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

Analisis Sentimen Ceramah Ustad Abdul Somad Menggunakan Algoritma Support Vector Machine Komparasi Feature Selection

  • Dedi Aridarma (1*) STMIK Nusa Mandiri Jakarta, Indonesia
  • Rifki Sadikin (2) Sekolah Tinggi Manajemen Informatika dan Komputer Nusa Mandiri
  • Bobby Suryo Prakoso (3) STMIK Nusa Mandiri Jakarta, Indonesia
  • Heru Sukma Utama (4) STMIK Nusa Mandiri Jakarta, Indonesia

  • (*) Corresponding Author
Keywords: Support Vector Machine, Particle Swarm Optimization, Information Gain

Abstract

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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Author Biographies

Dedi Aridarma, STMIK Nusa Mandiri Jakarta, Indonesia

Master of Computer Science Study Program

Rifki Sadikin, Sekolah Tinggi Manajemen Informatika dan Komputer Nusa Mandiri

Master of Computer Science Study Program

Bobby Suryo Prakoso, STMIK Nusa Mandiri Jakarta, Indonesia

Master of Computer Science Study Program

Heru Sukma Utama, STMIK Nusa Mandiri Jakarta, Indonesia

Master of Computer Science Study Program

References

Aridarma, D., Sadikin, R., Prakoso, B. S., & Utama, H. S. (2019). Independent Research Final Report: Ustadz Abdul Somad Lecture Sentiment Analysis Using Support Vector Machine Algorithm Comparison Of Comparative Features Selection.

Arifin, Y. T. (2016). Komparasi Fitur Seleksi Pada Algoritma Support Vector Machine Untuk Analisis Sentimen Review. Jurnal Informatika, 3(2), 191–199.

Awais, M., Altaf, B., Member, S., & Yoo, J. (2015). Epileptic Seizure Classification SoC Using a Non-Linear Support Vector Machine. 1–12.

Bimantoro, D. A., & ‘Uyun, Is. (2017). PENGARUH PENGGUNAAN INFORMATION GAIN UNTUK. 2(1), 42–52.

Chandani, V., Wahono, R. S., & Purwanto, P. (2015). Komparasi Algoritma Klasifikasi Machine Learning Dan Feature Selection pada Analisis Sentimen Review Film. Journal of Intelligent Systems, 1(1), 56–60. http://www.journal.ilmukomputer.org/index.php?journal=jis&page=article&op=view&path%5B%5D=10

Chandra, H. A. (2018). Particle Swarm Optimization Pada Metode Knn Euclidean Distance Berbasis Variasi Jarak Untuk Penilaian. Technologia: Jurnal Ilmiah, 9(1), 59. https://doi.org/10.31602/tji.v9i1.1103

Fei, S.-W., Miao, Y.-B., & Liu, C.-L. (2009). Chinese Grain Production Forecasting Method Based on Particle Swarm Optimization-based Support Vector Machine. Recent Patents on Engineering, 3(1), 8–12. https://doi.org/10.2174/187221209787259947

Fitriani, W. (2017). Pemanfaatan Kultum dalam Pembinaan Akhlak Siswa di SMPN 1 Indrapuri [UIN Ar-Raniry Banda Aceh]. https://repository.ar-raniry.ac.id/id/eprint/542/

Indrayuni, E. (2016). Analisa Sentimen Review Hotel Menggunakan Algoritma Support Vector Machine Berbasis Particle Swarm Optimization. EVOLUSI : Jurnal Sains Dan Manajemen, 4(2). https://doi.org/10.31294/EVOLUSI.V4I2.697

Jiawei, H., Kamber, M., & JianPei. (2013). Data mining Concepts and Techniques Preface and Introduction.

