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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 86 Documents

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Evaluation of a Proposed Road in a Campus Network based on Ideal Flow Kardi Teknomo
Data Science: Journal of Computing and Applied Informatics Vol. 3 No. 2 (2019): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

A proposed road project inside a campus that will have to down trees from its mini forest have attracted different public opinion among the faculty and students. In this paper, we would like to justify our view objectively based on transportation engineering point of view. The Ideal Flow Network (IFN) method was used to do the analysis because its source code is publicly available for clarification. The network data is based on previous study of Ateneo Traffic Group report. Two scenarios were set: based scenario that represents the current road network, and two proposed scenario that represents the current road network with additional proposed road in two ways and one way respectively. Analysis of the results show that the total network travel time of the proposed scenario are increased by 4.69% and 2.32% respectively for two ways and one-way scenarios. The network speed will be slightly improved by 0.03% in when the proposed road project is added in two ways. Thus, we failed to justify that the proposed network has better network performance.

Samawa Part of Speech Tagging using Brill Tagger Trienani Hariyanti; Saori Aida; Hiroyuki Kameda
Data Science: Journal of Computing and Applied Informatics Vol. 3 No. 2 (2019): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

There exist 7,097 living languages in the world cited by Ethnologue. Most of them, however, do not exist on the Internet as the objects of research. It indicates the gap in language resources. One of them is Samawa language which has over 500,000 native speakers and is identified as endangered language by UNESCO. What we known about Samawa so far is a lack of information, tools, and resources to maintain its sustainability. This paper aims to contribute to NLP, a growing field of research, by exploring Samawa part of speech tagging problem using rule-based approach, i.e. Brill tagger. It has been trained on very limited data of Samawa corpus, which is 24,627 tokens including punctuation marks with 24 tags of our original tagset. K-fold cross-validation (k = 5 and k = 10) was applied to compare Brill’s performance with Unigram, HMM, and TnT. Brill tagger with the combination of default tagger, Unigram, Bigram and Trigram as baseline tagger achieve higher accuracy over 95% than others. It suggests that the Brill tagger can be used to extend Samawa corpus automatically.

Data Security Using Multi-bit LSB and Modified Vernam Cipher Goklas Tomu Simbolon; Opim Salim Sitompul; Erna Budhiarti Nababan
Data Science: Journal of Computing and Applied Informatics Vol. 3 No. 2 (2019): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Data security is one of the most important aspects of today's information era. Some methods are used to secure important data from hackers. The LSB is a steganographic algorithm that is often used to store data in the last bit. In order to improve the security, we combine steganography with cryptography enables. In this research LSB is modified using the multi-bit LSB model. Modifications are made to the bits of each character, the rotation by a certain amount can randomize the plaintext content before cryptographic algorithm, Vernam is performed. The bit on LSB can be inserted data as much as 1, 2, 3 or 4 - bit information. The calculation results of MSE and PSNR values indicate that the use of 1-bit LSB is superior to that of 2-, 3-, or 4-bit LSB.

Usability Engineering and Evaluation of Usability In District Tourism And Culture Information Systems Wasis Haryono
Data Science: Journal of Computing and Applied Informatics Vol. 3 No. 2 (2019): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

So far, the potential of natural resources has not been fully managed by the government or the community. For that we need a mature tourist object planning either by visitors or by developers. Communities need information provided on a mobile basis that can be accessed anywhere. This information is inseparable from the role of technology that supports so that applications can be used by tourists who want to visit either in the form of an address or location. Applications that are worthy of use are applications that have been tested usefulness or called usability. To test a system requires a questionnaire which one of them is SUMI (Software Usability Measurement Inventory). This study uses a method adopted from usability engineering lifecycle. The results of this study in the form of assessment with several criteria, so the application is feasible to use.

Randomness of Poisson Distributed Random Number in the Queue System Ernestasia; Esther Nababan; Asima Manurung
Data Science: Journal of Computing and Applied Informatics Vol. 3 No. 2 (2019): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

In the queuing system, inter arrival variable and service time variable are probabilistic and its pattern follow a Poisson distribution. Simulations experiment for performance measurement of a queuing system required random data. In practice, random data is built using an application program. Pseudorandom data generated from application programs often have different patterns of randomness, although in each experiment simulated the same data distribution. Level of randomness may cause the results of simulation experiments experienced statistically significant deviations, especially on problems with stochastic variables. Statistical deviation can cause errors in interpreting the results of simulation experiments, especially in the assessment of the performance of the queuing system. It is required to evaluate whether the level of randomness of pseudorandom data effect on simulation results of performance measurement of a system. Simulation experiments on a simple queuing system (M / M / 1) was carried out by using a pseudorandom number generator. Application program used to generate pseudorandom numbers is Fortran90. The experimental results show that the greater the amount of pseudorandom data, the greater the statistical deviations occur, and the smaller the degree of randomness of data. This affects the results of the simulation system in which there is a probabilistic variable that require random data to conduct simulation

Operations Research, Mathematics, Computer Science and Statistics: The Relationships Adewoye S Olabode
Data Science: Journal of Computing and Applied Informatics Vol. 4 No. 1 (2020): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Many people have difficulty in seeing any difference between Mathematics, Operations Research, Statistics, Computer Science and other disciplines while others are just plain confused. In this work, OR and its applications are being exposed and then compared in order to look into the relationships between OR, Mathematics, Computer Science, Statistics and other fields. It has been realized that all these areas of knowledge are also interrelated with other areas such as Engineering, Physics, Microbiology, Economics etc.

