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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 6 Documents
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Search results for , issue "Vol 2, No 3: SEPTEMBER 2021" : 6 Documents clear
Data Mining Predicts the Need for Immunization Vaccines Using the Naive Bayes Method Widyanto, R Arri; Avizenna, Meidar Hadi; Prabowo, Nugroho Agung; Alfata, Kemal; Ismanto, Agus
Journal of Applied Data Sciences Vol 2, No 3: SEPTEMBER 2021
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v2i3.39

Abstract

In December 2019, SARS-CoV-2 caused the coronavirus disease (COVID-19) to spread to all countries, infecting thousands of people and causing death. COVID-19 causes mild illness in most cases, although it can make some people seriously ill. Therefore, vaccines are in various phases of clinical progress, and some of them have been approved for national use. The current state of affairs reveals that there is a critical need for a quick and timely solution to the need for a Covid-19 vaccine. Non-clinical methods such as data mining and machine learning techniques can help to do this. This study will focus on US COVID-19 Vaccination Advances using Machine learning classification algorithms and Using Geospatial analysis to visualize the results. The paper's findings indicate which algorithm is better for a given data set. Naive Bayes algorithm is used to run tests on real world data, and is used to analyze and draw conclusions. Period of Accuracy and performance, and it was found that Naive Bayes is very superior in terms of time and accuracy.
Implementation of Apriori Data Mining Algorithm on Medical Device Inventory System Avizenna, Meidar Hadi; Widyanto, R Arri; Wirawan, Dwi Kusuma; Pratama, Teguh Adhi; Nabila, Amandha Shafa
Journal of Applied Data Sciences Vol 2, No 3: SEPTEMBER 2021
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v2i3.35

Abstract

The pattern of the need for drugs and medical devices in various hospitals has a tendency to be repeated and similar in a relatively long period of time, especially in one particular department, because the cases found are often similar or even similar. Ensuring the availability of stock in each departmental depot is very vital, because the procurement of medical devices must go through a certain process and time, so that cases of critical rheumatism often occur but the equipment needed at depositors does not meet the standards. need or run from inventory and must indent first. By calculating the trend of demand patterns and needs using an algorithm (Apriori Association) in the dataset, a rule is formed that in the pattern of dependence between itemsets that have supporting criteria in the form of 33.3% support and 85% Confidence, where the items that appear are items with frequency of occurrence and associations so that it can be taken into consideration to ensure the availability of drugs and medical devices.
Application of the Vector Machine Support Method in Twitter Social Media Sentiment Analysis Regarding the Covid-19 Vaccine Issue in Indonesia Riyanto, Riyanto; Azis, Abdul
Journal of Applied Data Sciences Vol 2, No 3: SEPTEMBER 2021
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v2i3.40

Abstract

According to the Indonesian government, Indonesia has been afflicted by Covid-19 since March 2, 2020. Numerous countries, including Indonesia, have made efforts, but with the spread of perceptions, rumors, and a flood of information into the society regarding vaccines, there are both advantages and disadvantages to vaccines. government-led immunization campaigns. As a result, it is vital to examine public sentiment toward the government's immunization programs. The goal of this study is to ascertain the emotion toward the Covid-19 vaccination in Indonesia based on the classification results. The Support Vector Machine classification technique was employed in this investigation (SVM). The SVM classification method was chosen because it possesses the ability to generalize when it comes to identifying a pattern, excluding the data used in the method's learning phase. Classification with an SVM linear kernel and TF-IDF weighting, as well as data sharing via K-fold cross validation with a value of k=10. Positive and negative classifications are made. Following preprocessing and classification, we determined the f1 values, accuracy, precision, and recall to use as reference values when evaluating the classification. SVM performed well in classifying the data in this investigation, with  f1 = 88.7%, accuracy = 84.4%, precision = 86.2%, and recall = 97%. This value is acceptable, and hence SVM is suitable for identifying sentiment data about the Covid-19 vaccine in Indonesia. Additional study can be conducted with richer tweet data, more thorough preprocessing, and comparison to other classification algorithms to obtain a higher categorization evaluation score.
Meta-Analysis of Social Networking Sites for the Purpose of Preventing Privacy Threats in the Digital Age Alvarez, Teresa; Chen, Hsieh-Chih
Journal of Applied Data Sciences Vol 2, No 3: SEPTEMBER 2021
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v2i3.36

Abstract

This article will discuss the challenges to privacy and data mining in social networking sites (Social Networking Sites). The author begins by defining privacy and data mining in today's big data sector before doing a meta-synthesis analysis. Numerous references and literature reviews were undertaken in order to compile material pertinent to the topic of privacy risks and data mining on social networking sites. According to the researchers' conceptualization, privacy concerns and data mining on SNS can be classified into three categories: multimedia content threats, traditional threats, and social threats. Each category is subdivided into multiple threat types. The author notes that in addition to utilizing the privacy measures offered by the SNS site, users must develop an early understanding of the difference between information and secrets. Users must use caution when deciding what content to share on social media platforms and what not to share.
Data Mining Implementation with Algorithm C4.5 for Predicting Graduation Rate College Student Saputra, Jeffri Prayitno Bangkit; Waluyo, Retno
Journal of Applied Data Sciences Vol 2, No 3: SEPTEMBER 2021
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v2i3.37

Abstract

Academic evaluation and graduation of students are critical components of an academic information system's (AIS) effectiveness since they allow for the measurement of student learning progress. Additionally, the assessment stating whether the student passed or failed would benefit both the student and teacher by acting as a reference point for future performance suggestions and evaluations. Using Decision Tree C4.5, a comprehensive analysis of the student academic evaluation approach was conducted. Age, gender, public or private high school status, high school department, organization activity, age at high school admission, progress GPA (pGPA), and total GPA (tGPA) were all documented and evaluated from semester 1–4 utilizing three times the graduation criterion periods. The article's scope is confined to undergraduate programs. An accuracy algorithm (AC) with a performance accuracy of 79.60 percent, a true positive rate (TP) of 77.70 percent, and 91 percent quality training data achieved the highest performance accuracy value.
A Brief Overview of the Accuracy of Classification Algorithms for Data Prediction in Machine Learning Applications Jen, Lichung; Lin, Yu-Hsiang
Journal of Applied Data Sciences Vol 2, No 3: SEPTEMBER 2021
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v2i3.38

Abstract

Many business applications rely on their history data to anticipate their company future. The marketing products process is one of the essential procedures for the firm. Customer needs supply a useful piece of information that helps to promote the suitable products at the proper moment. Moreover, services are recognized recently as products. The development of education and health services is reliant on historical data. For the more, lowering online social media networks problems and crimes need a big supply of information. Data analysts need to utilize an efficient categorization system to predict the future of such businesses. However, dealing with a vast quantity of data demands tremendous time to process. Data mining encompasses numerous valuable techniques that are used to anticipate statistical data in a number of business applications. The classification technique is one of the most extensively utilized with a range of algorithms. In this work, numerous categorization methods are revised in terms of accuracy in diverse domains of data mining applications. A complete analysis is done following delegated reading of 20 papers in the literature. This study intends to allow data analysts to identify the best suitable classification algorithm for numerous commercial applications including business in general, online social media networks, agriculture, health, and education. Results reveal FFBPN is the best accurate algorithm in the business arena. The Random Forest algorithm is the most accurate in categorizing online social networks (OSN) activity. Naïve Bayes method is the most accurate to classify agriculture datasets. OneR is the most accurate method to classify occurrences inside the health domain. The C4.5 Decision Tree method is the most accurate to classify students’ records to forecast degree completion time.

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