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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. 4 No. 1 (2020): Data Science: Journal of Computing and Applied Informatics (JoCAI)" : 5 Documents clear

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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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