Adaptive Moment Estimation To Minimize Square Error In Backpropagation Algorithm Roy Nuary Singarimbun
Data Science: Journal of Computing and Applied Informatics Vol. 4 No. 1 (2020): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Back - propagation Neural Network has weaknesses such as errors of gradient descent training slowly of error function, training time is too long and is easy to fall into local optimum. Back - propagation algorithm is one of the artificial neural network training algorithm that has weaknesses such as the convergence of long, over-fitting and easy to get stuck in local optima. Back - propagation is used to minimize errors in each iteration. This paper investigates and evaluates the performance of Adaptive Moment Estimation (ADAM) to minimize the squared error in back - propagation gradient descent algorithm. Adaptive Estimation moment can speed up the training and achieve the level of acceleration to get linear. ADAM can adapt to changes in the system, and can optimize many parameters with a low calculation. The results of the study indicate that the performance of adaptive moment estimation can minimize the squared error in the output of neural networks.

The Analysis Knowledge Management System Of Electronic Government South Tangerang Based On Usability Evaluation Using SUMI (Software Usability Measurement Inventory) Thoyyibah T; Asep Taufik Muharram
Data Science: Journal of Computing and Applied Informatics Vol. 4 No. 1 (2020): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

E-Government performance in the application of quality management information communication technology is very necessary. A website-based system is also used to improve the quality of services and community participation in development. The context that becomes the material that needs to be examined is public service, the quality of websites managed by the government and user satisfaction so that there is a two-way interaction between the government and the community. The purpose of this study consisted of three stages which were first to determine the role of E-government by utilizing technology to support the development of a reliable information system in South Tangerang Regency. The second is to find out the extent to which the system is used in South Tangerang E-government through Usability Evaluation using SUMI (Software Usability Measurement Inventory). The method used in this study is a method adopted from the Knowledge Management System Life Cycle. The stages of the method are Evaluate Existing Infrastructure, Form The KM Team, Knowledge Capture, Implement the KM system. The results of this study are in the form of usability values on the E-Government website of South Tangerang and usability testing which becomes a benchmark for the success of a system with score scores for 85 effectiveness categories, 81.5

Classification for Driver’s Distraction and Drowsiness Through Eye Closeness Using Receiver Operating Curve (ROC) Anis Hazirah Rodzi; Zalhan Bin Mohd Zin; Norazlin Ibrahim
Data Science: Journal of Computing and Applied Informatics Vol. 4 No. 1 (2020): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

In Malaysia, driver inattention and drowsiness becomes one of the causes of road accidents which sometime could lead to fatal ones. From the data provided by Malaysian Police Force, Polis Di Raja Malaysia or PDRM in 2016, deaths from road accidents increased from 6,706 in 2015 to 7,512 in 2016. Some accidents were caused by human factor such as driver's inattention and drowsiness. This problem motivates many parties to look for better solution in dealing with this human factor. Some of the car manufacturers have introduced to their certain models of car with an assistant system to oversee driver’s condition. The assistant system is in fact part of the main safety system known as Advanced Driver Assistance Systems (ADAS). The kind of system has been developed to strengthen vehicle systems for safety and conducive driving. The system has been contemplated to congregate accurate input, rapid processing data, precisely predict context, and respond in real time. In addition to that, suitable method is also needed to detect and classify driver drowsiness and inattention using computer vision as the latter become more and more important in any intelligent system development. In this paper, the proposed system introduces a method to classify drowsiness into three different classes of eye state; open, semi close and close. The classification has been done by using feature extraction method, percentage of eye closure (PERCLOS) technique and Support Vector Machine (SVM) classifier. The performances of the methods have been then measured and represented by using confusion matrix and ROC performance graph. The results have show that the proposed system has been able to achieve high performance of distraction and drowsiness detection according to driver's eye closeness level.

Improving KNN by Gases Brownian Motion Optimization Algorithm to Breast Cancer Detection Majid Abdolrazzagh-Nezhad; Shokooh Pour Mahyabadi; Ali Ebrahimpoor
Data Science: Journal of Computing and Applied Informatics Vol. 4 No. 1 (2020): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

In the last decade, the application of information technology and artificial intelligence algorithms are widely developed in collecting information of cancer patients and detecting them based on proposing various detection algorithms. The K-Nearest-Neighbor classification algorithm (KNN) is one of the most popular of detection algorithms, which has two challenges in determining the value of k and the volume of computations proportional to the size of the data and sample selected for training. In this paper, the Gaussian Brownian Motion Optimization (GBMO) algorithm is utilized for improving the KNN performance to breast cancer detection. To achieve to this aim, each gas molecule contains the information such as a selected subset of features to apply the KNN and k value. The GBMO has lower time-complexity order than other algorithms and has also been observed to perform better than other optimization algorithms in other applications. The algorithm and three well-known meta-heuristic algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Imperialist Competitive Algorithm (ICA) have been implemented on five benchmark functions and compared the obtained results. The GBMO+KNN performed on three benchmark datasets of breast cancer from UCI and the obtained results are compared with other existing cancer detection algorithms. These comparisons show significantly improves this classification accuracy with the proposed detection algorithm.

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

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Home Page

OAI Link

Editorial Team

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Reviewer

Google Scholar

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

Contact Email ainul.hizriadi@usu.ac.id

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Journal Mail Official jocai@usu.ac.id

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Location Kota medan, Sumatera utara INDONESIA

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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