2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)最新文献_第5页

Sentiment Analysis on Social Security Administrator for Health Using Recurrent Neural Network 基于递归神经网络的卫生社会保障管理员情感分析

2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) Pub Date : 2021-12-16 DOI: 10.1109/ISRITI54043.2021.9702816

Faisal Faturohman, Budhi Irawan, C. Setianingsih

{"title":"Sentiment Analysis on Social Security Administrator for Health Using Recurrent Neural Network","authors":"Faisal Faturohman, Budhi Irawan, C. Setianingsih","doi":"10.1109/ISRITI54043.2021.9702816","DOIUrl":"https://doi.org/10.1109/ISRITI54043.2021.9702816","url":null,"abstract":"Twitter is a social media used to convey opinions, exchange information, upload videos and photos. On social media Twitter, the exchange of information is fast becoming an advantage, so it is often used in delivering news and opinions in the form of criticism and suggestions such as to government agencies, for example, every time there is an issue of increasing dues to Social Security Administrator for Health, it is always a battle of opinion between the public. Social Security Administrator for Health is a government agency that guarantees the health of the Indonesian people; in this case, civil servants and private workers are required to register for this insurance and insurance for the poor. Opinion wars related to the issue of increasing insurance contributions between the public in the form of positive and negative opinions, a sentiment analysis system will be created using the Recurrent Neural Network classification method. This system can help analyze opinions based on people's perspectives on Twitter social media, from the research results in the sentiment analysis of Twitter users, with an average accuracy of 86.67%.","PeriodicalId":156265,"journal":{"name":"2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126934221","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

Object Detection for Autonomous Vehicle using Single Camera with YOLOv4 and Mapping Algorithm 基于YOLOv4和映射算法的单摄像头自动驾驶车辆目标检测

2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) Pub Date : 2021-12-16 DOI: 10.1109/ISRITI54043.2021.9702764

M. Sahal, A. Kurniawan, R. E. A. Kadir

{"title":"Object Detection for Autonomous Vehicle using Single Camera with YOLOv4 and Mapping Algorithm","authors":"M. Sahal, A. Kurniawan, R. E. A. Kadir","doi":"10.1109/ISRITI54043.2021.9702764","DOIUrl":"https://doi.org/10.1109/ISRITI54043.2021.9702764","url":null,"abstract":"In this paper, we propose a new algorithm combined with the existing object recognition algorithm. Multi-object recognition algorithms are now various, with their respective advantages and disadvantages according to their uses. However, these algorithms can only detect and recognize objects without being able to know the location of the object relative to the sensor. The ability to know the location of the object is needed so that the autonomous car can make the right decisions without harming the driver. Since it requires fast and precise object detection and recognition capabilities, the algorithm used in object recognition is YOLOv4 with CSPDarknet-53. And because object recognition uses a neural network, the algorithm in determining the location of the object needs to be made as efficient as possible without affecting the performance of the object recognition algorithm, so that the mapping algorithm is used. The YOLOv4 model used has a precision value of 57.23 percent with a detection capability of 0.03785 seconds without a mapping algorithm, and if it is added with a mapping algorithm, the detection time becomes 0.03792 seconds. Since it has fast detection time, thus it can be applied to a real-time application.","PeriodicalId":156265,"journal":{"name":"2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125198474","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

EEG Data Analytics to Distinguish Happy and Sad Emotions Based on Statistical Features 基于统计特征的脑电数据分析识别快乐和悲伤情绪

2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) Pub Date : 2021-12-16 DOI: 10.1109/ISRITI54043.2021.9702766

Yuri Pamungkas, A. Wibawa, M. Purnomo

{"title":"EEG Data Analytics to Distinguish Happy and Sad Emotions Based on Statistical Features","authors":"Yuri Pamungkas, A. Wibawa, M. Purnomo","doi":"10.1109/ISRITI54043.2021.9702766","DOIUrl":"https://doi.org/10.1109/ISRITI54043.2021.9702766","url":null,"abstract":"Affective computing is part of the important study of Human-Computer Interaction. Currently, EEG-based affective computing (emotion recognition) has become an interesting issue to be studied further. Emotions are not only closely related to aspects of HCI but also affect human health. Meanwhile, EEG is also considered a transparent tool in objectively revealing human emotions because the brain naturally produces EEG signals. This study focuses on comparing and classifying human emotions (happy and sad) based on EEG data. The channels used for recording EEG data are F7, F8, FP1, and FP2. Data preprocessing such as signal filtering, Independent Component Analysis, and Band Decomposition aims to clean the raw signal from artifacts and separate the signals according to specific frequency bands (Alpha, Beta, and Gamma). Then, statistical feature extraction is performed in the time domain to obtain the Mean values, Mean Absolute Value (MAV), and Standard Deviation values for further data analysis. The results show that emotion of happy has a higher feature value compared to emotion of sad. In the classification of happy and sad emotions using several algorithms, Random Forest signifies the highest classification accuracy (88.90%), compared to other algorithms such as SVM (86.70%), K-NN (88.87%), and Naive Bayes (86.63%).","PeriodicalId":156265,"journal":{"name":"2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124338280","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

