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

  • Lilyani Asri Utami (1*) Sistem Informasi STMIK Nusa Mandiri

  • (*) Corresponding Author
Keywords: PARTICLE SWARM OPTIMIZATION, Data Mining, Support Vector Machine, K-Nearest Neighbor (K-NN), Sentiment analysis

Abstract

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.

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Author Biography

Lilyani Asri Utami, Sistem Informasi STMIK Nusa Mandiri

Lilyani Asri Utami, M.Kom.  Lahir di Bogor pada tanggal 15 November 1991, lulusan pendidikan Program S2 jurusan Ilmu Komputer – Pasca Sarjana STMIK Nusa Mandiri Jakarta tahun 2016. Bekerja sebagai instruktur di STMIK Nusa Mandiri Jakarta sejak tahun 2014. Sampai saat ini telah mengikuti beberapa kegiatan seminar nasional untuk menambah pengetahuan tentang menulis untuk menuangkan pemikiran dalam rangka melaksanakan Tri Dharma Perguruan Tinggi. Sebuah prociding berjudul “Sistem Informasi Administrasi Pasien Pada Klinik Keluarga Depok” pernah dimuat pada Konferensi Nasional Ilmu Pengetahuan dan Teknologi (KNIT) Nusa Mandiri pada tahun 2015. Semoga penelitian ini dapat memberikan manfaat bagi para pembacanya. 

References

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
Published
2017-03-15
How to Cite
Utami, L. (2017). ANALISIS SENTIMEN OPINI PUBLIK BERITA KEBAKARAN HUTAN MELALUI KOMPARASI ALGORITMA SUPPORT VECTOR MACHINE DAN K-NEAREST NEIGHBOR BERBASIS PARTICLE SWARM OPTIMIZATION. Jurnal Pilar Nusa Mandiri, 13(1), 103-112. https://doi.org/10.33480/pilar.v13i1.153
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