Effective Garbage Data Filtering Algorithm for SNS Big Data Processing by Machine Learning

Ha, Il-Kyu; Back, Bong-Hyun

doi:10.1109/icaiic48513.2020.9065276

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…Data validasi banyak digunakan untuk mengukur performa generalisasi model dalam pembelajaran mesin, yang selanjutnya akan di proses melalui integrasi data [21] validasi data adalah proses mengidentifikasi data yang mengandung kesalahan (misalnya, nilai di luar jangkauan, fitur tidak ada, dan sebagainya) [22] Setelah tahap cleaning data, tahap integrasi data juga menjadi tunjuan utama dari layer pemrosesan data, mengintegrasikan data yang dikumpulkan dari sumber data terstruktur dan tidak terstruktur [23] setelah proses integrasi selesai maka data memiliki banyak noise yang terdiri darai garbage data, irrelevant data hingga advertisement data, kita dapat melakukan filter data untuk mendapatkan data data yang kita butuhkan. [24] .…”

Section: Lapisan Pemrosesan Dataunclassified

Lapisan Arsitektur Big Data Dalam Kajian Studi Pustaka

Cucus

Endra

Aprilinda

et al. 2021

Explore. jurnal. sistem. inf. dan. telematika

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The era of big data is an interesting phenomenon to be discussed by researchers and software developers, application development and data management concepts are increasingly becoming variants and support makes the big data framework can enter every line of life, data that is arranged both synchronously and asynchronously, involves machines and humans in data collection make this technology more in line with the concept of the Industrial Revolution 4.0. In various studies, Big Data concepts and frameworks are presented, from these studies some researchers present layers in the Big Data architecture, where each layer provides input to other layers to be able to be processed into a ready-to-serve form in the community, a layer consisting of data collection, data storage, data processing and data analysis, so that at the application layer the use of data can be felt more optimally by users This paper presents some literature study materials which are summarized to get an explanation of the layers of Big Data architecture that can be developed and applied to other research fields.

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Section: Lapisan Pemrosesan Dataunclassified

Lapisan Arsitektur Big Data Dalam Kajian Studi Pustaka

Cucus

Endra

Aprilinda

et al. 2021

Explore. jurnal. sistem. inf. dan. telematika

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“…In 2020, Ha and Back [22] discussed about the use of social network services (SNS). It has been discussed in terms of data collection, data processing and data analysis.…”

Section: Literature Surveymentioning

confidence: 99%

A survey and analysis of big data management based on computational methodologies

Kumari¹

2020

TIPCV

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n a business enterprise there is an enormous amount of data generated or processed daily through different data points. It is increasing day by day. It is tough to handle it through traditional applications like excel or any other tools. So, big data analytics and environment may be helpful in the current scenario and the situation discussed above. This paper discussed the big data management ways with the impact of computational methodologies. It also covers the applicability domains and areas. It explores the computational methods applicability scenario and their conceptual design based on the previous literature. Machine learning, artificial intelligence and data mining techniques have been discussed for the same environment based on the related study.

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Effective Garbage Data Filtering Algorithm for SNS Big Data Processing by Machine Learning

Cited by 2 publications

References 18 publications

Lapisan Arsitektur Big Data Dalam Kajian Studi Pustaka

Lapisan Arsitektur Big Data Dalam Kajian Studi Pustaka

A survey and analysis of big data management based on computational methodologies

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