ANALISIS SENTIMEN TWITTER MENGGUNAKAN METODE MAXIMUM ENTROPY

FRISKA, FRISKA and Yusliani, Novi and Miraswan, Kanda Januar (2022) ANALISIS SENTIMEN TWITTER MENGGUNAKAN METODE MAXIMUM ENTROPY. Undergraduate thesis, Sriwijaya University.

	Text RAMA_55201_09021181621034.pdf - Bibliography Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (6MB) | Request a copy
	Text RAMA_55201_09021181621034_TURNITIN.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (6MB) | Request a copy
	Text RAMA_55201_09021181621034_0008118205_0009019002_01_front_ref.pdf - Accepted Version Available under License Creative Commons Public Domain Dedication. Download (1MB)
	Text RAMA_55201_09021181621034_0008118205_0009019002_02.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (668kB) | Request a copy
	Text RAMA_55201_09021181621034_0008118205_0009019002_03.pdf - Bibliography Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (710kB) | Request a copy
	Text RAMA_55201_09021181621034_0008118205_0009019002_04.pdf - Bibliography Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (1MB) | Request a copy
	Text RAMA_55201_09021181621034_0008118205_0009019002_05.pdf - Bibliography Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (445kB) | Request a copy
	Text RAMA_55201_09021181621034_0008118205_0009019002_06.pdf - Bibliography Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (394kB) | Request a copy
	Text RAMA_55201_09021181621034_0008118205_0009019002_06_ref.pdf - Bibliography Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (183kB) | Request a copy
	Text RAMA_55201_09021181621034_0008118205_0009019002_07_lamp.pdf - Bibliography Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (297kB) | Request a copy

Abstract

Social media is one of the media used by humans to socialize with each other and is done online without being constrained by space and time. The more developed social media, the more public opinion will be on a problem. One of the social media that is often used for opinions is Twitter. Sentiment analysis is a multidisciplinary field of study used to analyze people's sentiments towards an entity. Sentiment analysis can be done using tweet data originating from social media Twitter. This study uses the Maximum Entropy method. Maximum entropy is usually used to determine the polarity of opinion on a sentiment. Maximum Entropy has a predictive modeling algorithm that estimates the most uniform distribution (maximum entropy) starting with a uniform probability distribution and iteratively changing the weights one by one to increase the probability of reaching the optimal probability distribution. The dataset used amounted to 550 data consisting of 495 training data and 55 test data. The dataset used has two labels, namely positive and negative. Based on the results of testing the 55 datasets that have been carried out, the results obtained an accuracy of 53%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Analisis Sentimen, Maximum Entropy, Twitter
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation. Z Bibliography. Library Science. Information Resources > Z004 Books. Writing. Paleography
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Friska Friska
Date Deposited:	11 Apr 2023 06:58
Last Modified:	11 Apr 2023 06:58
URI:	http://repository.unsri.ac.id/id/eprint/95480

Actions (login required)

View Item