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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 2, No 1: JANUARY 2021" : 5 Documents clear
Exploratory Data Analysis & Booking Cancelation Prediction on Hotel Booking Demands Datasets Pujo Hari Saputro; Herlino Nanang
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.
Semi-Supervised Classification on Credit Card Fraud Detection using AutoEncoders Nur Rachman Dzakiyullah; Andri Pramuntadi; Anni Karimatul Fauziyyah
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.
Sentiment Analysis between VADER and EDA for the US Presidential Election 2020 on Twitter Datasets Ria Devina Endsuy
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.
Maximizing Strategy Improvement in Mall Customer Segmentation using K-means Clustering Musthofa Galih Pradana; Hoang Thi Ha
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.
Predict high school students' final grades using basic machine learning Sigit Sugiyanto
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

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