Data Science: Journal of Computing and Applied Informatics
Data Science: Journal of Computing and Applied Informatics (JoCAI) is a peer-reviewed biannual journal (January and July) published by TALENTA Publisher and organized by Faculty of Computer Science and Information Technology, Universitas Sumatera Utara (USU) as an open access journal. It welcomes full research articles in the field of Computing and Applied Informatics related to Data Science from the following subject area: Analytics, Artificial Intelligence, Bioinformatics, Big Data, Computational Linguistics, Cryptography, Data Mining, Data Warehouse, E-Commerce, E-Government, E-Health, Internet of Things, Information Theory, Information Security, Machine Learning, Multimedia & Image Processing, Software Engineering, Socio Informatics, and Wireless & Mobile Computing. ISSN (Print) : 2580-6769 ISSN (Online) : 2580-829X Each publication will contain 5 (five) manuscripts published online and printed. JoCAI strives to be a means of periodic, accredited, national scientific publications or reputable international publications through printed and online publications.
Articles by issue : Vol 2 No 1 (2018): Data Science: Journal of Computing and Applied Informatics (JoCAI)
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Articles
A Framework to Ensure Data Integrity and Safety

Paulus, Erick, Fauzan, Mochamad Azmi

Data Science: Journal of Computing and Applied Informatics Vol 2 No 1 (2018): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

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Abstract

The technology development allows people to more easily communicate and convey information. The current communication media can facilitate its users to send and receive digital data, such as text, sound or digital image. But in terms of security, communications media not always ensure the confidentiality and authentication of data traffic. Most people rely solely on the security provided by the communications media providers in securing their data, which is essentially still inadequate. This paper presents the development of a data security framework by applying the principles of cryptography and digital signatures, such as authenticity, integrity, and data confidentiality. The application is designed using the SHA-256 algorithm as digital signature, AES algorithm as file encryption, and RSA algorithm as asymmetric key in digital file distribution and signature. Then, several simulation testing was performed to ensure the robustness of the framework. Furthermore, we also evaluated the speed of framework based on CPU and memory capacity. Based on the experiment, our proposed framework can be a reliable solution for securing data in data transaction

Using random search and brute force algorithm in factoring the RSA modulus

Budiman, Mohammad Andri, Rachmawati, Dian

Data Science: Journal of Computing and Applied Informatics Vol 2 No 1 (2018): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

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Abstract

Abstract. The security of the RSA cryptosystem is directly proportional to the size of its modulus, n. The modulus n is a multiplication of two very large prime numbers, notated as p and q. Since modulus n is public, a cryptanalyst can use factorization algorithms such as Euler’s and Pollard’s algorithms to derive the private keys, p and q. Brute force is an algorithm that searches a solution to a problem by generating all the possible candidate solutions and testing those candidates one by one in order to get the most relevant solution. Random search is a numerical optimization algorithm that starts its search by generating one candidate solution randomly and iteratively compares it with other random candidate solution in order to get the most suitable solution. This work aims to compare the performance of brute force algorithm and random search in factoring the RSA modulus into its two prime factors by experimental means in Python programming language. The primality test is done by Fermat algorithm and the sieve of Eratosthenes.

Time Series And Data Envelopment Analysis On The Performance Efficiency Of Dmmmsu-South La Union Campus

Baldemor, Milagros

Data Science: Journal of Computing and Applied Informatics Vol 2 No 1 (2018): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

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Abstract

This study entitled “Time Series and Data Envelopment Analysis (DEA) on the Performance Efficiency of DMMMSU-South La Union Campus” determined the performance of the Don Mariano Marcos Memorial State University -South La Union Campus, La Union, Philippines, a Level Four state university in the country, vis-à-vis its efficiency along the following performance indicators: Program Requirements, Research, Extension and Production for five (5) academic years 2009-2014. Furthermore, it determined the peer groups and weights of the DMUs (Decision Making Units – the different Colleges and Institutes), the virtual inputs/outputs or potential improvements of the colleges/institutes to be in the efficient frontier, the input and output slacks (input excesses and output shortfalls)needed in the different indicators and the best practices to be considered by the inefficient and weak efficient DMUs.The “best practice” in the frontier is the basis to calculate the adjustments necessary for the DMUs. Different indicators showed varied performance levels in the different academic years but there are best practices from the “efficient” DMUs which could be adapted by the “weak efficient” and “inefficient” ones.

Data Analysis as a Method to gather Data to study the Relation between Fundamental Rights and Rule of Law

Muntjewerff, Antoinette, Van Loo, Kirsten

Data Science: Journal of Computing and Applied Informatics Vol 2 No 1 (2018): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

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Abstract

The research described here involves the relation between fundamental rights and the rule of law. In the end we want to find out what meaning is attributed to ‘rule of law’ by the European Court of Human Rights. In this article we describe our method to gather the data we need to be able to perform an analysis of the reasoning process by the Court.

The Determining Gender Using Facial Recognition Based On Neural Network With Backpropagation

Fauziah, Fauziah

Data Science: Journal of Computing and Applied Informatics Vol 2 No 1 (2018): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

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Abstract

One area of science that can apply facial recognition applications is artificial intelligence. The algorithms used in facial recognition are quite numerous and varied, but they all have the same three basic stages, face detection, facial extraction and facial recognition (Face Recognition) . Facial recognition applications using artificial intelligence as a major component, especially artificial neural networks for processing and facial identification are still not widely encountered. Ba ckpropagation is a learning algorithm to minimize the error rate by adjusting the weights based on the desired output and target differences. The test results of 30 images have the average value of mse is 0.14796 and the best value of mse on the test of man number 3 with mse value 0.1488 and mean 0.0047 while for the female number 2 with mse value 0.1497 and niali mean 0.0047.