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

Intelligent Computational Model for Early Heart Disease Prediction using Logistic Regression and Stochastic Gradient Descent (A Preliminary Study) 基于Logistic回归和随机梯度下降的早期心脏病预测智能计算模型(初步研究)

2021 1st International Conference on Computer Science and Artificial Intelligence (ICCSAI) Pub Date : 2021-10-28 DOI: 10.1109/iccsai53272.2021.9609724

Eka Miranda, Faair M Bhatti, Mediana Aryuni, C. Bernando

{"title":"Intelligent Computational Model for Early Heart Disease Prediction using Logistic Regression and Stochastic Gradient Descent (A Preliminary Study)","authors":"Eka Miranda, Faair M Bhatti, Mediana Aryuni, C. Bernando","doi":"10.1109/iccsai53272.2021.9609724","DOIUrl":"https://doi.org/10.1109/iccsai53272.2021.9609724","url":null,"abstract":"Heart disease, also known as cardiovascular disease (CVDs) caused major death worldwide. Heart disease couldcan be diagnosed using non-invasive and invasive methods. The main distinctions for invasive and non-invasive tests were invasive test use medical equipment entering the human body while non-invasive tests did not. This study was designed a model for non-invasive prediction with an intelligent computational and machine learning approach for predicting early heart disease. Logistic regression and stochastic gradient descent applied for this model. A clinical dataset of 303 patients was gathered from the UCI repository that was available at http://archive.ics.uci.edu/ml/datasets/Heart+Disease. Age, Sex, Cp, Trestbps, Chol, Fbs, Exang Continuous Maximum heart rate achieved, Thalach, Old peak ST, Slope, Ca and Thal variables were used to classify the patient into two class prediction namely No presence or Have heart disease. Classifier performance for logistic regression namely accuracy 91.67%, precision 93.93%, F Measure 92.53%, recall 91.18% and for gradient descent namely accuracy 80.00%, precision 76.47%, F Measure 81.25%, recall, 86.67%. The experiment result revealed logistic regression gained higher accuracy, precision, F -measure and recall value than stochastic gradient descent.","PeriodicalId":426993,"journal":{"name":"2021 1st International Conference on Computer Science and Artificial Intelligence (ICCSAI)","volume":"45 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":"114678651","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

Aspect Based Sentiment Analysis: Restaurant Online Review Platform in Indonesia with Unsupervised Scraped Corpus in Indonesian Language 基于面向的情感分析:印尼餐馆在线评论平台的无监督印尼语抓取语料库

2021 1st International Conference on Computer Science and Artificial Intelligence (ICCSAI) Pub Date : 2021-10-28 DOI: 10.1109/iccsai53272.2021.9609794

Samuel Mahatmaputra Tedjojuwono, Clement Neonardi

{"title":"Aspect Based Sentiment Analysis: Restaurant Online Review Platform in Indonesia with Unsupervised Scraped Corpus in Indonesian Language","authors":"Samuel Mahatmaputra Tedjojuwono, Clement Neonardi","doi":"10.1109/iccsai53272.2021.9609794","DOIUrl":"https://doi.org/10.1109/iccsai53272.2021.9609794","url":null,"abstract":"The paper has designed a dynamic dashboard that will show a summarized information of restaurants in Indonesia on four distinct metrics which are Food, Service, Ambience and Covid Safety. Each metrics shown will have their own ratings which shows the detailed score for each aspect of the restaurant. The data inside the dashboard have been developed by using semi supervised learning of aspect-based sentiment analysis approach. The idea is to analyze past reviews/comments of each restaurant in the current restaurant's online review platform and extract the sentiment as well as the aspect of each of the reviews. The restaurant lists and the reviews have been collected through web scraping method on one of the most used online review platforms in Indonesia which is Tripadvisor. Scraped data has been cleaned through several process of data pre-processing by utilizing Sastrawi and NLTK library for Indonesian languages. The machine learning tools that will extract the aspect and sentiments in every of the reviews will be built by applying Monkeylearn machine learning platform through APIs. Cleaned datasets have been imported into the platform for data annotations of model training to identify the set of words belongs in each aspect categories as well as their sentiment values. Although after reaching the end of the analysis, this paper has concluded that accuracy of the analysis may not be ideal due to lack of negative sentiment dataset being gathered which affects the model during the training process. In conclusion, the feature has successfully been built and implemented as well as deployed into a web server which supported by Ngrok services however, there are still more room for improvement regarding the analysis of the model.","PeriodicalId":426993,"journal":{"name":"2021 1st International Conference on Computer Science and Artificial Intelligence (ICCSAI)","volume":"58 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":"116710684","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

