Jurnal SINTA 2
Pengaruh Word Affect Intensities Terhadap Deteksi Ulasan Palsu
Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK) · Vol. 9 No. 2 (2022) · pp. 427–434
- ISSN
- 2528-6579, 2355-7699
- Published
- 2022-02-24
- Publisher
- Fakultas Ilmu Komputer, Universitas Brawijaya
- Language
- id
Abstract
Transaksi jual beli elektronik melalui internet terus berkembang dan menjadi populer, begitu pula dengan jumlah ulasan dari pelanggan yang meningkat pesat. Dengan banyaknya pemberi ulasan, terdapat kemungkinan seseorang menulis ulasan palsu yang disebut fake review untuk mempromosikan produk atau menjatuhkan produk kompetitor. Sangat penting untuk dapat mendeteksi ulasan palsu sehingga ulasan yang digunakan pelanggan sebagai pertimbangan untuk memilih produk atau jasa merupakan ulasan yang andal. Studi sebelumnya hanya menggunakan fitur sentimen yang terbatas pada objektivitas dan polaritas untuk melakukan deteksi ulasan palsu. Sedangkan studi yang lebih baru menunjukan adanya leksikon kosa kata berbasis emosi yang diberi nama word affect intensities yang terbukti mampu mengukur sentimen dengan lebih baik pada kalimat. Penelitian ini bermaksud untuk mengetahui apakah word affect intensities dapat menjadi faktor yang mempengaruhi hasil deteksi ulasan palsu. Penelitian dilakukan dengan memunculkan dua fitur baru berlandaskan word affect intensities berupa fitur kelompok emosi positif dan fitur kelompok emosi negatif. Fitur tersebut kemudian dikombinasikan dengan fitur pada penelitian sebelumnya dan dievaluasi menggunakan beberapa algoritme klasifikasi. Hasil penelitian menunjukan word affect intensities dapat menjadi faktor yang mempengaruh peningkatan akurasi deteksi ulasan palsu sebesar 2.1%. Abstract Electronic buying and selling transactions over the internet continue to grow and become popular, as well as the number of reviews from customers that is increasing rapidly. With so many reviewers, it is possible that someone wrote a fake review to promote a product or demote a competitor’s product. It is very important to be able to detect fake review so that the reviews customers use as a consideration for choosing a product or service are reliable reviews. Previous studies only used sentiment features that were limited to objectivity and polarity to detect fake review. Meanwhile, a more recent study shows that there is an emotion-based vocabulary lexicon called word affect intensities which are proven to be able to better measure sentiment in sentences. This study intends to determine whether word affect intensities can be a factor that affects the results of fake review detection. The research was conducted by bringing up two new features based on the word affect intensities in the form of positive emotion group features and negative emotion group features. These features are then combined with features in previous studies and evaluated using several classification algorithms. The results showed that word affect intensities can be a factor that affects the increased accuracy of fake review detection by 2.1%.
Collaboration network of the authors
40 authors · 71 co-authorship links · showing the 40 most connected
Hover or tap a name to highlight its co-authors. Double-click to open the author's page.
- University of Brawijaya
- Affiliation unknown
- Different organisations
- Same organisation
- Affiliation unknown
- Thicker line = more joint articles · bigger circle = more articles
Show as table
| Co-author | Organisation | Joint articles |
|---|---|---|
| Fajar Pradana | University of Brawijaya | 10 |
| Novanto Yudistira | University of Brawijaya | 4 |
| Retno Indah Rokhmawati | University of Brawijaya | 3 |
| Fitri Utaminingrum | University of Brawijaya | 3 |
| Bayu Priyambadha | University of Brawijaya | 2 |
| Satrio Agung Wicaksono | University of Brawijaya | 2 |
| Achmad Basuki | University of Brawijaya | 2 |
| Imam Cholissodin | University of Brawijaya | 2 |
| Muhammad Wafi | University of Brawijaya | 2 |
| Herman Tolle | University of Brawijaya | 2 |
| Aisyah Awalina | University of Brawijaya | 2 |
| Joseph Ananda Sugihdharma | University of Brawijaya | 1 |
| Rizal Setya Perdana | University of Brawijaya | 1 |
| Nathanael Victor Darenoh | University of Brawijaya | 1 |
| Aditya Rachmadi | University of Brawijaya | 1 |
| Indriati . | Affiliation unknown | 1 |
| Aulia Akhrian Syahidi | University of Brawijaya | 1 |
| Fajerin Biabdillah | University of Brawijaya | 1 |
| Nanang Yudi Setiawan | University of Brawijaya | 1 |
| Iqbal Taufiq Ahmad Nur | University of Brawijaya | 1 |
| Indra Syahputra | University of Brawijaya | 1 |
| Bayu Sutawijaya | University of Brawijaya | 1 |
| Mochammad Dearifaldi Al Ikhsan | University of Brawijaya | 1 |
| Sulandri Sulandri | University of Brawijaya | 1 |
| Rona Salsabila | University of Brawijaya | 1 |
| Muhammad Zulfikarrahman | University of Brawijaya | 1 |
| Diva Fardiana Risa | University of Brawijaya | 1 |
| Aisyatul Maulidah | University of Brawijaya | 1 |
| Intan Yusuf Habibie | University of Brawijaya | 1 |
| Adam Syarif Hidayatullah | University of Brawijaya | 1 |
| Ovy Rochmawanti | University of Brawijaya | 1 |
| M. Sofyan Irwanto | University of Brawijaya | 1 |
| Admi Rut Sinana | University of Brawijaya | 1 |
| Kharis Alfian | University of Brawijaya | 1 |
| Karina Amadea | University of Brawijaya | 1 |
| Budi Darma Setiawan | University of Brawijaya | 1 |
| Muhammad Nugraha Delta Revanza | Affiliation unknown | 1 |
Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.