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
61 Documents
<![CDATA[Efficiency of Local Government Units in North Western Philippines as to the Attainment of the Millennium Development Goals ]]>
<![CDATA[Milagros Baldemor]]>
<![CDATA[ Data Science: Journal of Computing and Applied Informatics Vol. 1 No. 1 (2017): Data Science: Journal of Computing and Applied Informatics (JoCAI) ]]>
Publisher : <![CDATA[Talenta Publisher ]]>
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DOI: 10.32734/jocai.v1.i1-64
<![CDATA[This study entitled “Efficiency of Local Government Units in Northwestern Philippines as to the Attainment of the Millennium Development Goals” determined the performance of the four provinces and eight cities in Region I, Philippines, vis-à-vis their efficiency along the eight goals and 21 targets of the Millennium Development Goals (MDGs) for 2012-2015. Furthermore, it determined the peer groups and weights of the DMUs (Decision Making Units – the different provinces and cities), the virtual inputs/outputs or potential improvements of the DMUs 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 years but there are best practices from the “efficient” DMUs which could be adapted by the “weak efficient” and “inefficient” ones.]]>
<![CDATA[On Factoring The RSA Modulus Using Tabu Search ]]>
<![CDATA[Ade Candra]]>;
<![CDATA[Mohammad Andri Budiman]]>;
<![CDATA[Dian Rachmawati]]>
<![CDATA[ Data Science: Journal of Computing and Applied Informatics Vol. 1 No. 1 (2017): Data Science: Journal of Computing and Applied Informatics (JoCAI) ]]>
Publisher : <![CDATA[Talenta Publisher ]]>
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DOI: 10.32734/jocai.v1.i1-65
<![CDATA[It is intuitively clear that the security of RSA cryptosystem depends on the hardness of factoring a very large integer into its two prime factors. Numerous studies about integer factorization in the field of number theory have been carried out, and as a result, lots of exact factorization algorithms, such as Fermat’s factorization algorithm, quadratic sieve method, and Pollard’s rho algorithm have been found. The factorization problem is in the class of NP (non-deterministic polynomial time). Tabu search is a metaheuristic in the field of artificial intelligence which is often used to solve NP and NP-hard problems; the result of this method is expected to be close-to-optimal (suboptimal). This study aims to factorize the RSA modulus into its two prime factors using tabu search by conducting experiments in Python programming language and to compare its time performance with an exact factorization algorithm, i.e. Pollard’s algorithm. The primality test is done with Lehmann’s algorithm.]]>
<![CDATA[Implementation and comparison of Berry-Ravindran and Zhu- Takaoka exact string matching algorithms in Indonesian-Batak Toba dictionary ]]>
<![CDATA[Efelin Siburian]]>;
<![CDATA[Mohammad Andri Budiman]]>;
<![CDATA[Jos Timanta]]>
<![CDATA[ Data Science: Journal of Computing and Applied Informatics Vol. 1 No. 1 (2017): Data Science: Journal of Computing and Applied Informatics (JoCAI) ]]>
Publisher : <![CDATA[Talenta Publisher ]]>
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DOI: 10.32734/jocai.v1.i1-67
<![CDATA[Indonesia has a variety of local languages, which is the Batak Toba language. This time, there are still some Batak Toba people who do not know speak Batak Toba language fluently. Nowadays, desktop based dictionary is one of reference that very efficiently used to learn a language and also to increase vocabulary. In making the dictionary application, string matching can be implemented for word-searching process. String matching have some algorithm, which is Berry – Ravindran algorithm and Zhu-Takaoka algorithm and will be implemented on the dictionary application. Zhu-Takaoka algorithm and Berry – Ravindran algorithm have two phases, which are the preprocessing phase and the searching phase. Preprocessing phase is a process to make the shifting values according to in pattern that input by user. To know the shifting value with Zhu-Takaoka algorithm, it’s need Zhu-Takaoka Bad Character (Ztbc) and Boyer-Moore Good Suffix (Bmgs). Then, Ztbc will be compared to Bmgs to get the maximum value of them that will be set as shifting value. While Berry-Ravindran algorithm, to know the shifting value is needed Berry-Ravindran Bad Character, which the two characters right of the text at the position m + 1 and m+ 2, is needed to determine the shifting value, where m is length of the pattern.]]>
<![CDATA[Improving Data Collection on Article Clustering by Using Distributed Focused Crawler ]]>
<![CDATA[Dani Gunawan]]>;
<![CDATA[Amalia Amalia]]>;
<![CDATA[Atras Najwan]]>
<![CDATA[ Data Science: Journal of Computing and Applied Informatics Vol. 1 No. 1 (2017): Data Science: Journal of Computing and Applied Informatics (JoCAI) ]]>
Publisher : <![CDATA[Talenta Publisher ]]>
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DOI: 10.32734/jocai.v1.i1-82
