2021 1st International Conference on Computer Science and Artificial Intelligence (ICCSAI)最新文献_第3页

Enhancement Design for Smart Parking System Using IoT and A-Star Algorithm 基于物联网和A-Star算法的智能停车系统增强设计

2021 1st International Conference on Computer Science and Artificial Intelligence (ICCSAI) Pub Date : 2021-10-28 DOI: 10.1109/iccsai53272.2021.9609779

Briant Stevanus, Suharjito, A. A. Sukmandhani

引用次数: 0

AR - Mart: The Implementation of Augmented Reality as a Smart Self-Service Cashier in the Pandemic Era AR - Mart:流行病时代增强现实作为智能自助收银的实现

2021 1st International Conference on Computer Science and Artificial Intelligence (ICCSAI) Pub Date : 2021-10-28 DOI: 10.1109/iccsai53272.2021.9609740

Chasandra Puspitasari, Gusti Pangestu, Anita Rahayu, Bening Insaniyah Al-Abdillah

引用次数: 3

Spatiotemporal Features Learning from Song for Emotions Recognition with Time Distributed CNN 基于时间分布CNN的歌曲时空特征学习情绪识别

2021 1st International Conference on Computer Science and Artificial Intelligence (ICCSAI) Pub Date : 2021-10-28 DOI: 10.1109/iccsai53272.2021.9609722

Andry Chowanda

{"title":"Spatiotemporal Features Learning from Song for Emotions Recognition with Time Distributed CNN","authors":"Andry Chowanda","doi":"10.1109/iccsai53272.2021.9609722","DOIUrl":"https://doi.org/10.1109/iccsai53272.2021.9609722","url":null,"abstract":"Building a system that can naturally interact with humans has been one of the ultimate goals for researchers in the computer science field. The system should be able to interpret both verbal and non-verbal meanings from the messages conveyed by the interlocutors. A song can also be a vehicle to express a message to the listeners, and capturing the emotions from the song automatically can provide a system that can have the digital feeling when they are listening to the song. Emotions can be automatically captured and processed through several modalities via sensors. Deep learning has been the golden standard of learning architecture in many fields. The emotions recognition model can be trained well with some of the deep learning architectures. Convolution Neural Networks (CNN) is famous to train models that have multi-dimensional input features. However, it has a limitation when dealing with features that have temporal information. This research aims to use Time Distributed layers to CNN architecture to learn Spatio-temporal features from the songs (audio signals). Eight architectures were proposed in this research to explore the potential of learning Spatio-temporal features from songs with CNN architecture. The best model presented in this paper achieved 99.95%, 93.41 %, 1.84, 2.03 in training accuracy, testing accuracy, training loss and testing loss, respectively.","PeriodicalId":426993,"journal":{"name":"2021 1st International Conference on Computer Science and Artificial Intelligence (ICCSAI)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126329543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Indonesia China Trade Relations, Social Media and Sentiment Analysis: Insight from Text Mining Technique 印尼与中国贸易关系、社交媒体与情感分析:基于文本挖掘技术的洞察

2021 1st International Conference on Computer Science and Artificial Intelligence (ICCSAI) Pub Date : 2021-10-28 DOI: 10.1109/iccsai53272.2021.9609735

Eka Miranda, Rangga Aditya Elias, T. M. Kibtiah, A. Permana

引用次数: 1

Exploration of React Native Framework in designing a Rule-Based Application for healthy lifestyle education React Native框架在健康生活方式教育基于规则应用设计中的探索

2021 1st International Conference on Computer Science and Artificial Intelligence (ICCSAI) Pub Date : 2021-10-28 DOI: 10.1109/iccsai53272.2021.9609763

Anik Hanifatul Azizah, Siti Zuliatul Faidah, Muhammad Bahrul Ulum, Putri Handayani

{"title":"Exploration of React Native Framework in designing a Rule-Based Application for healthy lifestyle education","authors":"Anik Hanifatul Azizah, Siti Zuliatul Faidah, Muhammad Bahrul Ulum, Putri Handayani","doi":"10.1109/iccsai53272.2021.9609763","DOIUrl":"https://doi.org/10.1109/iccsai53272.2021.9609763","url":null,"abstract":"Researches indicates the implementation of hybrid applications is more profitable for mobile application development solutions. Hybrid application combine the advantages of web and native application. React Native is a hybrid framework for developing mobile applications. React Native framework can create within two platform applications by compiling the code written in React. The utilization of React Native in a rule-based application can build a solution for healthy lifestyle education. The aim of this study is to build a rule-based application for healthy reminder in daily activities. By developing an application in React Native, the study will design a comprehensive mobile application that make users easy to use. Moreover, this study will explore and construct an application that guide the user to maintain their daily health. To develop the application, author uses waterfall methodology. Before building the application, a systematic survey was conducted to gain relevant data from the users and also to invent a rule-based that will be the way of thinking of the application design. The results indicate that React Native framework can be utilized in building a reminder application about healthy lifestyle education. This study built a mobile application product that has good performance and helps users change the lifestyle in order to improve the quality of a healthy lifestyle.","PeriodicalId":426993,"journal":{"name":"2021 1st International Conference on Computer Science and Artificial Intelligence (ICCSAI)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121071343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Towards Classification of Personality Prediction Model: A Combination of BERT Word Embedding and MLSMOTE 面向分类的人格预测模型:BERT词嵌入与MLSMOTE的结合

2021 1st International Conference on Computer Science and Artificial Intelligence (ICCSAI) Pub Date : 2021-10-28 DOI: 10.1109/iccsai53272.2021.9609750

