Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal Jurnal Sistem Informasi Kaputama (JSIK) Merkurius: Jurnal Riset Sistem Informasi dan Teknik Informatika
Zhya Anggraini
Unknown Affiliation
Computer Science & IT

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
PENERAPAN SUPER ENKRIPSI HILL CIPHER DAN RSA UNTUK PENGAMANAN DATA FILE AUDIO MP3 Hakim, Azizhil; Zhya Anggraini; Dilla Sillfani; Renika Ayuni; Fauzi, Achmad
Jurnal Sistem Informasi Kaputama (JSIK) Vol. 9 No. 1 (2025): Volume 9, Nomor 1, Januari 2025
Publisher : STMIK KAPUTAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59697/jsik.v9i1.959

Abstract

Digital data security, especially audio files, is a major issue in the modern era, especially with the increasing risk of hacking and file tapping. To protect audio files such as MP3 format, this study suggests a combination of Hill Cipher and RSA algorithms for the Super Encryption method. Hill Cipher uses a linear matrix to encrypt data at high speed, while RSA uses a symmetric key created by Hill cipher to encrypt. The combination of these two algorithms produces a strong double layer of security to protect audio files. The layered encryption process uses Hill Cipher as the initial stage before using RSA for additional security. This super encryption is very good for maintaining data confidentiality. This study helps develop a digital data security system based on cryptography with a focus on audio files. This method can be used for various things, such as maintaining sensitive voice communications, maintaining important recordings, and creating applications based on audio data. However, further development can be done to improve the scalability and efficiency of this method, especially for large audio data files or data in the form of.
Sistem Pendukung Keputusan untuk Penentuan Penerima Bantuan Pendidikan di SMK Negeri 2 Binjai Menggunakan Metode Waspas Zhya Anggraini; Yani Maulita; Kristina Annatasia Br Sitepu
Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika Vol. 3 No. 6 (2025): November: Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/merkurius.v3i6.1135

Abstract

Advances in information and communication technology (ICT) have revolutionized various aspects of life, including the education sector. The application of technology in education enables more flexible, interactive, and personalized learning, as well as expanding access for students in various regions, including remote areas. SMK Negeri 2 Binjai is one of the vocational education institutions in Binjai City that is currently facing challenges in the process of determining recipients of educational assistance, particularly the Indonesia Pintar (PIP) Program, which is intended for underprivileged students. Until now, the selection mechanism for PIP recipients has been carried out manually, making it prone to subjectivity, lack of transparency, and limited integrated data. To overcome this problem, a decision support system is needed to help schools determine the recipients of educational assistance objectively and structurally using the Weighted Aggregated Sum Product Assessment (WASPAS) method, which is an effective multi-criteria decision-making technique for processing various assessment criteria to produce the best alternative ranking. The results of the calculation using the WASPAS method produced different preference values (Qi) for each student alternative. The highest score was obtained by Ilyas Ramadhanu with a Qi of 0.6899, followed by Binar Sembiring with a Qi of 0.5963, and Dimas Dwi Andika with a Qi of 0.5790. The lowest value was obtained by Adit Rahmazi with a Qi of 0.3852.
Co-Authors Dilla Sillfani Fauzi, Achmad Hakim, Azizhil Kristina Annatasia Br Sitepu Renika Ayuni Yani Maulita