English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

ABSTRACT 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. Keywords: analysis sentiment, text mining, word n-gram, support vector machine (SVM). ABSTRAK Aplikasi Sambara merupakan inovasi dari Bapenda Jabar untuk pelayanan pajak kendaraan bermotor. Aplikasi Sambara diharapkan memberikan efesiensi, efektifitas, dan perbaikan pelayanan. Keberhasilan aplikasi dapat diketahui dengan melakukan analysis sentiment review. Analysis sentiment bertujuan untuk mendeteksi polaritas di dalam teks berupa opini negatif atau positif., dengan menggunakan text mining . Pada tahapan text processing ditambahkan fitur Word N-Gram sebagai pendekatan identifikasi kata dan untuk klasifikasinya menggunakan metode Support Vector Machine (SVM). Penelitian ini bertujuan untuk mengetahui penerapan Word N-Gram, hasil nilai akurasi dengan menggunakan metode SVM, dan mengetahui seberapa besar pengaruh penerapan Word N-Gram terhadap nilai akurasi. Hasil nilai akurasi tertinggi pada penelitian ini sebesar 89.00% dengan nilai AUC 0.944 ( excellent classification ) pada jumlah data 900, namun saat dilakukan penerapan Bi-gram dan Tri-gram menghasilkan penurunan akurasi. Nilai akurasi dengan kenaikan tertinggi yaitu pada penerapan Tri-gram dengan jumlah data 1.200. Kenaikan nilai akurasi sebesar 0.92% dibandingkan dengan Uni-Gram menjadi 88.59% dengan nilai AUC 0.954. Kata Kunci: analysis sentiment, text mining, word n-gram, support vector machine (SVM)

PENERAPAN WORD N-GRAM UNTUK SENTIMENT ANALYSIS REVIEW MENGGUNAKAN METODE SUPPORT VECTOR MACHINE (STUDI KASUS: APLIKASI SAMBARA)