Article Per Year (5 Year)
p-Index From 2019 - 2024
0.562
P-Index
This Author published in this journals
All Journal Transformasi SmartComp
Ivan Rifky Hendrawan
Universitas Amikom Yogyakarta
Computer Science & IT Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Mathematics

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
PERBANDINGAN ALGORITMA NAÏVE BAYES, SVM DAN XGBOOST DALAM KLASIFIKASI TEKS SENTIMEN MASYARAKAT TERHADAP PRODUK LOKAL DI INDONESIA Ivan Rifky Hendrawan
TRANSFORMASI Vol 18, No 1 (2022): TRANSFORMASI
Publisher : STMIK BINA PATRIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56357/jt.v18i1.295

Abstract

Marketplace has become a popular online transaction medium with various features taken, one of the features that can be used for research is online reviews. Reviews can also be used as a data source for making various management decisions. Online reviews are very important in supporting purchasing decisions because of the development of e-commerce, there are more and more fake reviews so that more consumers are worried about online shopping. This cannot be denied because customer reviews can determine the level of customer satisfaction with the products that have been purchased. Sentiment analysis can be applied to Marketplace product reviews so that it can be used as product improvement suggestions for sellers and competitors so that they can find out what products are pleasing and needed by the community. Based on research that has been done, that the combination of Word2vec + XGBoost produces a higher F1 score of 0.941 followed by TF-IDF + XGBoost 0.940. Meanwhile, the SVM algorithm using vector space TF-IDF and Word2vec only produces 0.938 and 0.939. Meanwhile, Naïve Bayes has an F1-Score of 0.915 with TF-IDF and 0.900 with word2vec. Classification with word2vec in representing words into vectors is better than TF-IDF, this is because the advantages of word2vec are able to process semantic relations between words.
Analisis Perbandingan Metode Tf-Idf dan Word2vec pada Klasifikasi Teks Sentimen Masyarakat Terhadap Produk Lokal di Indonesia Ivan Rifky Hendrawan; Ema Utami; Anggit Dwi Hartanto
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 11, No 3 (2022): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v11i3.3902

Abstract

Secara umum, proses menghasilkan setiap ulasan produk pada dasarnya terkait dengan tingkat rating, yang membuat pengguna memberikan komentar yang bias.Analisis sentimen dapat diterapkan pada ulasan produk Marketplace sehingga dapat digunakan sebagai saran perbaikan produk untuk penjual dan pesaing sehingga dapat mengetahui produk apa yang disenangi dan dibutuhkan oleh masyarakat.Penelitian ini  menggunakan algoritma XGBoost dengan menggunakan dataset bahasa Indonesia yang dikombinasikan dengan TF-IDF dan Word2vec dan akan dievaluasi kombinasi mana yang lebih baik dalam mengklasifikasikan data teks yang tidak seimbang.Berdasarkan penelitian yang telah dilakukan, dua vector space TF-IDF dan Word2vec menghasilkan nilai F1-Score yang berbeda pada algoritma klasifikasi XGBoost, kombinasi Word2vec+XGboost menghasilkan nilai F1-Score lebih tinggi 0.941% dibanding TF IDF+XGBoost 0.940%. Hal ini dikarenakan word2vec lebih baik karena memiliki keunggulan dapat melihat hubungan semantik antar kata.Kata kunci: word2vec, tfidf, sentimen analisis, XGBoost,
Co-Authors Anggit Dwi Hartanto Ema Utami