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All Journal Proceedings of KNASTIK Jurnal Ilmu Komputer dan Agri-Informatika JPSriwijaya JOIV : International Journal on Informatics Visualization Jurnal Teknoinfo MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Krea-TIF: Jurnal Teknik Informatika Journal of Science Innovare Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika IJEEIT : International Journal of Electrical Engineering and Information Technology Building of Informatics, Technology and Science Jurnal Leverage, Engagement, Empowerment of Community (LeECOM) JTIK (Jurnal Teknik Informatika Kaputama) Journal Social Science And Technology For Community Service Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (Senastindo) BEES: Bulletin of Electrical and Electronics Engineering Jurnal Aplikasi Bisnis dan Komputer J-Icon : Jurnal Komputer dan Informatika Bulletin of Informatics and Data Science CHAIN: Journal of Computer Technology, Computer Engineering and Informatics Journal of Artificial Intelligence and Technology Information JIPETIK:Jurnal Ilmiah Penelitian Teknologi Informasi & Komputer Prosiding SeNTIK STI&K
Sufiatul Maryana
Universitas Pakuan
Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Medicine & Pharmacology Neuroscience Public Health Social Sciences Other

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IMPLEMENTASI ALGORITMA AES (ADVANCE ENCRYPTION STANDARD) RIJNDAEL PADA APLIKASI KEAMANAN DATA Prajuhana Putra, Agung; Herfina, Herfina; Maryana, Sufiatul; Setiawan, Andrian
JIPETIK:Jurnal Ilmiah Penelitian Teknologi Informasi & Komputer Vol 1, No 2 (2020): JIPETIK : Jurnal Ilmiah Pendidikan Teknologi Informasi & Komputer
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/jipetik.v1i2.4599

Abstract

AES Rijndael algorithm is a modern cryptographic algorithm published by NIST (National Institute of Standards and Technology) in 2001 using block cipher mode and using symmetric keys. AES Rijndael as a substitute for DES (Data Encryption Standard) algorithm whose use began in 1977 and has ended. The length of the block cipher used is 128 bits and variations in key length are 128 bits, 192 bits and 256 bits. AES Rijndael's algorithm has a reliability that is the simplicity of the bytes transformation process so that it can streamline encryption and decryption, and has high security.Implementation of the AES Rijndael algorithm will be done on Android devices to secure digital files, which can be used for all types of file types. The parameters used in the analysis are testing of the encryption and decryption processing time, changes in size and bits of the encrypted and decrypted files, then proving the strength of the key length against the robustness of the AES Rijndael algorithm as a symmetric algorithm.
Automatic Door Access Model Based on Face Recognition using Convolutional Neural Network Tjut Awaliyah Zuraiyah; Sufiatul Maryana; Asep Kohar
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 1 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i1.2350

Abstract

Automatic door access technology by utilizing biometrics such as fingerprints, retinas and facial structures is constantly evolving. The use of masks during the Covid-19 Pandemic and post-pandemic has become an obligation wherever humans are active. The study aimed to create an automated door access model using Convolutional Neural Network (CNN) algorithms and Amazon Rekognition as cloud-based software. The CNN algorithm is applied to classify faces wearing masks or not wearing masks. The CNN architecture model uses sequential, convolution2D, max polling 2D, flatten dan dense. The hardware includes the Raspberry Pi, USB Webcam, Relay, and Magnetic Doorlock. The test results were obtained from the results of the accuracy plot on the Convolutional Neural Network model with an accuracy rate of 99% at an epoch value of 8 with a learning time of 67 seconds.
Co-Authors Abdat, Misel Umar Agung Prajuhana Putra Agung Prajuhana Putra Agung Prajuhana Putra Alfiansyah Alfiansyah Andi Mulyono Anggraeni, Ade Irma Arie Arie Qur’ania Arshad, Muhammad Waqas Asep Kohar Benny Nofika Benny Rachman Chandra, Iryanto Dian Kartika Utami Dieke Aprianto Edi Santosa Eneng Tita Tosida Fani Susanti Hamzah, Rizki Maulana Heni Sulistiani Herfina Herfina Herlinda Herlinda Hermawan Hermawan Hifshan Riesvicky Iyan Mulyana Kamel Mahdi Lita Karlitasari Lita Karlitasari Mehta, Abhishek Mubarok, Yasir Pahmi mulyono, andi Mutiara Prihatini Nofika, Benny Oktaviani, Nabila Oktaviani Palupiningsih, Pritasari Prajuhana Putra, Agung Putri Dwi Lestari Rahma, Yunita Reno Cahyo Hutomo Setiawan, Andrian Setiawansyah Setiawansyah Sitna Hajar Hadad Soewarto Hardhienata Sri Wahjuni Sudrajat, Cecep Suriansyah, Muhammad Iqbal Tjut Awaliyah Zuraiyah Yudhia Mulya Yuli Wahyuni Yuri Rahmanto Zebua, Mitra Darma Zolla Zolla