Algorithm Comparation of Naive Bayes and Support Vector Machine based on Particle Swarm Optimization in Sentiment Analysis of Freight Forwarding Services
| Main Authors: | Sharazita Dyah Anggita, Ikmah |
|---|---|
| Format: | Article info application/pdf Journal |
| Bahasa: | eng |
| Terbitan: |
Ikatan Ahli Informatika Indonesia (IAII)
, 2020
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| Subjects: | |
| Online Access: |
http://jurnal.iaii.or.id/index.php/RESTI/article/view/1840 http://jurnal.iaii.or.id/index.php/RESTI/article/view/1840/239 |
Daftar Isi:
- 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.
- Kebutuhan masyarakat akan ekspedisi barang saat ini mulai meningkat dengan adanya marketplace. Opini pengguna tentang pelayanan ekspedisi barang saat ini dilakukan masyarakat melalui banyak hal salah satunya media sosial Twitter. Dengan sentimen analisis, kecenderungan sebuah opini akan mampu terlihat apakah mempunyai kecenderungan positif atau negatif. Metode yang dapat diterapkan pada analisis sentimen yaitu Algoritma classifier Naive Bayes dan Support Vector Machine (SVM). Penelitian ini akan melakukan penerapan kedua algoritma tersebut yang dioptimasi menggunakan algoritma PSO pada analisis sentimen. Pengujian dilakukan dengan melakukan setting parameter pada PSO di masing-masing algoritma classifier. Hasil dari pengujian yang dilakukan mampu menghasilkan peningkatan akurasi sebesar 15.11% pada penerapan PSO di algoritma Naive Bayes. Peningkatan akurasi pada algoritma SVM berbasis PSO senilai 1.74% pada kernel sigmoid.