Coronary Artery Disease Prediction Model using CART and SVM: A Comparative Study 基于CART和SVM的冠状动脉疾病预测模型的比较研究

2021 1st International Conference on Computer Science and Artificial Intelligence (ICCSAI) Pub Date : 2021-10-28 DOI: 10.1109/iccsai53272.2021.9609721

Mediana Aryuni, Eka Miranda, C. Bernando, Andrian Hartanto

引用次数: 2

IoT Sensors Integration for Water Quality Analysis 物联网传感器集成用于水质分析

2021 1st International Conference on Computer Science and Artificial Intelligence (ICCSAI) Pub Date : 2021-10-28 DOI: 10.1109/iccsai53272.2021.9609707

Hermantoro, Suparman, D. Ariyanto, R. Rahutomo, T. Suparyanto, B. Pardamean

引用次数: 0

Waste Classification Using EfficientNet-B0 使用高效网b0进行废物分类

2021 1st International Conference on Computer Science and Artificial Intelligence (ICCSAI) Pub Date : 2021-10-28 DOI: 10.1109/ICCSAI53272.2021.9609756

William Mulim, Muhammad Farrel Revikasha, Rivandi, Novita Hanafiah

引用次数: 3

The Impact of E-Transport Platforms' Gojek and Grab UI/UX Design to User Preference in Indonesia 印尼电子交通平台Gojek和Grab UI/UX设计对用户偏好的影响

2021 1st International Conference on Computer Science and Artificial Intelligence (ICCSAI) Pub Date : 2021-10-28 DOI: 10.1109/ICCSAI53272.2021.9609767

Henry Hamilton Prasetya, Bima Bagaskarta Ridwanto, Muhammad Ashraf Rahman, Alexander Agung Santoso Gunawan

引用次数: 2

Big Data For Smart City: An Advance Analytical Review 智慧城市的大数据:先进分析综述

2021 1st International Conference on Computer Science and Artificial Intelligence (ICCSAI) Pub Date : 2021-10-28 DOI: 10.1109/ICCSAI53272.2021.9609728

E. D. Madyatmadja, Asnan Habib Munassar, Sumarlin, A. Purnomo

{"title":"Big Data For Smart City: An Advance Analytical Review","authors":"E. D. Madyatmadja, Asnan Habib Munassar, Sumarlin, A. Purnomo","doi":"10.1109/ICCSAI53272.2021.9609728","DOIUrl":"https://doi.org/10.1109/ICCSAI53272.2021.9609728","url":null,"abstract":"The use of IOT over the centuries has evolved on many ways, many people have developed various of method on using these technologies, it has even reached to the point that it can even support the disaster response, even though not every system is perfect, there is always a room to improve trough maintenance and improving every development milestone. One of the biggest usages of IOT are mainly on big companies, cities, or even just used on daily basis by everyone. One of the biggest usages of these IOT technologies are on smart city. The development of smart city require many information from various department, since smart city is a big scope covering the daily basis of their citizen which covers many scope from energy, transportation, public services, security, and many more, these massive traffic data that are gathered to ensure the productivity on smart city to keep running on daily basis, which is why on this paper the author will be discussing on the challenges on implementing smart city, the role of big data on smart city, and an advance review on the systematic of the big data system on the smart city. The author of this paper hoped that the result of this research will benefit government understanding on how to build an appropriate system for smart city and its citizen.","PeriodicalId":426993,"journal":{"name":"2021 1st International Conference on Computer Science and Artificial Intelligence (ICCSAI)","volume":"36 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":"132764871","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: 1

