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
Articles
5 Documents
Search results for
, issue
"Vol. 2 No. 1 (2022): September 2022"
:
5 Documents
clear
<![CDATA[Implementasi Algoritma Learning Vector Quantization Untuk Pengenalan Barcode Barang ]]>
<![CDATA[Junita Gea]]>
<![CDATA[ Journal of Informatics, Electrical and Electronics Engineering Vol. 2 No. 1 (2022): September 2022 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi (FKPT) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
<![CDATA[Problems in barcode recognition during the barcode identification process. Where when the barcode has noise (damage) then the barcode becomes difficult to recognize. Learning Vector Quantization (LVQ) is a classification method in which each output unit presents a class. LVQ is used for grouping and is also one of the artificial neural networks which is a competitive learning algorithm supervised version of the Kohonen Self-Organizing Map (SOM) algorithm. The purpose of this algorithm is to approach the distribution of vector classes in order to minimize errors in classifying. LVQ learning models are trained significantly to be faster than other algorithms such as the Back Propagation Neural Network. This can summarize or reduce large datasets for a small number of vectors. Based on the results of barcode recognition testing using LVQ algorithm success with training data as much as 4 and conducted calrifikas trial of two data namely: {1,1,1,0} and {1,0,1,1}. Obtained accuracy value generated as much as 90% barcode recognized. The more training data used, LVQ will have a more complete knowledge.]]>
<![CDATA[Perancangan Aplikasi Absensi Karyawan Dengan Deteksi Wajah Menggunakan Metode Eigenface ]]>
<![CDATA[Susi Tamba]]>
<![CDATA[ Journal of Informatics, Electrical and Electronics Engineering Vol. 2 No. 1 (2022): September 2022 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi (FKPT) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
<![CDATA[Attendance is one of the most important repetitive transactions, because it is related to the productivity of employees and is one of the indicators of controlling human resources (HR) which aims to increase the potential of human resources and is used for efficiency. Current technological developments make it possible to create a system that can assist humans in recognizing a digital image. One area that is currently being developed is pattern recognition. This technology identifies a person's special physical characteristics. Examples of pattern recognition, for example, are face recognition, iris recognition, and fingerprint recognition. specific databases. Broadly speaking, the process of facial recognition is that the webcam camera captures the face. Then obtained a value of R, G, B. By using the initial processing, after that the facial processing stage is carried out using the eigenface method. In this eigenface method there are several core stages, namely: converting faces into matrices, calculating the FlatVector average, determining the eigenface values ??and carrying out the face identification process by looking for the eigenface values ??that are close to it. One of these facial recognition can be developed to become an attendance application that can be applied in the company to prevent manipulation of absenteeism by employees.]]>
<![CDATA[Implementasi Algoritma Quicksort Untuk Pembangkitan Kunci Algoitma RSA Pada Pengamanan Data Audio ]]>
<![CDATA[Muhammad Rido Hasibuan]]>
<![CDATA[ Journal of Informatics, Electrical and Electronics Engineering Vol. 2 No. 1 (2022): September 2022 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi (FKPT) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
<![CDATA[The RSA algorithm has two keys, namely the public key and the private key. This algorithm has security which lies in the difficulty in calculating discrete algorithms. Both encryption and decryption keys are integers. The RSA algorithm is a type of asymmetric cryptography algorithm, which consists of two keys, namely the public key for encryption and the private key for decryption. In the RSA algorithm, the distributed key is a public key which is not required to be kept secret while the private key is either stored or not distributed. Everyone who has the public key can perform the encryption process but the results of the encryption can only be read by the person who has the private key. To increase the strength of the algorithm, the key used to perform the encryption and decryption process will be modified first using a randomization algorithm, namely the Quicksort Algorithm. The Quicksort algorithm is a sorting algorithm developed by Tony Hoare, the average sorting performance is O (n log n) to sort n items. The purpose of using the Quicksort algorithm is to make the resulting key more difficult to guess, making it difficult for cryptanalysts to read the message or information.]]>
<![CDATA[Prediksi Flight Delay Berbasis Algoritma Neural Network ]]>
<![CDATA[Valian Yoga Pudya Ardhana]]>;
<![CDATA[Muh. Yusuf Syam]]>;
<![CDATA[Eka Fitri Ramadani]]>;
<![CDATA[Eliyah A M Sampetoding]]>;
<![CDATA[Mohammad Syahril]]>;
<![CDATA[Esther Sanda Manapa]]>;
<![CDATA[Rahmat Mardzuki]]>
<![CDATA[ Journal of Informatics, Electrical and Electronics Engineering Vol. 2 No. 1 (2022): September 2022 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi (FKPT) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
<![CDATA[Pada penerbangan komersial salah satu masalahyang tidak dapat dihindari adalah keterlambatan penerbangan. Keterlambatan penerbangan (flight delay) adalah terjadinya perbedaan waktu antara waktu keberangkatan atau kedatangan yang dijadwalkan dengan realisasi waktu keberangkatan atau kedatangan. Dengan terjadinya keterlambatan penerbangan, maka secara langsung akan menyebabkan banyak kerugian. Pada penelitian ini dibangun sebuah model untuk memprediksi keterlambatan penerbangan yang akan terjadi kedepannya dengan menggunakan metode pengenalan pola yaitu neural network. Neural network atau biasa disebut juga jaringan syaraf tiruan adalah suatu metode komputasi yang meniru sistem jaringan syaraf biologi. Metode ini menggunakan elemen perhitungan non-linear dasar yang disebut neuran yang diorganisasikan sebagai jaringan yang saling berhubungan, sehingga mirip dengan jaringan saraf manusia. Dengan menerapkan model neural network pada data latih sebanyak 20 epichs dalam waktu 11 menit. Akurasi akhir dari data latih yaitu 81,89%, sementara akurasi pada data tes yaitu 81,59%.]]>
<![CDATA[Evaluasi Usability E-Learning Universitas Qamarul Huda Menggunakan System Usability Scale (SUS) ]]>
<![CDATA[Valian Yoga Pudya Ardhana]]>
<![CDATA[ Journal of Informatics, Electrical and Electronics Engineering Vol. 2 No. 1 (2022): September 2022 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi (FKPT) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
<![CDATA[The need for e-learning to support the learning process in higher education is very high, not least at Qamarul Huda University. Qamarul Huda University's e-learning has been running but has not been evaluated and tested for its effectiveness, convenience and usefulness for users, in this case students. In this study, the quality of e-learning that will be measured by users, namely students from the University of Qamarul Huda, is based on measuring the quality of e-learning using the System Usability Scale (SUS). The usability evaluation of e-learning was carried out to collect opinions from different respondents regarding the usefulness of e-learning. In accordance with these problems, it is necessary to evaluate the usability of e-learning at Qamarul Huda University in order to determine the feasibility of the system whether e-learning is easy to use by students, how quickly students can easily understand and use e-learning, whether students still experience many problems or difficulties. in using e-learning. Based on the results of distributing questionnaires or questionnaires to 50 students where different study programs and classes as users of e-learning to get the level of student satisfaction with the e-learning system, it is obtained a score of 71.15 which is in the C grade category falls into the Acceptable range. . With this value, the Qamarul Huda University e-learning system is fairly good and feasible for students to use.]]>
