Implementing deep learning-based named entity recognition for obtaining narcotics abuse data in Indonesia

Abstract

<span lang="EN-US">The availability of drug abuse data from the official website of the National Narcotics Board of Indonesia is not up-to-date. Besides, the drug reports from Indonesian National Narcotics Board are only published once a year. This study aims to utilize online news sites as a data source for collecting information about drug abuse in Indonesia. In addition, this study also builds a named entity recognition (NER) model to extract information from news texts. The primary NER model in this study uses the convolutional neural network-long short-term memory (CNNs-LSTM) architecture because it can produce a good performance and only requires a relatively short computation time. Meanwhile, the baseline NER model uses the bidirectional long short-term memory-conditional random field (Bi-LSTMs-CRF) architecture because it is easy to implement using the Flair framework. The primary model that has been built results in a performance (F1 score) of 82.54%. Meanwhile, the baseline model only results in a performance (F1 score) of 69.67%. Then, the raw data extracted by NER is processed to produce the number of drug suspects in Indonesia from 2018-2020. However, the data that has been produced is not as complete as similar data sourced from Indonesian National Narcotics Board publications.</span>