Exploiting Facial Action Unit in Video for Recognizing Depression using Metaheuristic and Neural Networks 基于元启发式和神经网络的视频面部动作单元识别抑郁症

2021 1st International Conference on Computer Science and Artificial Intelligence (ICCSAI) Pub Date : 2021-10-28 DOI: 10.1109/iccsai53272.2021.9609747

H. Akbar, Sintia Dewi, Yuli Azmi Rozali, Lita Patricia Lunanta, Nizirwan Anwar, Djasminar Anwar

{"title":"Exploiting Facial Action Unit in Video for Recognizing Depression using Metaheuristic and Neural Networks","authors":"H. Akbar, Sintia Dewi, Yuli Azmi Rozali, Lita Patricia Lunanta, Nizirwan Anwar, Djasminar Anwar","doi":"10.1109/iccsai53272.2021.9609747","DOIUrl":"https://doi.org/10.1109/iccsai53272.2021.9609747","url":null,"abstract":"The ubiquity of coronavirus cases around the world has been severe and its impact is not only affecting the economy and physical health, but also mental health such as depression. Unfortunately, the number of coronavirus cases may inhibit people to look for general practitioners or hospitals. This study represents research on facial behaviour analysis on recognizing depression from facial action units extracted from images or videos. We aimed to find a reduced set of facial action unit features using the metaheuristic approach. We utilized particle swarm optimization to select the best predictors and feed them to optimized standard feedforward neural networks. We obtained 97.83% accuracy for depression detection based on Distress Analysis Interview Corpus Wizard-of-Oz (DAIC WOZ) database containing 189 video sessions associated with the Patient Health Questionnaire depression label. This level of accuracy requires almost 9 minutes. However, this level of accuracy is higher than other state-of-the-art methods.","PeriodicalId":426993,"journal":{"name":"2021 1st International Conference on Computer Science and Artificial Intelligence (ICCSAI)","volume":"146 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":"133726052","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: 6

Design of Water Information Management System in Palm Oil Plantation 棕榈油种植园用水信息管理系统的设计

2021 1st International Conference on Computer Science and Artificial Intelligence (ICCSAI) Pub Date : 2021-10-28 DOI: 10.1109/iccsai53272.2021.9609780

A. Krisdiarto, Eddy Julianto, Irya Wisnubhadra, T. Suparyanto, D. Sudigyo, B. Pardamean

{"title":"Design of Water Information Management System in Palm Oil Plantation","authors":"A. Krisdiarto, Eddy Julianto, Irya Wisnubhadra, T. Suparyanto, D. Sudigyo, B. Pardamean","doi":"10.1109/iccsai53272.2021.9609780","DOIUrl":"https://doi.org/10.1109/iccsai53272.2021.9609780","url":null,"abstract":"The water level on peatlands is a critical factor in the production of oil palm plantations on peatlands because oil palm requires water but should not be inundated. The optimal water depth from the surface should be controlled from 70 to 80 cm by opening or closing the drain gate. Currently, most measurements are made with a piezometer. Then the opening and closing of the sluice gate at the end of the primary channel are done manually. In many cases, the distance between the plantation block and the floodgate is quite far and is limited by inaccessible infrastructure (roads), so opening or closing take a lot of time and money. Currently, a water level measurement system has been designed automatically using a microcontroller. This study aims to develop a design of a drainage management information system in oil palm plantations. The information system includes the water level of peatlands data in oil palm plantations taken from the water level sensor in the drainage system. The system uses the data from the water level sensor to manage the drainage system's sluice gates. This system was developed using the SDLC waterfall method. The final result of this research was a design of a water information management system that can regulates automatic and real-time peatland oil palm plantations water level.","PeriodicalId":426993,"journal":{"name":"2021 1st International Conference on Computer Science and Artificial Intelligence (ICCSAI)","volume":"122 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":"123556203","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: 1

Level of Password Vulnerability 密码漏洞级别

2021 1st International Conference on Computer Science and Artificial Intelligence (ICCSAI) Pub Date : 2021-10-28 DOI: 10.1109/iccsai53272.2021.9609778

Indira Mannuela, Jessy Putri, Michael, Maria Susan Anggreainy

引用次数: 2