KLASIFIKASI STATUS K-POPERS MENGGUNAKAN COSINE SIMILARITY
Total View This Week0
Total View This Week0
Institusion
Institut Teknologi Perusahaan Listrik Negara
Author
TURNIP, YUNI MAY CHYNTIA
Sudirman, M. Yoga Distra
Kusuma, Dine Tiara
Sudirman, M. Yoga Distra
Kusuma, Dine Tiara
Subject
Teknik Informatika
Datestamp
2022-09-22 06:22:29
Abstract :
Social media like twitter and facebook do not have the ability to collect information into a conclusion. For that we need an approach in drawing conclusions from sentiments or opinions of K-Popers to obtain information to classify emotions on sentiments or opinions in social media. This research is based on the rules of System Development Life Cycle (SDLC) Prototype by applying the Text Mining Cosine Similarity algorithm. This application is designed using State Transition Diagram (STD) to describe the processes in the application. Later will be tested against the application by using White Box testing method. The test results of 50 status or opinion of K-Popers on social media shows that the Cosine Similarity algorithm is able to classify the types of emotions contained in that status. With the application of the classification of K-Popers status is expected to provide information on how many opinions Happy, Sad, Angry and Neutral circulating on social media twitter and facebook.
Social media like twitter and facebook do not have the ability to collect information into a conclusion. For that we need an approach in drawing conclusions from sentiments or opinions of K-Popers to obtain information to classify emotions on sentiments or opinions in social media. This research is based on the rules of System Development Life Cycle (SDLC) Prototype by applying the Text Mining Cosine Similarity algorithm. This application is designed using State Transition Diagram (STD) to describe the processes in the application. Later will be tested against the application by using White Box testing method. The test results of 50 status or opinion of K-Popers on social media shows that the Cosine Similarity algorithm is able to classify the types of emotions contained in that status. With the application of the classification of K-Popers status is expected to provide information on how many opinions Happy, Sad, Angry and Neutral circulating on social media twitter and facebook.
Download
book
BibTex
Latex, Jabref
cloud_download
Original Resource
url resource Institution
