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Journal : Journal of Applied Data Sciences

Application of the Vector Machine Support Method in Twitter Social Media Sentiment Analysis Regarding the Covid-19 Vaccine Issue in Indonesia Riyanto Riyanto; Abdul Azis
Journal of Applied Data Sciences Vol 2, No 3: SEPTEMBER 2021
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v2i3.40


According to the Indonesian government, Indonesia has been afflicted by Covid-19 since March 2, 2020. Numerous countries, including Indonesia, have made efforts, but with the spread of perceptions, rumors, and a flood of information into the society regarding vaccines, there are both advantages and disadvantages to vaccines. government-led immunization campaigns. As a result, it is vital to examine public sentiment toward the government's immunization programs. The goal of this study is to ascertain the emotion toward the Covid-19 vaccination in Indonesia based on the classification results. The Support Vector Machine classification technique was employed in this investigation (SVM). The SVM classification method was chosen because it possesses the ability to generalize when it comes to identifying a pattern, excluding the data used in the method's learning phase. Classification with an SVM linear kernel and TF-IDF weighting, as well as data sharing via K-fold cross validation with a value of k=10. Positive and negative classifications are made. Following preprocessing and classification, we determined the f1 values, accuracy, precision, and recall to use as reference values when evaluating the classification. SVM performed well in classifying the data in this investigation, with  f1 = 88.7%, accuracy = 84.4%, precision = 86.2%, and recall = 97%. This value is acceptable, and hence SVM is suitable for identifying sentiment data about the Covid-19 vaccine in Indonesia. Additional study can be conducted with richer tweet data, more thorough preprocessing, and comparison to other classification algorithms to obtain a higher categorization evaluation score.
Training Autonomous Vehicles in Carla model using Augmented Random Search Algorithm Riyanto Riyanto; Abdul Azis; Tarwoto Tarwoto; Wei Li Deng
Journal of Applied Data Sciences Vol 2, No 2: MAY 2021
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v2i2.29


CARLA is an open source simulator for autonomous driving research. CARLA has been developed from scratch to support the development, training and validation of autonomous driving systems. In addition to open source code and protocols, CARLA provides open digital assets (urban layouts, buildings, vehicles) that are created for this purpose and can be used freely. We use CARLA to study the performance of Augmented Random Search (ARS) to autonomous driving: a classic modular pipeline, an end-to-end model trained via imitation learning, and an end-to-end model trained via reinforcement learning. Test the ability of the Augmented Random Search (ARS) algorithm to train driverless cars on data collected from the front cameras per car. In this study, a framework that can be used to train driverless car policy using ARS in Carla will be built. Although effective policies were not achieved after the first round of training, many insights on how to improve these outcomes in the future have been obtained.