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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Didi Rosiyadi
Universitas Bina Sarana Informatika
Computer Science & IT Engineering

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Integrasi N-gram, Information Gain, Particle Swarm Optimation di Naïve Bayes untuk Optimasi Sentimen Google Classroom Fajar Pramono; Didi Rosiyadi; Windu Gata
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 3 No 3 (2019): Desember 2019
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (561.234 KB) | DOI: 10.29207/resti.v3i3.1119

Abstract

The use of Learning Management System (LMS) applications made by Google with name Google Classroom since 2015 in junior and senior high schools in Bekasi City helps the learning process become easier. However, its use can have positive and negative effects on students. Google Class Sentiment by integrating N-grams, Information Gain, Particle Swarm Optimization, and Naïve Bayes Classifiers that have never been done by researchers before. From the experiments carried out, N-gram can increase the accuracy of 6.7% and AUC 4%, while using PSO can increase the Accuracy of 9.9% and AUC of 10.4%.
Co-Authors Fajar Pramono Windu Gata