Analisis Sentimen Terhadap Video Ulasan Produk Menggunakan Metode Support Vector Machine Dengan Sequential Minimal Optimization

Mohammad Aji Subarkah, Wenny Mistarika Rahmawati, Septiyawan Rosetya Wardhana, Rinci Kembang Hapsari


The popularity of video as a medium for reviewing a product today has created interest and dependence on other users to look for video recommendations before buying the desired effect. Youtube social media is one of the media that has product reviews in the form of videos. The use of sentiment analysis can predict the tendency of someone's video review to have a positive or negative opinion which can be done by processing the video review into text form first using speech recognition. In this study, thoughts that have been in the form of text will be followed by the process of tokenizing, weighting and classification by applying the Support Vector Machine (SVM) algorithm model using Sequential Minimal Optimization (SMO) optimization. Based on the results of this study, it shows that the accuracy, recall, precision and f-measure values will increase with the increasing number of terms tested, while the C value variable in the enhanced SMO cannot be retrieved because the resulting accuracy value fluctuates. The test results with term 300 and C 1.5 get the highest value: accuracy 89.91%, recall 89.12%, precision 94.97% and f-measure 91.05.


Analisis sentiment, Support vector machine, Sequential minimal optimization

Full Text:



C. Yan-Xu, L. Xiang-Guan, and G. Chuan-Hou, “Multiscale models on time series of silicon content in blast furnace hot metal based on Hilbert-Huang transform,” in 2011 Chinese Control and Decision Conference (CCDC), Mianyang, China, May 2011, pp. 842–847. doi: 10.1109/CCDC.2011.5968300.

C. Chairunnisa, I. Ernawati, and M. M. Santoni, “Klasifikasi Sentimen Ulasan Pengguna Aplikasi PeduliLindungi di Google Play Menggunakan Algoritma Support Vector Machine dengan Seleksi Fitur Chi-Square,” Inform. J. Ilmu Komput., vol. 18, no. 1, p. 69, Aug. 2022, doi: 10.52958/iftk.v17i4.4594.


V. Këpuska, “Comparing Speech Recognition Systems (Microsoft API, Google API And CMU Sphinx),” Int. J. Eng. Res. Appl., vol. 07, no. 03, pp. 20–24, Mar. 2017, doi: 10.9790/9622-0703022024.

A. R. Naradhipa and A. Purwarianti, “Sentiment Classification for Indonesian Message in Social Media”.

J. C. Platt, “Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines”.

N. Washani and S. Sharma, “Speech Recognition System: A Review,” Int. J. Comput. Appl., vol. 115, no. 18, pp. 7–10, Apr. 2015, doi: 10.5120/20249-2617.

V. I. Santoso, G. Virginia, and Y. Lukito, “PENERAPAN SENTIMENT ANALYSIS PADA HASIL EVALUASI DOSEN DENGAN METODE SUPPORT VECTOR MACHINE,” J. Transform., vol. 14, no. 2, p. 72, Jan. 2017, doi: 10.26623/transformatika.v14i2.439.

R. K. Hapsari, M. Miswanto, R. Rulaningtyas, and H. Suprajitno, “Identification of Diabetes Mellitus and High Cholesterol Based on Iris Image”.

R. K. Hapsari, M. I. Utoyo, R. Rulaningtyas, and H. Suprajitno, “Iris segmentation using Hough Transform method and Fuzzy C-Means method,” J. Phys. Conf. Ser., vol. 1477, no. 2, p. 022037, Mar. 2020, doi: 10.1088/1742-6596/1477/2/022037.

M. Sokolova and G. Lapalme, “A systematic analysis of performance measures for classification tasks,” Inf. Process. Manag., vol. 45, no. 4, pp. 427–437, Jul. 2009, doi: 10.1016/j.ipm.2009.03.002.



  • There are currently no refbacks.

Copyright (c) 2023 Rinci Kembang Hapsari, Mohammad Aji Subarkah, Wenny Mistarika Rahmawati, Septiyawan Rosetya Wardhana

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.