<![CDATA[Collecting or harvesting data from the Internet is often done by using web crawler. General web crawler is developed to be more focus on certain topic. The type of this web crawler called focused crawler. To improve the datacollection performance, creating focused crawler is not enough as the focused crawler makes efficient usage of network bandwidth and storage capacity. This research proposes a distributed focused crawler in order to improve the web crawler performance which also efficient in network bandwidth and storage capacity. This distributed focused crawler implements crawling scheduling, site ordering to determine URL queue, and focused crawler by using Naïve Bayes. This research also tests the web crawling performance by conducting multithreaded, then observe the CPU and memory utilization. The conclusion is the web crawling performance will be decrease when too many threads are used. As the consequences, the CPU and memory utilization will be very high, meanwhile performance of the distributed focused crawler will be low.]]>
<![CDATA[A Framework to Ensure Data Integrity and Safety ]]>
<![CDATA[Erick Paulus]]>;
<![CDATA[Mochamad Azmi Fauzan]]>
<![CDATA[ Data Science: Journal of Computing and Applied Informatics Vol. 2 No. 1 (2018): Data Science: Journal of Computing and Applied Informatics (JoCAI) ]]>
Publisher : <![CDATA[Talenta Publisher ]]>
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DOI: 10.32734/jocai.v2.i1-90
<![CDATA[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]]>
<![CDATA[Using random search and brute force algorithm in factoring the RSA modulus ]]>
<![CDATA[Mohammad Andri Budiman]]>;
<![CDATA[Dian Rachmawati]]>
<![CDATA[ Data Science: Journal of Computing and Applied Informatics Vol. 2 No. 1 (2018): Data Science: Journal of Computing and Applied Informatics (JoCAI) ]]>
Publisher : <![CDATA[Talenta Publisher ]]>
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DOI: 10.32734/jocai.v2.i1-91
<![CDATA[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.]]>
<![CDATA[Time Series And Data Envelopment Analysis On The Performance Efficiency Of Dmmmsu-South La Union Campus ]]>
<![CDATA[Milagros Baldemor]]>
<![CDATA[ Data Science: Journal of Computing and Applied Informatics Vol. 2 No. 1 (2018): Data Science: Journal of Computing and Applied Informatics (JoCAI) ]]>
Publisher : <![CDATA[Talenta Publisher ]]>
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DOI: 10.32734/jocai.v2.i1-92
<![CDATA[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.]]>
<![CDATA[Data Analysis as a Method to gather Data to study the Relation between Fundamental Rights and Rule of Law ]]>
<![CDATA[Antoinette Muntjewerff]]>;
<![CDATA[Kirsten Van Loo]]>
<![CDATA[ Data Science: Journal of Computing and Applied Informatics Vol. 2 No. 1 (2018): Data Science: Journal of Computing and Applied Informatics (JoCAI) ]]>
Publisher : <![CDATA[Talenta Publisher ]]>
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DOI: 10.32734/jocai.v2.i1-93
<![CDATA[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.]]>
<![CDATA[The Determining Gender Using Facial Recognition Based On Neural Network With Backpropagation ]]>
<![CDATA[Fauziah Fauziah]]>
<![CDATA[ Data Science: Journal of Computing and Applied Informatics Vol. 2 No. 1 (2018): Data Science: Journal of Computing and Applied Informatics (JoCAI) ]]>
Publisher : <![CDATA[Talenta Publisher ]]>
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DOI: 10.32734/jocai.v2.i1-96
<![CDATA[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.]]>
<![CDATA[Enhancing Performance of Parallel Self-Organizing Map on Large Dataset with Dynamic Parallel and Hyper-Q ]]>
<![CDATA[Alexander F.K. Sibero]]>;
<![CDATA[Opim Salim Sitompul]]>;
<![CDATA[Mahyuddin K.M. Nasution]]>
<![CDATA[ Data Science: Journal of Computing and Applied Informatics Vol. 2 No. 2 (2018): Data Science: Journal of Computing and Applied Informatics (JoCAI) ]]>
Publisher : <![CDATA[Talenta Publisher ]]>
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DOI: 10.32734/jocai.v2.i2-324
<![CDATA[Self-Organizing Map (SOM) is an unsupervised artificial neural network algorithm. Even though this algorithm is known to be an appealing clustering method,many efforts to improve its performance are still pursued in various research works. In order to gain faster computation time, for instance, running SOM in parallel had been focused in many previous research works. Utilization of the Graphics Processing Unit (GPU) as a parallel calculation engine is also continuously improved. However, total computation time in parallel SOM is still not optimal on processing large dataset. In this research, we propose a combination of Dynamic Parallel and Hyper-Q to further improve the performance of parallel SOM in terms of faster computing time. Dynamic Parallel and Hyper-Q are utilized on the process of calculating distance and searching best-matching unit (BMU), while updating weight and its neighbors are performed using Hyper-Q only. Result of this study indicates an increase in SOM parallel performance up to two times faster compared to those without using Dynamic Parallel and Hyper-Q.]]>
