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Husni Teja Sukmana
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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 59 Documents
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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.
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.
Knowledge Management Strategy by Means of Virtualization in Covid-19 Eissa Mohammed Ali Qhal
Journal of Applied Data Sciences Vol 1, No 2: DECEMBER 2020
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v1i2.46

Abstract

In an era where companies, governmental programs, and the economy are knowledge-driven, they must understand the best ways to apply knowledge management to yield better results. Fortunately, technology is continuously evolving to better cope with knowledge management's arising challenges by innovating better problem-solving models. Cloud computing is one of the many technologies today that has revolutionized how different companies and economic sectors treat knowledge management by enabling this Knowledge's virtualization. The paper focuses on knowledge management transition to virtualization technology in supporting businesses during the COVID-19. Pandemic times despite many social restrictions being put in place to contain the virus. The text further presents the results of a study involving major firms in different sectors and how their applied virtualization technology is yielding results even during the toughest of times in any company or economy.
Diagnosis of Preeclampsia in Pregnant Women Based on K-Nearest Neighbor Algorithm Rifki Hidayat; Tri Astuti
Journal of Applied Data Sciences Vol 1, No 2: DECEMBER 2020
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v1i2.53

Abstract

Maternal deaths are divided into two namely direct and indirect deaths. Globally 80% of direct maternal deaths, preeclampsia are included in direct maternal deaths. Preeclampsia conditions of pregnancy with hypertension occur after the 20th week in women who previously had normal blood pressure. Preeclampsia can also be characterized by hypertension (systolic blood pressure ≥ 140 mmHg or diastolic blood pressure ≥ 90 mmHg) accompanied by proteinuria (≥ 300 mg / dl in tamping urine 24 hours). In this study, an analysis of medical records in the Purbalingga and Banyumas areas using 8 attributes, namely age, body weight, blood pressure, edema, multiple pregnancy, history of hypertension, how many children, urine protein, and preeclampsia class. From calculations using the K-NN (K-Nearest Neighbor) algorithm, the Sensitivity performance value of 98.19%, Specificity 100%, and Accuracy 98.33%.
Analysis of Transaction Data for Modeling the Pattern of Goods Purchase Supporting Goods Location Linda Rosliadewi; Yusmedi Nurfaizal; Retno Waluyo; Mohammad Imron
Journal of Applied Data Sciences Vol 1, No 2: DECEMBER 2020
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v1i2.54

Abstract

Arlinda shop is a shop that sells daily necessities located in Salem, Brebes. Each day, this shop generates more and more data that is not used. The store layout which does not get enough attention will affect the level of sales. This study aimed to process the unused transaction data to obtain purchase patterns, some of the most frequently used algorithms were the apriori algorithm and FP-Growth algorithm to find relationship patterns, however, there was a technical constraint in the recommendation technique used which was frequently ignoring a large collection of items. To overcome this problem, the clustering process was carried out using the K-Medoids algorithm so that the association process became smaller. The test was carried out using RapidMiner with a minimum support of 10% - 30% and a minimum confidence of 70% and the results of recommendations for the layout of the goods with the highest lift ratio, namely if someone buys Nuvo BW then he buys pepsodent act, if someone buys wrapping papers then he buys mamy poko, and if someone buys cereal milo then he buys chitato.
Analysis of Heart Rate Variability of College Students in Altitude Training Based on Big Data Huiling Wang; Jingyuan Yang
Journal of Applied Data Sciences Vol 1, No 2: DECEMBER 2020
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v1i2.55

Abstract

Heart activity is regulated by sympathetic and parasympathetic autonomic nerves, which is a common method to measure and evaluate autonomic nerve activity. Collecting ECG data of college students and analyzing heart rate variability can evaluate autonomic nerve activity of college students. This paper discusses the influence of altitude training on students' heart rate variability, and the influence of altitude hypoxia and low pressure on students' autonomic nervous system, which provides a scientific basis for coaches to control the training intensity and amount and reduce the risk factors. The results show that good adaptability of altitude training can improve the activity ability of the vagus nerve.
Visual Design of Artificial Intelligence Based on the Image Search Algorithm Xiaobo Jiang; Zongren Chen; Jun Yu; Lixia Huang
Journal of Applied Data Sciences Vol 1, No 2: DECEMBER 2020
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v1i2.56

Abstract

With the rise of the wave of artificial intelligence and the development and popularization of intelligent technology, the digital images generated by the Internet and mobile intelligence terminals grow exponentially. As the most important information carrier of data content, pictures occupy immeasurable value in this era. To solve the shortage of image search engines, this paper uses the image browsing algorithm, which combines the semantic and image characteristics of the image, and organizes the returned images according to the visual characteristics similarity of the images. In addition, in order to reduce the computational time and improve the performance of similarity search, a near neighbor search algorithm based on key dimensions is applied. Experiments show that the AI visualization design based on the image search algorithm can not only overcome the semantic gap to some extent, but also strengthen the interaction between 88% systems and users to browse the search results more efficiently and naturally.

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