Henry Lucky, Roslynlia, Derwin Suhartono

{"title":"Towards Classification of Personality Prediction Model: A Combination of BERT Word Embedding and MLSMOTE","authors":"Henry Lucky, Roslynlia, Derwin Suhartono","doi":"10.1109/iccsai53272.2021.9609750","DOIUrl":"https://doi.org/10.1109/iccsai53272.2021.9609750","url":null,"abstract":"The rise in internet usage improved digital communication and an increase in user data, particularly on social media. The information supplied from social media, including Twitter, can be used to retrieve user personality. In this paper, we experiment to predict user's personality based on Big Five Personality Trait on Twitter, particularly Indonesian users. We focus on using XGBoost classifier as it gives promising result in the previous study. We experiment on using multiple Bidirectional Encoder Representations from Transformer (BERT) models for extracting contextual word embeddings from tweets data to see the best model. We also address the imbalanced dataset problem with Multilabel Synthetic Minority Oversampling Technique (MLSMOTE). Our research found that the IndoBERT model, which is pre-trained with general data including Indonesian Twitter tweets, has the best overall performance on our dataset. We also found that using MLSMOTE could increase the accuracy up to 19,91% and the F1 up to 19,38%, which is a huge increment and shows that MLSMOTE works well with our dataset.","PeriodicalId":426993,"journal":{"name":"2021 1st International Conference on Computer Science and Artificial Intelligence (ICCSAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130811236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Indonesian Banking Stock Price Prediction with LSTM and Random Walk Method 基于LSTM和随机漫步法的印尼银行股价格预测

2021 1st International Conference on Computer Science and Artificial Intelligence (ICCSAI) Pub Date : 2021-10-28 DOI: 10.1109/iccsai53272.2021.9609752

Mike Christ Heru, R. N. Rachmawati, Derwin Suhartono

{"title":"Indonesian Banking Stock Price Prediction with LSTM and Random Walk Method","authors":"Mike Christ Heru, R. N. Rachmawati, Derwin Suhartono","doi":"10.1109/iccsai53272.2021.9609752","DOIUrl":"https://doi.org/10.1109/iccsai53272.2021.9609752","url":null,"abstract":"Investing in stock market is the challenging for every new investor, as the stock market always move in dynamic way. When using technical or fundamental analysis approach, investor can reduce the loss probability and increase the profit probability. When one tries to analyze the stock market data, any techniques can be used. For example, the LSTM as the part of Neural Network and Machine Learning, which need past data to train the model and try to give the best prediction result based on the model generated by the data. The other example of techniques used in this paper is the Random Walk which come from Integrated Nested Laplace Approximation (INLA) library of R language which approximate the Bayesian Inference. Both methods are used to get the best prediction result. To get some comparison, the data can be split to several period and from several choices, the best result can be generated. As a result, the LSTM always predict the best result (comparison using the RMSEP / Root Mean Square Error for Prediction value) and the more data fed to the model will produce lower rate of RMSE, which is good for prediction result.","PeriodicalId":426993,"journal":{"name":"2021 1st International Conference on Computer Science and Artificial Intelligence (ICCSAI)","volume":"252 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134191970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sentiment Analysis of E-commerce Review using Lexicon Sentiment Method 基于Lexicon情感分析法的电子商务评论情感分析

2021 1st International Conference on Computer Science and Artificial Intelligence (ICCSAI) Pub Date : 2021-10-28 DOI: 10.1109/iccsai53272.2021.9609786

Michael Hakkinen, Ferry Agustius Wong, Maria Susan Anggreainy, Wahyu Hidayat

引用次数: 3

Developing An Automated Face Mask Detection Using Computer Vision and Artificial Intelligence 基于计算机视觉和人工智能的人脸自动检测

2021 1st International Conference on Computer Science and Artificial Intelligence (ICCSAI) Pub Date : 2021-10-28 DOI: 10.1109/ICCSAI53272.2021.9609768

Samuel Mahatmaputra Tedjojuwono, Sheryl Livia Sulaiman

{"title":"Developing An Automated Face Mask Detection Using Computer Vision and Artificial Intelligence","authors":"Samuel Mahatmaputra Tedjojuwono, Sheryl Livia Sulaiman","doi":"10.1109/ICCSAI53272.2021.9609768","DOIUrl":"https://doi.org/10.1109/ICCSAI53272.2021.9609768","url":null,"abstract":"As the number of people affected by COVID-19 keeps on rising. Importance of wearing masks and washing hands has been the most important protocol right now to prevent the spread of COVID-19. As the pandemic has been going on for almost a year now, people have already started to go around to public places whether it is to eat out, work, or grocery shopping. Many people, however, have not been wearing masks properly by only putting them below their nose or putting it down until their chin. Hence, in this project a mask detection system is made to detect people live time who are wearing or not wearing a mask and can generate a business intelligence report for the shop owner to be aware of the number of people not wearing a mask per day. This system can detect the percentage of the mask is worn properly or not. The more proper it is worn (full up to nose), the higher the percentage will be. This system is useful in a pandemic like this as it is hard to keep track of the number of people who are not wearing masks, especially in a big crowd or in a large space as one person not wearing a mask can greatly affect others.","PeriodicalId":426993,"journal":{"name":"2021 1st International Conference on Computer Science and Artificial Intelligence (ICCSAI)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116943902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparative of Advanced Sorting Algorithms (Quick Sort, Heap Sort, Merge Sort, Intro Sort, Radix Sort) Based on Time and Memory Usage 基于时间和内存使用的高级排序算法(快速排序，堆排序，合并排序，引入排序，基数排序)的比较

2021 1st International Conference on Computer Science and Artificial Intelligence (ICCSAI) Pub Date : 2021-10-28 DOI: 10.1109/iccsai53272.2021.9609715

Marcellino Marcellino, Davin Pratama, Steven Santoso Suntiarko, K. Margi

引用次数: 3