cover
Contact Name
Taqwa Hariguna
Contact Email
thariguna@gmail.com
Phone
+6282138053306
Journal Mail Official
jads@bright-journal.org
Editorial Address
Puri Mersi Baru, Jl.Martadireja II, Gang Sitihingil 3 Blok A No 2, Purwokerto Timur, Jawa Tengah
Location
Unknown,
Unknown
INDONESIA
Journal of Applied Data Sciences
Published by Bright Publisher
ISSN : -     EISSN : 27236471     DOI : doi.org/10.47738/jads
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.
Articles 21 Documents
Survey Opinion using Sentiment Analysis Hariguna, Taqwa; Sukmana, Husni Teja; Kim, Jong Il
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.
Predict high school students' final grades using basic machine learning Sugiyanto, Sigit
Journal of Applied Data Sciences Vol 2, No 1: JANUARY 2021
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v2i1.19

Abstract

To improve the quality of students, teachers must be able to take precautionary measures to deal with students who are lacking or have the potential to experience deficiency. Student ratings are temporary, however, have a profound impact on students' mental and enthusiasm for learning. As a teacher, it is very important to make predictions in dealing with this matter because if the ranking has been issued, it is too late. In this article, we will discuss and make Student grade predictions using basic machine learning, we will also discuss the continuity between student data and machine learning
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.
Problems, Challenges, and Opportunities Visualization on Big Data Wahyuningsih, Tri
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 isn t 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.
Maximizing Strategy Improvement in Mall Customer Segmentation using K-means Clustering Pradana, Musthofa Galih; Ha, Hoang Thi
Journal of Applied Data Sciences Vol 2, No 1: JANUARY 2021
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v2i1.18

Abstract

The application of customer segmentation is very vital in the world of marketing, a manager in determining a marketing strategy, knowing the target customer is a must, otherwise it will potentially waste resources to pursue the wrong target. Customer segmentation aims to create a relationship with the most profitable customers by designing the most appropriate marketing strategy. Many statistical techniques have been applied to segment the market but very large data are very influential in reducing their effectiveness. The aim of clustering is to optimize the experimental similarity within the cluster and to maximize the dissimilarity in between clusters. In this study, we use K-means clustering as the basis for the segmentation that will be carried out, and of course, there are additional models that will be used to support the research results. As a result, we have succeeded in dividing the customer into 5 clusters based on the relationship between annual income and their spending score, and it has been concluded that customers who have high-income levels & have a high spending score are also very appropriate targets for implementing market strategies.
Sentiment Analysis between VADER and EDA for the US Presidential Election 2020 on Twitter Datasets Endsuy, Ria Devina
Journal of Applied Data Sciences Vol 2, No 1: JANUARY 2021
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v2i1.17

Abstract

The 2020 US Election took place on November 3, 2020, the result of the election was that Joe Biden received 51.4% of the votes, Donald Trump 46.9%, and the rest were other candidates. The period before the election was a time when people conveyed who would vote and conveyed the reasons directly or through social media, especially Twitter through keywords or tags such as #JoeBiden & #DonaldTrump. In this paper, we will compare sentiment analysis and explanatory data analysis against US election data on Twitter. The overall objective of the two case studies is to evaluate the similarity between the sentiment of location-based tweets and on-ground public opinion reflected in election results. In this paper, we find that there are more "neutral" sentiments than "negative" and "positive" sentiments.
Semi-Supervised Classification on Credit Card Fraud Detection using AutoEncoders Dzakiyullah, Nur Rachman; Pramuntadi, Andri; Fauziyyah, Anni Karimatul
Journal of Applied Data Sciences Vol 2, No 1: JANUARY 2021
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v2i1.16

Abstract

The use of credit cards for online purchases has increased dramatically and led to an explosion in credit card fraud. Credit card companies need to be able to identify fraudulent credit card transactions so that customers are not charged for items they do not buy. In this study, we will use semi-supervised learning and combine it with AutoEncoders to identify fraudulent credit card transactions. In this paper, we will implement the use of T-SNE to visualize fraud and non-fraud transactions, then improve the visualization using autoencoders. Classification report proved that it is possible to achieve very acceptable precision using semi-supervised classification to detect credit card fraud.
Limitations of Big Data Partitions Technology Trang, Nguyen Huyen
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.
Data mining for Education Sector, a proposed concept Mujali Al-Rawahnaa, Ammar Salamh; Bader Al Hadid, Anas Yahya
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.
Exploratory Data Analysis & Booking Cancelation Prediction on Hotel Booking Demands Datasets Saputro, Pujo Hari; Nanang, Herlino
Journal of Applied Data Sciences Vol 2, No 1: JANUARY 2021
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v2i1.20

Abstract

Online ordering is the latest breakthrough in the hospitality industry, but when it comes to booking cancellations, it has a negative impact on it. To reduce and anticipate an increase in the number of booking cancellations, we developed a booking cancellations prediction model using machine learning interpretable algorithms for hotels. Both models used Random Forest and the Extra Tree Classifier share the highest precision ratios, Random Forest on the other hand has the highest recall ratio, this model predicted 79% of actual positive observations. These results prove that it is possible to predict booking cancellations with high accuracy. These results can also help hotel owners or hotel managers to predict better predictions, improve cancellation regulations, and create new tactics in business.

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