2021 International Seminar on Intelligent Technology and Its Applications (ISITIA)最新文献_第7页

Eyeball Movement Detection Using Sector Division Approach and Extreme Learning Machine 基于扇形分割和极限学习机的眼球运动检测

2021 International Seminar on Intelligent Technology and Its Applications (ISITIA) Pub Date : 2021-07-21 DOI: 10.1109/ISITIA52817.2021.9502211

F. A. Bachtiar, Gusti Pangestu, F. Pradana, Issa Arwani, Dahnial Syauqy

{"title":"Eyeball Movement Detection Using Sector Division Approach and Extreme Learning Machine","authors":"F. A. Bachtiar, Gusti Pangestu, F. Pradana, Issa Arwani, Dahnial Syauqy","doi":"10.1109/ISITIA52817.2021.9502211","DOIUrl":"https://doi.org/10.1109/ISITIA52817.2021.9502211","url":null,"abstract":"Eyeball movement is being widely used for many purposes. A lot of research is trying to find the best approaches and methods to detect, track and recognize the movements. In this research, we propose an approach to detect the direction of eyeball movements using Sector Division Approach and Extreme Learning Machine (ELM). The extraction process of Sector Division is detecting facial image, detecting the eye location using subset points in the Facial Landmark. Selected eye location is segmented and through several processes such as image cropping, conversion into grayscale image, blurring process, and finally binary process. The final image in the binary process is divided into 9 (nine) sectors and extracted resulting in 9 feature vectors. ELM is used to classify the eyeball movement. The optimal number of hidden neurons identified first before the model is used in the testing step. A total of 50 data is used to train the ELM to classify the eyeball movement. The ELM model is executed 5 (five) times to reduce the variability of the random weight in the ELM model. Testing is done by evaluating each eyeball movement using 12 still images in each direction. Based on the experiment, a number of 20 hidden neurons results in the highest predictive accuracy and is used in the testing step. The result shows that the proposed model is able to achieve a satisfactory result by showing an accuracy of 81.67%. The result of this study could be beneficial to be used in similar studies as using a small number of training data, basic feature extraction, and a small number of feature vectors could achieve satisfactory accuracy.","PeriodicalId":161240,"journal":{"name":"2021 International Seminar on Intelligent Technology and Its Applications (ISITIA)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125225394","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

Optimization Learning Approaches in Predicting Facebook Metrics from User Posts Behavior 从用户帖子行为预测Facebook指标的优化学习方法

2021 International Seminar on Intelligent Technology and Its Applications (ISITIA) Pub Date : 2021-07-21 DOI: 10.1109/ISITIA52817.2021.9502245

Yuliazmi, D. Purwitasari, S. Sumpeno, M. Purnomo

{"title":"Optimization Learning Approaches in Predicting Facebook Metrics from User Posts Behavior","authors":"Yuliazmi, D. Purwitasari, S. Sumpeno, M. Purnomo","doi":"10.1109/ISITIA52817.2021.9502245","DOIUrl":"https://doi.org/10.1109/ISITIA52817.2021.9502245","url":null,"abstract":"The current situation of the Covid-19 pandemic has an impact on increasing the use of social media. In various aspects, social media has a role in human activities, especially in working-age groups. Breaking the stigma that social media interferes with someones’ performance, we argue that using social media actually supports someones’ work activities. In this preliminary study, we explore post behavior on Facebook social media networks for understanding user productivity. The dataset used in this study is gained from an online survey with the respondent of social media users over age 15 years old. Later on, based on surveys’ responses, web scraping of Facebook post were set to complete the data needed. From the dataset, demographic features, metadata-based features, and behavior-based features are examined with some regression algorithms such Support Vector Regression (SVR) and Particle Swarm Optimization Extreme Learning Machine (PSO-ELM). The result from this study is only one feature that positively correlated to almost all other features during the pandemic.","PeriodicalId":161240,"journal":{"name":"2021 International Seminar on Intelligent Technology and Its Applications (ISITIA)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129602837","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

Online OPF Using Combined MOGA-ETS to Minimize Losses and Extend Battery Lifetime in Micro-Grid 结合MOGA-ETS的微电网在线OPF最小化损耗和延长电池寿命

