Analisis sentimen kritik dan saran pelatihan aplikasi teknologi informasi (PATI) menggunakan algoritma support vector machine (SVM)

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Alimuddin Hasan Al Kabir
Setio Basuki
Galih Wasis Wicaksono

Abstract

Public opinion is one of many instrument that can be used to evaluate of an event. This research based on several problems, they are (1) quality improvement are necessary for Pelatihan Aplikasi Teknologi Informasi, (2) too much of data make the participant’s opinion that has been collected has not been maximally utilized. Criticism and suggestions datas are taken from 2016/2017th school year in amount of 1050. Support Vector Machine is used as a method in sentiment analysis. The data training process will produce the best hyperplane used as a reference to determine which sentiment class is much appropriate for a sentence. The test is done by dividing the dataset into the test data as much as 20% and the training data as much as 80% so it can be done the analysis process up to 5 times iteration with different data arrangement. The test results show the calculation of Accuracy, Precision, Recall, and F-Measure generated by system is equal to 82,08%, 83,42%, 81,16%, and 81,82%.

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How to Cite
[1]
A. H. Al Kabir, S. Basuki, and G. W. Wicaksono, “Analisis sentimen kritik dan saran pelatihan aplikasi teknologi informasi (PATI) menggunakan algoritma support vector machine (SVM)”, JR, vol. 1, no. 1, Jan. 2024.
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Sains Data

References

Panitia PATI UMM, “Pelatihan Aplikasi Teknologi Informasi (PATI) Universitas Muhammadiyah Malang,” 2013. [Online]. Available: https://ppaiumm.wordpress.com/. [Accessed: 06-Sep-2017].

S. E. P. Widoyoko, “Evaluasi Program Pembelajaran,” J. Ilmu Pendidik., pp. 1–16, 2000.

D. Mardapi, “Pengukuran Penilaian dan Evaluasi Pendidikan,” Yogyakarta Nuha Med., 2012.

B. I. Page and R. Y. Shapiro, “Effect of Public Opinion on Policy.” pp. 175–190, 1983.

J. Riany, M. Fajar, and M. P. Lukman, “Penerapan Deep Sentiment Analysis pada Angket Penilaian Terbuka Menggunakan K-Nearest Neighbor,” vol. 6, no. 1, pp. 147–156, 2016.

M. DeHaff, “Sentiment Analysis, Hard But Worth It!,” 2010. [Online]. Available: http://customerthink.com/sentiment_analysis_hard_but_worth_it/. [Accessed: 03-Mar-2017].

V. Chandani, R. S. Wahono, and . Purwanto, “Komparasi Algoritma Klasifikasi Machine Learning Dan Feature Selection pada Analisis Sentimen Review Film,” J. Intell. Syst., vol. 1, no. 1, pp. 55–59, 2015.

A. Hamzah, “Sentiment Analysis untuk Memanfaatkan Saran Kuesioner Dalam Evaluasi Pembelajaran dengan Menggunakan Naive Bayes Classifier (NBC),” Snast, no. November, pp. 18–24, 2014.

B. Santosa, “Tutorial Support Vector Machine,” pp. 1–23, 1995.

K. Sembiring, “Tutorial SVM Bahasa Indonesia,” Training, no. September, pp. 1–28, 2007.

B. Boehm, Barry Boehm Software Engineering Economics, vol. 10, no. 1. Prentice-hall, 1984.

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