Spectro-temporal Filtering based on The Beta-divergence for Speech Separation using Nonnegative Matrix Factorization 基于β散度的非负矩阵分解语音分离频谱时间滤波

2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) Pub Date : 2021-12-16 DOI: 10.1109/ISRITI54043.2021.9702880

M. Fakhry

引用次数: 0

Development of a Non-contact Two-Tier Biometric Security System for the DSWD 4Ps using Iris recognition and Speech Recognition 使用虹膜识别和语音识别为社会福利署4Ps开发非接触式双层生物识别保安系统

2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) Pub Date : 2021-12-16 DOI: 10.1109/ISRITI54043.2021.9702822

Alfonso J. Enriquez, Gerard Edilbert H. Tuazon, Ericson D. Dimaunahan, Alehandro H. Ballado

{"title":"Development of a Non-contact Two-Tier Biometric Security System for the DSWD 4Ps using Iris recognition and Speech Recognition","authors":"Alfonso J. Enriquez, Gerard Edilbert H. Tuazon, Ericson D. Dimaunahan, Alehandro H. Ballado","doi":"10.1109/ISRITI54043.2021.9702822","DOIUrl":"https://doi.org/10.1109/ISRITI54043.2021.9702822","url":null,"abstract":"There has been a steep rise of contactless payment during COVID-19. The rapid improvements of miniaturized sensors and biometric recognition systems for face identification, fingerprint, iris, and voice are conducive and fit during this rise of COVID-19. Thus, non-contact interactions are the most effective way to fight against the spread of the virus and any other diseases. One of the most used is iris scanners and speech recognition. The study promotes contactless payments to address the accompanying issues in cash aid distribution particularly in the DSWD 4Ps, where it has a two-tier biometric security system which is iris recognition and speech recognition. This can provide the same type of service and securities as a normal ATM while removing the worry of getting different kinds of viruses and diseases. Testing the iris recognition system, a False acceptance ratio of 13% and 3% of False Rejection rates were achieved. While for the testing of speech recognition (security questions), a False Acceptance Ratio of 0% and False Rejection Ratio of 12.12% were achieved. Lastly, testing of speech recognition (navigation)a False Acceptance Ratio of 0% and False Rejection Ratio of 3.62% were achieved. Giving the system an 84% accuracy for the iris recognition, 87.88% for the security questions, and 96.36% for the navigation.","PeriodicalId":156265,"journal":{"name":"2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131901291","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

Virtual Inertia Enhancement using DC-Link Capacitors in Wind Integrated Power Plants 利用直流电容增强风力综合发电厂的虚拟惯性

2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) Pub Date : 2021-12-16 DOI: 10.1109/ISRITI54043.2021.9702869

Sidratul Montaha Silmee, Md. Sabbir Hosen

引用次数: 0

Filter Selection and Feature Extraction to Distinguish Types of CT Scan Images CT扫描图像类型的滤波器选择与特征提取

2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) Pub Date : 2021-12-16 DOI: 10.1109/ISRITI54043.2021.9702847

O. Nurhayati, B. Surarso

{"title":"Filter Selection and Feature Extraction to Distinguish Types of CT Scan Images","authors":"O. Nurhayati, B. Surarso","doi":"10.1109/ISRITI54043.2021.9702847","DOIUrl":"https://doi.org/10.1109/ISRITI54043.2021.9702847","url":null,"abstract":"The study aims to select the most powerful filtering method as input for feature extraction to distinguish the types of Head CT Scan images. Visually determining the scanned medical image (head CT Scan) has difficulty because it has similar results. So that research is needed that aims to determine the types of digital images scanned by using image processing methods, filtering, and feature extraction. This research used a medical image taken from the head CT-Scan of the patient. To be processed using a computer, the data is scanned to obtain digital image data. Furthermore, various filtering methods were selected, such as median, bandpass filter, XYZ colour transformer filter, enhanced local contrast filter, and histogram equalization. The most significant filtered image results are then segmented with the graph cut segmentation method and extracted using the statistical feature extraction method. The results showed that histogram equalization and enhanced local contrast filter methods were the most significant filtering methods. While the mean and standard deviation are the two most important characteristics that can distinguish the three classes of head CT Scan","PeriodicalId":156265,"journal":{"name":"2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132604689","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

