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JOIV : International Journal on Informatics Visualization
Published by Politeknik Negeri Padang
ISSN : 25499610     EISSN : 25499904     DOI : -
Core Subject : Science,
Computer Science & IT
JOIV : International Journal on Informatics Visualization is an international peer-reviewed journal dedicated to interchange for the results of high quality research in all aspect of Computer Science, Computer Engineering, Information Technology and Visualization. The journal publishes state-of-art papers in fundamental theory, experiments and simulation, as well as applications, with a systematic proposed method, sufficient review on previous works, expanded discussion and concise conclusion. As our commitment to the advancement of science and technology, the JOIV follows the open access policy that allows the published articles freely available online without any subscription.
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Articles 5 Documents
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Search results for , issue "Vol 1, No 2 (2017)" : 5 Documents clear
Data Scientists’ Skills in Detecting Archetypes in Iran Hamideh Iraj; Babak Sohrabi
JOIV : International Journal on Informatics Visualization Vol 1, No 2 (2017)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1467.722 KB) | DOI: 10.30630/joiv.1.2.17

Abstract

The use of data-driven decision making and data scientists is on the rise in Iran as companies have rapidly been focusing on gathering data and analyzing it to guide corporate decisions. In order to facilitate the process and understand the nature and characteristics of this transformation, the current study intends to learn about data scientists’ skills and archetypes in Iran. Detecting skills archetypes has been done via analyzing the skills of data scientists which were self-expressed through an online survey. The results revealed that there are three archetypes of data scientists including high level data scientists, low level data scientists and software developers. The archetypal patterns are based on levels of data scientists’ skills rather than the type of dominant skills they possess which was the most frequent pattern in previous studies.
An Application of Artificial Neural Networks and Fuzzy Logic on the Stock Price Prediction Problem Thanh Tung Khuat; My Hanh Le
JOIV : International Journal on Informatics Visualization Vol 1, No 2 (2017)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1499.134 KB) | DOI: 10.30630/joiv.1.2.20

Abstract

The financial industry has been becoming more and more dependent on advanced computing technologies in order to maintain competitiveness in a global economy. Hence, the stock price prediction problem using data mining techniques is one of the most important issues in finance. This field has attracted great scientific interest and has become a crucial research area to provide a more precise prediction process. Fuzzy logic (FL) and Artificial Neural Network (ANN) present an exciting and promising technique with a wide scope for the applications of prediction. There is a growing interest in both fields of fuzzy logic computing and the financial world in the use of fuzzy logic to predict future changes in prices of stocks, exchange rates, commodities, and other financial time series. Fuzzy logic provides a way to draw definite conclusions from vague, ambiguous or imprecise information. Artificial Neural Network is one of data mining techniques being widely accepted in the business area due to its ability to learn and detect relationships among nonlinear variables. The ANN outperforms statistical regression models and also allows deeper analysis of large data sets, especially those that have the tendency to fluctuate within a short of time period. In this paper, we investigate the ability of Fuzzy logic and multilayer perceptron (MLP), which is a kind of the ANN, to tackle the financial time series stock forecasting problem. The proposed approaches were tested on the historical price data collected from Yahoo Finance with different companies. Furthermore, the comparison between those techniques is performed to examine their effectiveness.
Analyzing COBIT 5 IT Audit Framework Implementation using AHP Methodology Mutiara AB; - Prihandoko; Prasetyo E; Widya C
JOIV : International Journal on Informatics Visualization Vol 1, No 2 (2017)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2095.979 KB) | DOI: 10.30630/joiv.1.2.18

Abstract

COBIT has been known as the best practice standard in IT Governance, both in management or evaluated of the IT utilization. The role of IT Audit framework to evaluate the benefits of Information Technology in an enterprise either its gain benefits or fail in order to achieved the business objective.  In Indonesia, most organization has been implemented the IT as their main support of process business, and deliberately conduct the evaluation of the implementation used some IT Audit framework such as ITIL, TOGAF, COBIT and other Government rule. Those frameworks have been known as an IT governance framework, most of organizations are choosing COBIT and ITIL due to the internal control issues.  Therefore, this research will be focus on COBIT 5 utilization as an IT audit frameworks,  a comparison also will be done between the COBIT 5 and ITIL. The comprehensive parameters in COBIT 5 which provides 5 category process in two domain, management and control will be the variables of prioritizing process among them for each object.  This paper will analyze the use of those parameters for some selected organization and prioritize them using the Analytical Hierarchy Process (AHP) methodology that will lead to create a new model of IT Audit frameworks based on the user requirement and opinion.  the analyzing process the implementation of COBIT 5 framework in some organizations, and priorities the preferred attributes of COBIT 5 that very likely and suitable to the culture and needs of user in Indonesia using AHP Methodology, and create the best qualified model of IT Audit that fit with the requirements of the organizations especially for Indonesia organizations and companies.
Encrypted Arabic Documents Search Model using Fuzzy Keywords in Cloud Computing Nidal Hassan Hussein; Ahmed Khalid; Khalid Khanfar
JOIV : International Journal on Informatics Visualization Vol 1, No 2 (2017)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1243.13 KB) | DOI: 10.30630/joiv.1.2.22

Abstract

Cloud computing is a burgeoning and revolutionary technology that has changed how data are stored and computed in the cloud. This technology incorporates many elements into an innovative architecture. Among them are autonomic computing, grid computing, and utility computing. Moreover, the rapid storage of data in the clouds has an impact on the security level of organizations. The chief challenge of cloud computing is how to build a secured cloud storage.The reason for this difficulty is thatbefore data transfer, data are usually encrypted in order to achieve a high utilization. Another real challenging task of cloud computing is how to apply a search over encrypted data. As many techniques support only exact keywordmatches, we propose a model to search over encrypted data that are written in Arabic. If an exact keyword match fails, our model will approximate the file as a secondary result. Our model will also use a fuzzy keyword search to enhance system usability by obtaining matching result whenever users input exact matches or the closest possible matches based on keywords. To the best of our knowledge, our model is considered to be the first research work that applies fuzzy search over Arabic encrypted data.
Improvement of Email And Twitter Classification Accuracy Based On Preprocessing Bayes Naive Classifier Optimization In Integrated Digital Assistant Aldo Erianda; Indri Rahmayuni
JOIV : International Journal on Informatics Visualization Vol 1, No 2 (2017)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1333.273 KB) | DOI: 10.30630/joiv.1.2.21

Abstract

This research focuses on improving the accuracy  of email and twitter classification. Spelling mistakes and lack of matches with bag of word causes the low accuracy in classifying. This research used naïve Bayes as a text classification algorithms. Text is divided into three categories: personal, work and family. To achieve maximum likelikehood value for  the category, a better preprocessing techniques is needed. It is necessary for the process to normalize the preprocessing and search for words that correspond to classes in the bag of word. So that the text can be classified by category or has a higher precision accuracy.

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