Analisis Sentimen Opini Publik Berita Kebakaran Hutan melalui Komparasi Algoritma Support Vector Machine dan K-nearest Neighbor Berbasis Particle Swarm Optimization

research
  • 06 Sep
  • 2018

Analisis Sentimen Opini Publik Berita Kebakaran Hutan melalui Komparasi Algoritma Support Vector Machine dan K-nearest Neighbor Berbasis Particle Swarm Optimization

Sentiment analysis is a process to determine the content of text-based datasets which are positive or negative. At present, public opinion be an important resource in the decision of a person in finding a solution. Classification algorithms such as Support Vector Machine (SVM) and K-Nearest Neighbor (K-NN) is proposed by many researchers to be used in sentiment analysis for review opinion. The problem in this research is the selection of feature selection to improve accuracy values Support Vector Machine (SVM) and K-Nearest Neighbor (K-NN) and compare the highest accuracy for sentiment analysis review public opinion about the news of forest fires. The comparison algorithms, SVM produces an accuracy of 80.83% and AUC 0.947, then compared with SVM based on PSO with an accuracy of 87.11% and AUC 0.922. The test result data for K-NN algorithm accuracy was 85.00% and the AUC 0.918, then compared for accuracy by k-NN-based PSO amounted to 73.06% and the AUC 0.500. The results of the testing of the PSO algorithm can improve the accuracy of SVM, but are not able to improve the accuracy of the algorithm K-NN. SVM algorithm based on PSO proven to provide solutions to the problems of classification review news opinion forest fires in order to more accurately and optimally.

Unduhan

  • 14_JP Feb2017_Lilyani A.pdf

    ANALISIS SENTIMEN OPINI PUBLIK BERITA KEBAKARAN HUTAN MELALUI KOMPARASI ALGORITMA SUPPORT VECTOR MACHINE DAN K-NEAREST NEIGHBOR BERBASIS PARTICLE SWARM OPTIMIZATION

    •   diunduh 492x | Ukuran 1,519 KB

 

REFERENSI

Basari, A. S. H., Hussin, B., Ananta, I. G. P., & Zeniarja, J. (2013). Opinion mining of movie review using hybrid method of support vector machine and particle swarm optimization. Procedia Engineering, 53, 453–462. http://doi.org/10.1016/j.proeng.2013.02.059

Chou, J.-S. S., Cheng, M.-Y. Y., Wu, Y.-W. W., & Pham, A.-D. D. (2014). Optimizing parameters of support vector machine using fast messy genetic algorithm for dispute classification. Expert Systems with Applications, 41(8), 3955–3964. http://doi.org/10.1016/j.eswa.2013.12.035

Dehkharghani, R., Mercan, H., Javeed, A., & Saygin, Y. (2014). Sentimental causal rule discovery from Twitter. Expert Systems with Applications, 41(10), 4950–5958. http://doi.org/10.1016/j.eswa.2014.02.024

Jiang, S., Pang, G., Wu, M., & Kuang, L. (2012). An improved K-nearest-neighbor algorithm for text categorization. Expert Systems with Applications, 39(1), 1503–1509. http://doi.org/10.1016/j.eswa.2011.08.040

Jusoh, S., & Alfawareh, H. M. (2013). Applying fuzzy sets for opinion mining. 2013 International Conference on Computer Applications Technology (ICCAT), 1–5. http://doi.org/10.1109/ICCAT.2013.6521965

Langgeni, D. P., Baizal, Z. K. A., & W, Y. F. A. (2010). Clustering Artikel Berita Berbahasa Indonesia Menggunakan Unsupervised Feature Selection. In Seminar Nasional Informatika 2010 (Vol. 2010, pp. 1–10).

Liu, Y., Wang, G., Chen, H., Dong, H., Zhu, X., & Wang, S. (2011). An improved particle swarm optimization for feature selection. Journal of Bionic Engineering, 8(2), 191–200. http://doi.org/10.1016/S1672-6529(11)60020-6

Rozi, I. F., Hadi, S., & Achmad, E. (2012). Implementasi Opinion Mining ( Analisis Sentimen ) untuk Ekstraksi Data Opini Publik pada Perguruan Tinggi. Universitas Stuttgart, 6(1), 37–43.

Samsudin, N., Puteh, M., Hamdan, A. R., & Nazri, M. Z. A. (2012). Is artificial immune system suitable for opinion mining?

Conference on Data Mining and Optimization, (September), 131–136. http://doi.org/10.1109/DMO.2012.6329811

Vercellis, C. (2009). Business Intelligence: Data Mining and Optimization for Decision Making. Business Intelligence: Data Mining and Optimization for Decision Making. http://doi.org/10.1002/9780470753866

Xiang, J., Han, X., Duan, F., Qiang, Y., Xiong, X., Lan, Y., & Chai, H. (2015). A novel hybrid system for feature selection based on an improved gravitational search algorithm and k-NN method. Applied Soft Computing, 31, 293–307. http://doi.org/10.1016/j.asoc.2015.01.043

Yao, Zhi-Min. (2012), An Optimized NBC Approach in Text Classification. Physics Procedia, 24, 1910-1914

Zhao, M., Fu, C., Ji, L., Tang, K., & Zhou, M. (2011). Feature selection and parameter optimization for support vector machines: A new approach based on genetic algorithm with feature chromosomes. Expert Systems with Applications, 38(5), 5197–5204. http://doi.org/10.1016/j.eswa.2010.10.041