Classification of Stress in Office Work Activities Using Extreme Learning Machine Algorithm and One-way ANOVA F-Test Feature Selection 基于极限学习机算法和单因素方差分析f检验特征选择的办公室工作活动压力分类

2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) Pub Date : 2021-12-16 DOI: 10.1109/ISRITI54043.2021.9702802

Dariswan Janweri Perangin-Angin, F. A. Bachtiar

{"title":"Classification of Stress in Office Work Activities Using Extreme Learning Machine Algorithm and One-way ANOVA F-Test Feature Selection","authors":"Dariswan Janweri Perangin-Angin, F. A. Bachtiar","doi":"10.1109/ISRITI54043.2021.9702802","DOIUrl":"https://doi.org/10.1109/ISRITI54043.2021.9702802","url":null,"abstract":"Stress is a condition when humans feel psychological pressure consciously or unconsciously comes from within themselves and the environment. Based on the survey, more than 50% of workers experienced stress at work. Neglecting stressful conditions repeatedly can worsen the performance and health of workers. Identifying stress condition manually is not effective and may take some time. There is a need to build a system to diagnose stress quickly and accurately. Machine learning classification is one of the solutions to problems that are applied to intelligent systems. One of the machine learning methods that can be used is the Extreme Learning Machine (ELM) algorithm. One-way ANOVA F-test is used as a method of feature selection to require a quick decision so the reduction in features is expected to accelerate the results of the classification. The dataset used is the Heart Rate Variability totaling 5000 samples with 35 features and 3 classes. Based on experiments, the conventional ELM algorithm produces an accuracy of 0.878 while the combination of the ELM algorithm and the One-way ANOVA F-test produces an accuracy of 0.91 with 33 selected features. Thus, the effect of using the feature selection method can increase accuracy and reduce computational time, and the addition of hidden neurons results in a significant increase in accuracy and computational time.","PeriodicalId":156265,"journal":{"name":"2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132542248","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

Retinal Disease for Clasification Multilabel with Applying Convolutional Neural Networks Based Support Vector Machine and DenseNet 基于卷积神经网络支持向量机和DenseNet的视网膜疾病多标签分类

2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) Pub Date : 2021-12-16 DOI: 10.1109/ISRITI54043.2021.9702861

Alicia Anggelia Lumbantoruan, A. Bustamam, P. Anki

{"title":"Retinal Disease for Clasification Multilabel with Applying Convolutional Neural Networks Based Support Vector Machine and DenseNet","authors":"Alicia Anggelia Lumbantoruan, A. Bustamam, P. Anki","doi":"10.1109/ISRITI54043.2021.9702861","DOIUrl":"https://doi.org/10.1109/ISRITI54043.2021.9702861","url":null,"abstract":"The retina is an essential part of the eye and works to transmit visual information to the brain. In maintaining the eye, an ophthalmologist needs regular examinations, but the price is expensive and takes time. Therefore, technological developments are expected to help the medical world to detect diseases. The technology is image processing. Convolutional Neural Network (CNN) is the most popular neural network model to handle image analysis and can recognize patterns from an image accurately. This study detected Drusens, Optic Disc Cupping, and Tessellation diseases using 534 fundus images. The architecture used Convolutional Neural Network-based Support Vector Machine (CNN based SVM) and DenseNet, which is a Convolutional Neural Network architecture development. In obtaining the best results, in this study, we use several variations of the optimizer, namely adam, nadam, and RMSprop, and the best results from this study can be seen from the accuracy value of 93,21% using the DenseNet architecture.","PeriodicalId":156265,"journal":{"name":"2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"136 9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131347364","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

Improved HEVC Video Encoding Quality With Multi Scalability Techniques 改进HEVC视频编码质量与多可扩展性技术

2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) Pub Date : 2021-12-16 DOI: 10.1109/ISRITI54043.2021.9702868

A. Purwadi, Wirawan, Suwadi

引用次数: 0