cover
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
Data Science: Journal of Computing and Applied Informatics
ISSN : 25806769     EISSN : 2580829X     DOI : -
Core Subject : Science,
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.
Arjuna Subject : -
Articles 27 Documents
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1163.568 KB) | DOI: 10.32734/jocai.v2.i1-91

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1081.472 KB) | DOI: 10.32734/jocai.v2.i1-92

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1175.054 KB) | DOI: 10.32734/jocai.v2.i1-93

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1248.839 KB) | DOI: 10.32734/jocai.v2.i1-96

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.
Enhancing Performance of Parallel Self-Organizing Map on Large Dataset with Dynamic Parallel and Hyper-Q Sibero, Alexander F.K.; Sitompul, Opim Salim; Nasution, Mahyuddin K.M.
Data Science: Journal of Computing and Applied Informatics Vol 2 No 2 (2018): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1212.692 KB) | DOI: 10.32734/jocai.v2.i2-324

Abstract

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.
A Two Microphone-Based Approach for Detecting and Identifying Speech Sounds in Hearing Support System Sitompul, Andre; Nishimura, Masafumi
Data Science: Journal of Computing and Applied Informatics Vol 2 No 2 (2018): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (18.285 KB) | DOI: 10.32734/jocai.v2.i2-325

Abstract

For people with hearing disabilities, not only would give them difficulties in going through their everyday lives but also sometimes could be life threatening. In this research we proposed a simple, yet robust approach for helping the hearing-impaired people in identifying the important sounds around them by using two microphones as input channel that could be worn around the person’s head as a substitute for their ears. This device then could be used to record the situation of the surroundings, and then the system would estimate the Direction of Arrival (DOA) of the sound sources, then detect and classify them using Support Vector Machine (SVM) into target speech or noise category. As the results, system’s classifier could produce FAR and FRR as low as 2%, in which 274 out of 280 samples were successfully classified as target speeches and 22 from the total of 27 noise samples were successfully classified as noise
Genetic Algorithms Dynamic Population Size with Cloning in Solving Traveling Salesman Problem Nababan, Erna Budhiarti; Sitompul, Opim Salim; Cancer, Yuni
Data Science: Journal of Computing and Applied Informatics Vol 2 No 2 (2018): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1177.754 KB) | DOI: 10.32734/jocai.v2.i2-326

Abstract

Population size of classical genetic algorithm is determined constantly. Its size remains constant over the run. For more complex problems, larger population sizes need to be avoided from early convergence to produce local optimum. Objective of this research is to evaluate population resizing i.e. dynamic population sizing for Genetic Algorithm (GA) using cloning strategy. We compare performance of proposed method and traditional GA employed to Travelling Salesman Problem (TSP) of A280.tsp taken from TSPLIB. Result shown that GA with dynamic population size exceed computational time of traditional GA.
Study of Scheduling in Programming Languages of Multi-Core Processor Hosseini-Rad, Mina; Abdolrazzagh-Nezhad, Majid; Javadi-Moghaddam, Seyyed-Mohammad
Data Science: Journal of Computing and Applied Informatics Vol 2 No 2 (2018): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (18.285 KB) | DOI: 10.32734/jocai.v2.i2-327

Abstract

Over the recent decades, the nature of multi-core processors caused changing the serial programming model to parallel mode. There are several programming languages for the parallel multi-core processors and processors with different architectures that these languages have faced programmers to challenges to achieve higher performance. In addition, different scheduling methods in the programming languages for the multi-core processors have the significant impact on the efficiency of the programming languages. Therefore, this article addresses the investigation of the conventional scheduling techniques in the programming languages of multi-core processors which allows the researcher to choose more suitable programing languages by comparing efficiency than application. Several languages such as Cilk++، OpenMP، TBB and PThread were studied, and their scheduling efficiency has been investigated by running Quick-Sort and Merge-Sort algorithms as well.
User Centered Design Approach to Redesign Graduate Student Management Information System Purnamasari, Fanindia; Ashaari, Noraidah Sahari
Data Science: Journal of Computing and Applied Informatics Vol 2 No 2 (2018): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (726.621 KB) | DOI: 10.32734/jocai.v2.i2-328

Abstract

This study conducted a user centered design approach based on user perception using the Graduate Student Information System. This study start by requirement gathering employs interview method with discussing about its interface design and its available menu. Then following as design, evaluation and delivery to actual user. The proposed design is evaluated by 30 respondent using questionnaire The findings from the analyzed result show that usability factor encountered by user that has high average mean was interface standard. The study prove that the current system needs to improve from functionality aspect. The proposed system is expected to help the administration task.
Subject Bias in Image Aesthetic Appeal Ratings Siahaan, Ernestasia; Nababan, Esther
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (323.963 KB) | DOI: 10.32734/jocai.v1.i1-63

Abstract

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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