scholarly journals Community Analysis Sentiment Against Palestinian People with Naive Bayes Classification

Author(s):  
Hafiz Irsyad ◽  
Akhsani Taqwiym

Pada zaman teknologi 4.0 media social sangat pesat perkembangannya, ada yang menggunakan media social untuk berjualan, aksi pengumpulan dana, meberitakan kejadian secara live. Beberapa hari yang lalu, palestina mendapatkan agresi dari militer Israel sehingga jagad dunia mengetahui aksi yang sungguh sangat tidak terpuji yang dilakukan oleh militer Israel. Dari banyaknya informasi media online maka perlu dilakukan analisis sentiment terhadap agresi militer yang dilakukan kepada palestina. Data yang digunakan adalah salah satu platform media social yaitu Twitter. Penelitian ini dibuat untuk menganalisa tanggapan dari masyarakat dengan menggunakan data berupa tweet yang kemudian diklasifikasikan dengan metode naïve bayes. Berdasarkan tools yang digunakan adalah orange, maka didapatkan hasil sentiment positif 56%, sentiment negative 11% dan sentiment netral 33% dengan tingkatan akurasi 75%. Dari hasil tersebut telah membuktikan tingkat sentiment positif dari tweet masyarakat lebih besar dari pada tingkat sentiment negative bahkan netral.

2016 ◽  
Vol 97 ◽  
pp. 141-149 ◽  
Author(s):  
Hui Zhang ◽  
Zhi-Xing Cao ◽  
Meng Li ◽  
Yu-Zhi Li ◽  
Cheng Peng

2018 ◽  
Vol 14 (1) ◽  
pp. 155014771875603 ◽  
Author(s):  
Yao-Hua Ho ◽  
Yu-Te Huang ◽  
Hao-Hua Chu ◽  
Ling-Jyh Chen

Environmental sensors are important for collecting data to understand environmental changes and analyze environmental issues. In order to effectively monitor environmental changes, high-density sensor deployment and evenly distributed spatial distance between sensors become the requirements and desired properties for such applications. In many applications, sensors are deployed in locations that are difficult and dangerous to reach (e.g. mountaintop or skyscraper roof). To collect data from those sensors, unmanned aerial vehicles are used to act as data mules to overcome the problem of collecting data in challenging environments. In this article, we extend the adaptive return-to-home sensing algorithm with a parameter-tuning algorithm that combines naive Bayes classification and binary search to adapt adaptive return-to-home sensing parameters effectively on the fly. The proposed approach is able to (1) optimize number of sensing attempts, (2) reduce oscillation of the distance for consecutive attempts, and (3) reserve enough power for drone to return-to-home. Our results show that the naive Bayes classification–enhanced adaptive return-to-home sensing scheme is able to avoid oscillation in sensing and guarantees return-to-home feature while behaving more cost-effective in parameter tuning than the other machine learning–based approaches.


Sign in / Sign up

Export Citation Format

Share Document