Distributed Classifier for SDGs Topics in Online News using RabbitMQ Message Broker
Penulis/Author
ANAN NUGROHO (1); Widyawan, S.T., M.Sc., Ph.D. (2); Prof. Dr. Ir. Sri Suning Kusumawardani, S.T, M.T. (3)
Tanggal/Date
15 2020
Kata Kunci/Keyword
Abstrak/Abstract
Internet data has grown very fast and becomes very large. Thus continuous improvement will always be required to face this challenge. The Sustainable Development Goals (SDGs) are defined by the United Nations (UN) to encourage improvements in the field of life in each country. We proposed a combination of Distributed System (RabbitMQ) and Machine Learning (Naïve Bayes Classifier) as one of the support to measure the achievement level of Sustainable Development Goals (SDGs) in Indonesia. The methods will categorize the Detik.com news into two classes; the relevant to SDGs of Indonesia and the irrelevant to SDGs of Indonesia. Our work shows that the use of the load-balance feature in RabbitMQ could shorten the processing time of the Naïve Bayes Classifier. RabbitMQ as a load-balancer can divide the workload equally, thus reducing the latency time of the Naïve Bayes Classifier classification process by 30.3 percent.
Level
Internasional
Status
Dokumen Karya
No
Judul
Tipe Dokumen
Aksi
1
Distributed Classifier for SDGs Topics in Online News using RabbitMQMessage Broker.pdf
[PAK] Full Dokumen
2
Similarity Distributed Classifier for SDGs Topics in Online News using RabbitMQ Message Broker.pdf
