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Alimuddin Hasan Al Kabir
a:1:{s:5:"en_US";s:31:"Universitas Muhammadiyah Malang";}

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Analisis sentimen kritik dan saran pelatihan aplikasi teknologi informasi (PATI) menggunakan algoritma support vector machine (SVM) Alimuddin Hasan Al Kabir; Setio Basuki; Galih Wasis Wicaksono
Jurnal Repositor Vol. 1 No. 1 (2019): November 2019
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/repositor.v1i1.30414

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%.
Analisis sentimen kritik dan saran pelatihan aplikasi teknologi informasi (PATI) menggunakan algoritma support vector machine (SVM) Alimuddin Hasan Al Kabir; Setio Basuki; Galih Wasis Wicaksono
Jurnal Repositor Vol. 1 No. 1 (2019): November 2019
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/repositor.v1i1.31817

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%.
Co-Authors Basuki, Setio Wicaksono, Galih Wasis