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Perbandingan Algoritme Naïve Bayes dan Decision Tree Pada Analisis Sentimen Data Komentar Siswa Pada Aplikasi Digital Teacher Assessment

Ferat Kristanto(1*),Wing Wahyu Winarno(2),Asro Nasiri(3)
(1) Universitas Amikom Yogyakarta
(2) STIE YKPN
(3) Universitas Amikom Yogyakarta
(*) Corresponding Author
DOI : 10.35889/jutisi.v12i2.1193

Abstract

Telkom Purwokerto Vocational High School is a school managed by the Telkom Education Foundation. They use Digital Teacher Assessment (DITA) apps. Students provide comments to the teacher using the DITA application. The collected comment data will be grouped into three categories, namely positive, negative, and neutral. Based on the category of comments requires sentiment analysis in grouping these comments. Sentiment analysis uses lexicon-based. After getting sentiment analysis using lexicon-based, then the words are weighted using TF-IDF and then classified and evaluated. This study uses an algorithm naïve Bayes and a decision tree. So the results of the comparative research on the accuracy of the naïve Bayes algorithm and the decision tree with the decision tree algorithm have the highest level of accuracy, namely 99%. So it can be concluded that using the decision tree algorithm is better at classifying student comment sentiment analysis data.

Keywords: Digital Teacher Assessment; Naïve Bayes; Decision Tree; Lexicon Based; Sentiment Analysis

 

Abstrak

Sekolah Menengah Kejuruan (SMK) Telkom Purwokerto adalah sekolah yang dikelola oleh Yayasan Pendidikan Telkom. Mereka menggunakan aplikasi Digital Teacher Assessment (DITA). Siswa memberikan komentar ke Guru menggunakan aplikasi DITA. Data komentar yang terkumpul akan dikelompokkan menjadi tiga kategori yaitu komentar positif, negatif, dan netral. Berdasarkan kategori komentar membutuhkan analisis sentimen dalam mengelompokkan komentar tersebut. Analisis sentimen menggunaka lexicon based. Setelah mendapatkan analisis sentimen menggunakan lexicon based, selanjutnya kata-kata tersebut diberi bobot menggunakan TF-IDF lalu di klasifikasi dan di evaluasi. Dalam penelitian ini menggunakan algoritme Naïve bayes dan Decision tree. Maka hasil penelitian perbandingan akurasi dari algoritme Naïve bayes dan decision tree dengan algoritme Decision tree memiliki tingkat akurasi yang paling tinggi yaitu 99%. Maka dapat disimpulkan bahwa dengan menggunakan algoritme Decision tree lebih baik dalam mengklasifikasi data analisis sentimen komentar siswa.

 

Keywords


Digital Teacher Assessment; Naïve Bayes; Decision Tree; Lexicon Based; Analisis Sentimen

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