Integrasi N-gram, Information Gain, Particle Swarm Optimation di Naïve Bayes untuk Optimasi Sentimen Google Classroom

Pramono, Fajar; Rosiyadi, Didi; Gata, Windu

doi:10.29207/resti.v3i3.1119

Cited by 12 publications

(13 citation statements)

References 9 publications

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“…Then the research conducted by (Dewi et al, 2020) stated that google classroom can improve students' scores in reading comprehension. Another study was also conducted by (Pramono et al, 2019) which stated that the use of N-gram in improving the optimization of sentiment results in google classroom can increase accuracy by 6.7 and AUC by 4% compared to not using N-gram. The purpose of this study is to describe the implementation of based e-learning-ICT learning using the Google Classroom application during the Covid-19 pandemic in public high schools.…”

Section: Introductionmentioning

confidence: 99%

Utilization of E-Learning-Based ICT Learning Using the Google Classroom Application During the COVID-19 Pandemic

Ali

Maksum

2020

JERE

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The impact of the Covid-19 pandemic has brought changes to the learning process, from face-to-face at school to online distance learning called e-learning. The research objective is to describe the implementation of e-learning-based ICT learning using the Google Classroom application during the Covid-19 pandemic in public high schools. The research used a qualitative approach and was conducted on class X MIA students. 1. Data collection was carried out by observation, interviews, and documentation study. Data analysis was carried out, namely data reduction, data presentation, and concluding. The results showed that the use of the google classroom application made the ICT learning process easier during the covid-19 period. The advantages of the google classroom application as a learning medium are: (1) it has flexibility in terms of time and place; (2) the application is easy to understand and operate; (3) The use of applications is effective and efficient; (4) the learning evaluation process is more accurate. This research concludes that the google classroom application facilitates the ICT learning process during the covid-19 period.

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Section: Introductionmentioning

confidence: 99%

Utilization of E-Learning-Based ICT Learning Using the Google Classroom Application During the COVID-19 Pandemic

Ali

Maksum

2020

JERE

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“…Salah satu cara untuk dapat mengoptimasikan performa kedua algoritma tersebut dapat dilakukan dengan Particle Swarm Optimization (PSO) .Sebagai alat pemilihan fitur, dengan partikel PSO akan mampu memberikan sebuah kombinasi fitur sebuah ruang masalah [11]. Penelitian mengenai klasifikasi Google Clasroom yang mengintegrasikan beberapa algoritma untuk mengoptimasi algoritma Naive Bayes menghasilkansebuah kesimpulan bahwa penggunaan algoritma PSO pada Naive Bayes mampu meningkatkan akurasi sampai dengan 10% [12]. Selanjutnya penelitian tentang penerapan PSO pada algoritma Naive Bayes pada analisis sentinmen review hotel.…”

Section: Pendahuluanunclassified

Algorithm Comparation of Naive Bayes and Support Vector Machine based on Particle Swarm Optimization in Sentiment Analysis of Freight Forwarding Services

Anggita

Ikmah

2020

RESTI

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The needs of the community for freight forwarding are now starting to increase with the marketplace. User opinion about freight forwarding services is currently carried out by the public through many things one of them is social media Twitter. By sentiment analysis, the tendency of an opinion will be able to be seen whether it has a positive or negative tendency. The methods that can be applied to sentiment analysis are the Naive Bayes Algorithm and Support Vector Machine (SVM). This research will implement the two algorithms that are optimized using the PSO algorithms in sentiment analysis. Testing will be done by setting parameters on the PSO in each classifier algorithm. The results of the research that have been done can produce an increase in the accreditation of 15.11% on the optimization of the PSO-based Naive Bayes algorithm. Improved accuracy on the PSO-based SVM algorithm worth 1.74% in the sigmoid kernel.

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“…Penambahan proses n-gram ini telah dilakukan oleh beberapa penelitian yang menghasilkan penambahan nilai akurasi. Pada penelitian yang dilakukan oleh Indrayuni & Wahyudin [7] mendapatkan peningkatan akurasi 2%, sedangkan pada penelitian yang dilakukan oleh Pramono hasil akurasinya meningkat 6,7% [8].…”

Section: Pendahuluanunclassified

Penerapan Word N-Gram Untuk Sentiment Analysis Review Menggunakan Metode Support Vector Machine (Studi Kasus: Aplikasi Sambara)

Fitriyani¹,

Arifin²

2020

SISTEMASI

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Sambara application is an innovation from Bapenda West Java for motor vehicle tax services. The Sambara application expected can be provide efficiency, effectiveness and service improvement. The success of the application can be determined by conducting a sentiment review analysis. Sentiment analysis aims to detect polarity in the text in the form of negative or positive opinions, using text mining. At the text processing stage, the Word N-Gram feature is added as a word identification approach and for classification it uses the Support Vector Machine (SVM) method. This study aims to determine the application of Word N-Gram, the results of the accuracy value using the SVM method, and find out how much influence the application of Word N-Gram on the accuracy value. The highest accuracy value in this research was 89.00% with AUC value of 0.944 (excellent classification) on the amount of data 900, but when uses Bi-gram and Tri-gram results in a decrease in accuracy. The accuracy value with the highest increase is in the application of tri-grams with the amount of 1,200 data. Increase in accuracy value by 0.92% compared to Uni-Gram to 88.59% with AUC value of 0.95.

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Integrasi N-gram, Information Gain, Particle Swarm Optimation di Naïve Bayes untuk Optimasi Sentimen Google Classroom

Cited by 12 publications

References 9 publications

Utilization of E-Learning-Based ICT Learning Using the Google Classroom Application During the COVID-19 Pandemic

Utilization of E-Learning-Based ICT Learning Using the Google Classroom Application During the COVID-19 Pandemic

Algorithm Comparation of Naive Bayes and Support Vector Machine based on Particle Swarm Optimization in Sentiment Analysis of Freight Forwarding Services

Penerapan Word N-Gram Untuk Sentiment Analysis Review Menggunakan Metode Support Vector Machine (Studi Kasus: Aplikasi Sambara)

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