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Journal of Applied Data Sciences
Published by Bright Publisher
ISSN : -     EISSN : 27236471     DOI : doi.org/10.47738/jads
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management
One of the current hot topics in science is data: how can datasets be used in scientific and scholarly research in a more reliable, citable and accountable way? Data is of paramount importance to scientific progress, yet most research data remains private. Enhancing the transparency of the processes applied to collect, treat and analyze data will help to render scientific research results reproducible and thus more accountable. The datasets itself should also be accessible to other researchers, so that research publications, dataset descriptions, and the actual datasets can be linked. The journal Data provides a forum to publish methodical papers on processes applied to data collection, treatment and analysis, as well as for data descriptors publishing descriptions of a linked dataset.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 5 Documents
 1 
Search results for , issue "Vol 1, No 1: SEPTEMBER 2020" : 5 Documents clear
Data mining for Education Sector, a proposed concept Ammar Salamh Mujali Al-Rawahnaa; Anas Yahya Bader Al Hadid
Journal of Applied Data Sciences Vol 1, No 1: SEPTEMBER 2020
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v1i1.6

Abstract

Data mining is very much needed in various fields In accessing a large amount of data requires time and a high level of accuracy. In higher education the potential influence of data mining on the learning processes and outcomes of the students was realized. Especially in the field of education, knowing almost every educational institute, both public and private, has thousands of data from students with a variety of different programs and subjects. Understanding the benefits of data retrieval will facilitate the course of education itself. The use of Data mining in education will be useful in developing a student-focused strategy and in providing the correct tools that institutions would be able to use for quality improvement purposes. In this paper, we will find out the benefits of applying data mining in the education sector using classification, prediction, association and clustering methods.
Limitations of Big Data Partitions Technology Nguyen Huyen Trang
Journal of Applied Data Sciences Vol 1, No 1: SEPTEMBER 2020
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v1i1.7

Abstract

Big data is defined as the amount of data that is needed by new technology and architecture so that it is possible to extract the large amount of data provided by the analysis process. Due to its enormous size it is increasingly difficult for perfect analysis using existing traditional techniques. This technology is a solution for several problems that require a distributed system for storage needs because a problem cannot be solved in one machine. Since Big Data has become the latest technology in a market that brings tremendous profits to business organizations, it becomes possible when there are specific challenges and problems and it will continue to expand. This article introduces big data technology, and explains its partition limitations.
Problems, Challenges, and Opportunities Visualization on Big Data Tri Wahyuningsih
Journal of Applied Data Sciences Vol 1, No 1: SEPTEMBER 2020
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v1i1.8

Abstract

Today, almost everything is recorded digitally, from our browsing history to our health records in hospitals, we make and process billions of data every day. In this era of big data, large amounts of data are continuously obtained for different purposes. But, just processing and analyzing the data isnt enough. If data is displayed visually, humans always search for patterns more effectively. Visualization and interpretation of the data are very critical tasks in making choices in various industries. This also guides us to new ways to find innovative ideas through visualization to solve big-data problems. In this paper, we will discuss the problems, challenges, and potential of Visualization in Big Data.
Predicting Dropout on E-learning Using Machine Learning Akmal Akmal
Journal of Applied Data Sciences Vol 1, No 1: SEPTEMBER 2020
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v1i1.9

Abstract

After the corona virus outbreak (COVID-19) many various institutions changed how they work. From various sectors, which experienced the biggest change was the education sector. The education sector, which in general requires face-to-face interaction between teachers and students in a place, has now changed to online, which does not require that both parties be in a place. This is certainly a very big change and has an impact. In this paper we will discuss e-learning methods for drop-out prediction, based on three techniques of machine learning.
Survey Opinion using Sentiment Analysis Taqwa Hariguna; Husni Teja Sukmana; Jong Il Kim
Journal of Applied Data Sciences Vol 1, No 1: SEPTEMBER 2020
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v1i1.10

Abstract

Sentiment analysis or opinion mining is a computational study of the opinions, judgments, attitudes, and emotions of a person towards an entity, individual, issue, event, topic, and attributes. This task is very challenging technically but very useful in practice. For example, a business always wants to seek opinion about its products and services from the public or the consumers. Additionally, potential consumers want to learn what users think they have when using a service or purchasing a product. To get public opinion on food habits, ad strategies, political trends, social issues and business policy, this is a very critical factor. This paper will explain a survey of key sentiment-extraction approaches.

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