]]>
<![CDATA[Annisa, Liza]]>;
<![CDATA[Isnaini, Alvina]]>;
<![CDATA[Windarto, Agus Perdana]]>
<![CDATA[ Journal of Informatics, Electrical and Electronics Engineering Vol. 3 No. 1 (2023): September 2023 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi (FKPT) ]]>
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DOI: 10.47065/jieee.v3i1.1611
<![CDATA[A criminal act is a violation that can involve communities and is related to the law. The purpose of this study is to create the best architectural model using the backpropagation method where the best model can be used to predict the number of criminal acts according to the regional police in North Sumatra. The dataset used is sourced from the Central Statistics Agency of North Sumatra on the number of criminal acts in 2001-2020. The method used is the backpropagation method. The analysis process uses the help of Matlab 6.1 software. From the trials conducted using several architectural models 9-4-1, 9-8-1, 9-12-1, 9-16-1, and 9-20-1, the best architectural model is the 9-8 model. -1 with 90% correctness accuracy and MSE 0.0009992573.]]>