Kristiyanti, D. A. (2015). Analisis Sentimen Review Produk Kosmetik Melalui. Konferensi Nasional Ilmu Pengetahuan Dan Teknologi, 69–76. http://konferensi.nusamandiri.ac.id/prosiding/index.php/knit/article/view/33

Li, F. C. (2009). Comparison of the primitive classifiers without features selection in credit scoring. Proceedings - International Conference on Management and Service Science, MASS 2009, 1–5. https://doi.org/10.1109/ICMSS.2009.5302730

Li, H., Feng, X., Cao, L., Li, E., Liang, H., & Chen, X. (2016). A New ECG Signal Classification Based on WPD and ApEn Feature Extraction. Circuits, Systems, and Signal Processing, 35(1), 339–352. https://doi.org/10.1007/s00034-015-0068-7

Ma, P., Zhang, H., Fan, W., & Wang, C. (2019). Early fault diagnosis of bearing based on frequency band extraction and improved tunable Q-factor wavelet transform. Measurement: Journal of the International Measurement Confederation, 137, 189–202. https://doi.org/10.1016/j.measurement.2019.01.036

Maulana, M. R., & Karomi, M. A. Al. (2015). INFORMATION GAIN UNTUK MENGETAHUI PENGARUH ATRIBUT TERHADAP KLASIFIKASI PERSETUJUAN KREDIT. JURNAL LITBANG KOTA PEKALONGAN, 9(1), 113–123. https://jurnal.pekalongankota.go.id/index.php/litbang/article/view/28

Mwadulo, M. W. (2016). A Review on Feature Selection Methods For Classification Tasks. International Journal of Computer Applications Technology and Research, 5(6), 395–402. https://doi.org/10.7753/IJCATR0506.1013

Noor, A. (2018). Perbandingan Algoritma Support Vector Machine Biasa dan Support Vector Machine berbasis Particle Swarm Optimization untuk Prediksi Gempa Bumi. Jurnal Humaniora Teknologi, 4(1), 31–37. https://doi.org/10.34128/jht.v4i1.37

Pratama, Y. T., Bachtiar, F. A., & Setiawan, N. Y. (2018). Analisis Sentimen Opini Pelanggan Terhadap Aspek Pariwisata Pantai Malang Selatan Menggunakan TF-IDF dan Support Vector Machine. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer (J-PTIIK) Universitas Brawijaya, 2(12), 6244–6252.

Pristyanto, Y., Adi, S., & Sunyoto, A. (2019). The effect of feature selection on classification algorithms in credit approval. 2019 International Conference on Information and Communications Technology, ICOIACT 2019, 451–456. https://doi.org/10.1109/ICOIACT46704.2019.8938523

Rizqi, U., Fatichah, C., & Purwitasari, D. (2017). Pembentukan Tesaurus pada Cross-Lingual Text dengan Pendekatan Constraint Satisfaction Problem. Jurnal Teknik ITS, 6(2). https://doi.org/10.12962/j23373539.v6i2.23686

Sakti, Z. (2016). Pengertian Ceramah, Jenis, komponen, metode, dan Contohnya.

Shaltout, N. A., El-Hefnawi, M., Rafea, A., & Moustafa, A. (2014). Information gain as a feature selection method for the efficient classification of influenza based on viral hosts. Lecture Notes in Engineering and Computer Science, 1(October 2016), 625–631.

Somantri, O., & Apriliani, D. (2018). SUPPORT VECTOR MACHINE BERBASIS FEATURE SELECTION UNTUK SENTIMENT ANALYSIS KEPUASAN PELANGGAN TERHADAP PELAYANAN WARUNG DAN RESTORAN KULINER KOTA TEGAL. Jurnal Teknologi Informasi Dan Ilmu Komputer (JTIIK), 5(5), 537–548. https://doi.org/10.25126/jtiik20185867

Published
2020-03-31
How to Cite
Aridarma, D., Sadikin, R., Prakoso, B., & Utama, H. (2020). USTADZ ABDUL SOMAD LECTURE SENTIMENT ANALYSIS USING SUPPORT VECTOR MACHINE ALGORITHM COMPARISON OF COMPARATIVE FEATURES SELECTION. Jurnal Pilar Nusa Mandiri, 16(1), 111-116. https://doi.org/10.33480/pilar.v16i1.702
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