Indonesian ID Card Recognition using Convolutional Neural Networks

Indonesian ID Card can be used to recognize citizen of Indonesia identity in several requirements like for sales and purchasing recording, admission and other transaction processing systems (TPS). Current TPS system used citizen ID Card by entering the data manually that means time consuming, prone to error and not efficient. In this research, we propose a model of citizen id card detection using state-of-the-art Deep Learning models: Convolutional Neural Networks (CNN). The result, we can obtain possitive accuracy citizen id card recognition using deep learning. We also compare the result of CNN with traditional computer vision techniques

Influence Factors of Social Media and Gadget Addiction of Adolescent in Indonesia

Social media user in Indonesia has growth rapidly since its emergence. In 2019, one of largest social media platform, Facebook has 3 billion users world wide user and 130 million users of them come from Indonesia. Moreover, the other social media like Instagram also has significantly growth with most of user are teenagers. Massive social media usage was caused by more than 100 million active users that use gadget or smartphone to open application like social media. Both of widely social media and gadget usage is not only have possitive impact but also negative impact like mental and behaviour problem if the user has been addicted. Hence the requirement of knowing influence factors of social media and gadget addiction in Indonesia is required in order to prevent addiction of social media and gadget. In this paper, the influence factors of social media and gadget addiction in Indonesia is investigated using several techniques like data science, partial least square, and structural equation modellin

A benchmark of modeling for sentiment analysis of the Indonesian Presidential Election in 2019

Researching with a machine learning method approach, the truth is to try to solve a case by using various algorithmic approaches to obtain the most suitable model for a case. In this research, we want to know which process of modelling that has the best accuracy value for classifying emotions in the text. The algorithm used is using the LSTM algorithm, while the benchmarking that we tested is the Random Forest and Naive Bayes algorithm. This research takes public opinion about the 2019 Indonesian Presidential Election by classifying it into four types of emotions: happy, sad, angry, and afraid. The data we use contains more than 1200 Indonesian tweets. In this experiment, we achieved an accuracy of 68.25% using the Random Forest model, whereas, with the Multinomial Naïve Bayes model, the accuracy was 66%