Dashboard Pre-Processing Data (DPD) as Data Analysis System with Technological Innovation to Perform Pre-Processing Quantitative Data

Mashuri Mashuri, Albertus Eka Putra Haryanto, Yola Argatha Manik, Dinar Sukma Dewi, Tegar Primadana Putra

Abstract


In essence, data in real-life always needs to be pre-processed or better known as pre-processing data. Pre-processing Data is one of the early techniques for converting raw data from various sources into cleaner information that can be used for further analysis. There are three types of pre-processing data, missing values, checking outlier data, and identifying the types of distribution in the data. Currently, statistical software that offers to be used in pre-processing data analysis has been widely and is quite familiar. However, the user is often can not run the analysis quickly. Therefore, there is the idea to create and develop an application or dashboard that can be used to solve these problems. The application this at is offered and trying to be developed is called "DPD (Dashboard Pre-Processing Data)". This application serves as a tool to pre-process data quickly and efficiently. In addition, with this application, it’s expected that users can identify missing values, data outliers, and some types of data distribution, so users can determine the analysis method that will be used on the research data they have.

Keywords


Outlier Data; Distribution of the Data; Pre-Processing Data; Missing Value

Full Text:

PDF

References


N. Galuh Importance of Pre-Processing in Statistical Processing. https://www.dqlab.id/importance-preprocessing-dalam-pengolahan-data-statistik. [On line]. Available: dqlab, www.dqlab.id [Accessed on October 19, 2021].

Rosyid. Data Preprocessing Data Mining: Concepts and Techniques. [On line]. Available: http://rosyid.lecturer.pens.ac.id/dataMining/Data%20Preprocessing.pdf [Accessed on October 19, 2021].

T. I Made. Presentation and Data Processing using the R. Application, 2014. [E-book] Available: https://repository.unej.ac.id/bitstream/handle/123456789/59392/PromoBuku2014.pdf?sequence=1&isAllowed=y

J. Richard, and DW Wichern. Applied Multivariate Statistical Analysis, 5th edition. New Jersey: Prentice Hall Inc., 2002.

RE Walpole (In), & IP Sidhi (Ed.), Introduction to Statistics. Jakarta: PT Gramedia Pustaka Utama, 2012.

RE Walpole (In), & IP Sidhi (Ed.), Introduction to Statistics. Jakarta: PT Gramedia Pustaka Utama, 2007.

Didi. Chapter V : R for Statistical Processing & Analysis., 2021. [Online]. Available: http://didi.staff.gunadarma.ac.id/Downloads/files/13709/BabV.pdf [Accessed on October 19, 2021].

U. Abdullah. Getting to Know R Shiny Closer, 2021 [Online]. Available: https://www.abdumar.com/2021/03/mengenal-r-shiny-more-close.html [Accessed on October 19, 2022




DOI: http://dx.doi.org/10.12962/j23378557.v9i1.a13050

Refbacks

  • There are currently no refbacks.


Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License. IPTEK The Journal of Engineering published by Pusat Publikasi Ilmiah, Institut Teknologi Sepuluh Nopember

 

Please contact us for order or further information at: email: iptek.joe[at]gmail.com Fax/Telp: 031 5992945. Editorial Office Address: Pusat Riset Building 6th floor, ITS Campus, Sukolilo, Surabaya 60111, Indonesia.