PENINGKATAN AKURASI ALGORITMA C4.5 PADA ANALISIS SENTIMEN MENGGUNAKAN PARTICLE SWARM OPTIMIZATION

ARLIANSYAH, AZHARY and Primartha, Rifkie and Miraswan, Kanda Januar (2018) PENINGKATAN AKURASI ALGORITMA C4.5 PADA ANALISIS SENTIMEN MENGGUNAKAN PARTICLE SWARM OPTIMIZATION. Undergraduate thesis, Sriwijaya University.

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Abstract

Sentiment analysis can be considered as a classification task in natural language processing because it uses classification algorithm to determine the class of text data. In classification, feature extraction is a process to extract the features from the data so that it can be used as the inputs for the classification algorithm. However, not all features are relevant for classification. Irrelevant feature tend to decrease accuracy of the classification algorithm. In order to avoid such problem, the features of the datashould go through a feature selection mechanism to select only relevant features. In this research, C4.5 algorithm is used to classify class sentiment of public review of online transportation in Indonesia, and PSO is used as the feature selection mechanism to improve C4.5 accuracy. The result so that C4.5 achieves 1.69% improvement in accuracy by selecting features using PSO.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Analisis sentimen, Pemrosesan bahasa alami, Algoritma C4.5, PSO, Klasifikasi, Seleksi fitur, Transportasi online
Subjects:	T Technology > T Technology (General) > T58.5-58.64 Information technology > T58.64 Management of information systems Production capacity. Manufacturing capacity Cf. HD69.C3 Economics Cf. TS176+ Manufacturing engineering
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Mr Halim Sobri
Date Deposited:	20 Sep 2019 01:14
Last Modified:	20 Sep 2019 03:45
URI:	http://repository.unsri.ac.id/id/eprint/8142

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