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Implementation of the Jaccard Similarity Algorithm on Answer Type Description Riyanto Riyanto; Abdul Azis
International Journal of Informatics and Information Systems Vol 5, No 2: March 2022
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v5i2.130

Abstract

This study aims to measure the similarity of the answers to the description by using alternative answers as reference answers provided by the lecturer with a view to overcoming the diversity of student answers. This research focuses more on Indonesian language questions and answers by combining the jaccard similarity algorithm and keyword similarity. The results obtained indicate that by adding alternative reference answers, it can increase the correlation value to 0.78% and reduce MAE to 0.55. Likewise, after combining the jaccard similarity algorithm and keyword similarity, the correlation value increased to 0.78% and MAE decreased to 0.49.
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

Abstract

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.