scholarly journals Classification of human development index using particle swarm optimization based on support vector machine algorithm

2021 ◽  
Vol 1088 (1) ◽  
pp. 012033
Author(s):  
Mulyawan ◽  
Gifthera Dwilestari ◽  
Agus Bahtiar ◽  
Fadhil Muhammad Basysyar ◽  
Nana Suarna
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Human Development ◽  
Human Development Index ◽  
Particle Swarm ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
Swarm Optimization ◽  
Development Index

scholarly journals USTADZ ABDUL SOMAD LECTURE SENTIMENT ANALYSIS USING SUPPORT VECTOR MACHINE ALGORITHM COMPARISON OF COMPARATIVE FEATURES SELECTION

2020 ◽  
Vol 16 (1) ◽  
pp. 111-116
Author(s):  
Dedi Aridarma ◽  
Rifki Sadikin ◽  
Bobby Suryo Prakoso ◽  
Heru Sukma Utama
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Sentiment Analysis ◽  
Information Gain ◽  
Particle Swarm ◽  
Support Vector ◽  
Features Selection ◽  
Support Vector Machine Algorithm ◽  
Swarm Optimization ◽  
Algorithm Comparison

Religious lectures are activities that are identical to the religious presentation, delivered verbally by a person who has religious knowledge and then delivered to the community with the aim of the knowledge delivered can be understood. Ustadz Abdul Somad was one of the preachers who had been known to various levels of society, but his lectures were not all acceptable to the people who liked or disliked those who came from various positive and negative comments on social media. To solve these problems, Sentiment Analysis was used by applying the Support Vector Machine Algorithm method. The purpose of this study is to compile using the selection of feature Particle Swarm Optimization and Information Gain. The results for Particle Swarm Optimization Selection Feature resulted in Accuracy of 80.57%, Precision of 85.45%, and Recall of 79.52%, Selection Feature Information Gain resulted in Accuracy of 79.78%, Precision of 78.47%, and Recall of 78, 43%, Based on the results of this study, it can be concluded that using the Particle Swarm Optimization selection feature is better at the level of accuracy when compared to using the Information Gain selection feature.

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