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Data Science: Journal of Computing and Applied Informatics

JoCAI
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Published by Universitas Sumatera Utara

ISSN : 25806769 EISSN : 2580829X DOI : -

Core Subject : Science,

Computer Science & IT

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.

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Articles 5 Documents

Search results for , issue "Vol. 1 No. 1 (2017): Data Science: Journal of Computing and Applied Informatics (JoCAI)" : 5 Documents clear

Efficiency of Local Government Units in North Western Philippines as to the Attainment of the Millennium Development Goals Milagros Baldemor
Data Science: Journal of Computing and Applied Informatics Vol. 1 No. 1 (2017): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

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.

On Factoring The RSA Modulus Using Tabu Search Ade Candra; Mohammad Andri Budiman; Dian Rachmawati
Data Science: Journal of Computing and Applied Informatics Vol. 1 No. 1 (2017): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

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.

Implementation and comparison of Berry-Ravindran and Zhu- Takaoka exact string matching algorithms in Indonesian-Batak Toba dictionary Efelin Siburian; Mohammad Andri Budiman; Jos Timanta
Data Science: Journal of Computing and Applied Informatics Vol. 1 No. 1 (2017): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

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.

Improving Data Collection on Article Clustering by Using Distributed Focused Crawler Dani Gunawan; Amalia Amalia; Atras Najwan
Data Science: Journal of Computing and Applied Informatics Vol. 1 No. 1 (2017): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

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.

Subject Bias in Image Aesthetic Appeal Ratings Ernestasia Siahaan; Esther Nababan
Data Science: Journal of Computing and Applied Informatics Vol. 1 No. 1 (2017): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Automatic prediction of image aesthetic appeal is an important part of multimedia and computer vision research, as it contributes to providing better content quality to users. Various features and learning methods have been proposed in the past to predict image aesthetic appeal more accurately. The effectiveness of these proposed methods often depend on the data used to train the predictor. Since aesthetic appeal is a subjective construct, factors that influence the subjectivity in aesthetic appeal data need to be understood and addressed. In this paper, we look into the subjectivity of aesthetic appeal data, and how it relates with image characteristics that are often used in aesthetic appeal prediction. We use subject bias and confidence interval to measure subjectivity, and check how they might be influenced by image content category and features.

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Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name
Ainul Hizriadi, S.Kom., M.Sc.

Contact Email
ainul.hizriadi@usu.ac.id

Phone
-

Journal Mail Official
jocai@usu.ac.id

Editorial Address
-

Location
Kota medan,

Sumatera utara

INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name Ainul Hizriadi, S.Kom., M.Sc.

Contact Email ainul.hizriadi@usu.ac.id

Phone -

Journal Mail Official jocai@usu.ac.id

Editorial Address -

Location Kota medan, Sumatera utara INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Contact Name
Ainul Hizriadi, S.Kom., M.Sc.

Contact Email
ainul.hizriadi@usu.ac.id

Phone
-

Journal Mail Official
jocai@usu.ac.id

Editorial Address
-

Location
Kota medan,

Sumatera utara

INDONESIA