2021 International Seminar on Intelligent Technology and Its Applications (ISITIA) Pub Date : 2021-07-21 DOI: 10.1109/ISITIA52817.2021.9502201

Primaditya Sulistijono, A. Soeprijanto, D. Riawan

引用次数: 0

Development of Obstacle Detection Based on Region Convolutional Neural Network for Autonomous Car 基于区域卷积神经网络的自动驾驶汽车障碍物检测研究

2021 International Seminar on Intelligent Technology and Its Applications (ISITIA) Pub Date : 2021-07-21 DOI: 10.1109/ISITIA52817.2021.9502196

Khairunnsa Nurhandayani, D. Purwanto, R. Mardiyanto

{"title":"Development of Obstacle Detection Based on Region Convolutional Neural Network for Autonomous Car","authors":"Khairunnsa Nurhandayani, D. Purwanto, R. Mardiyanto","doi":"10.1109/ISITIA52817.2021.9502196","DOIUrl":"https://doi.org/10.1109/ISITIA52817.2021.9502196","url":null,"abstract":"Autonomous car is a transportation technology that has been developed. Its potencies can be run without human operators that decrease the road accident rate. The obstacle detection system becomes one of the significant systems for autonomous cars because it uses for sensing close obstacles. Indonesian people still rarely use the autonomous car because some objects can not be acknowledged by communal autonomous cars like becak. In this research, this obstacle detection system uses a dataset developed for an autonomous car in Indonesia. Faster Region Convolutional Neural Network (F-RCNN) with Residual Network-50 (ResNet-50) and Feature Pyramid Network (FPN) as the backbone system is applied. For training and validation, the self-made dataset comprises 1,451 annotations for the training process and 502 for validating process. The result is good enough which its Average Precision (AP) is 45.67% for 10,000 iterations, 43.07% for 40,000 iterations, and 43.26% for 55,000 iterations. The outputs from the obstacle detection system are an image for visualizing image resulted, the coordinates of objects detected, and their classes for the autonomous car input variables. The result for processing video also shows this system can process the image within $sim 5$ frames per second (fps) with the help of Tesla T4 15,109 MB Graphic Processing Unit and Intel ® Xeon Central Processing Unit@2.30 GHz.","PeriodicalId":161240,"journal":{"name":"2021 International Seminar on Intelligent Technology and Its Applications (ISITIA)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128668862","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

Traffic Lights and Traffic Signs Detection System Using Modified You Only Look Once 交通灯和交通标志检测系统使用改进的你只看一次

2021 International Seminar on Intelligent Technology and Its Applications (ISITIA) Pub Date : 2021-07-21 DOI: 10.1109/ISITIA52817.2021.9502268

Alvin Abraham, D. Purwanto, Hendra Kusuma

引用次数: 3

Driver for LED Lamp with Buck Converter Controlled by PID PID控制降压变换器的LED灯驱动器

2021 International Seminar on Intelligent Technology and Its Applications (ISITIA) Pub Date : 2021-07-21 DOI: 10.1109/ISITIA52817.2021.9502199

Widjonarko, Gamma Aditya Rahardi, Cries Avian, Widyono Hadi, Dedy Wahyu Herdiyanto, Panji Langgeng Satrio

引用次数: 4

Robustness of Convolutional Neural Network in Classifying Apple Images 卷积神经网络在苹果图像分类中的鲁棒性

2021 International Seminar on Intelligent Technology and Its Applications (ISITIA) Pub Date : 2021-07-21 DOI: 10.1109/ISITIA52817.2021.9502258

Dzalfa Tsalsabila Rhamadiyanti, S. Suyanto

{"title":"Robustness of Convolutional Neural Network in Classifying Apple Images","authors":"Dzalfa Tsalsabila Rhamadiyanti, S. Suyanto","doi":"10.1109/ISITIA52817.2021.9502258","DOIUrl":"https://doi.org/10.1109/ISITIA52817.2021.9502258","url":null,"abstract":"Apple is one of the popular fruits for public consumption. People can distinguish many apples based on their colors and shapes, such as the Braeburn Apple with skin color varies from orange to red, the Pink Lady Apple that is red with pseudo pink, the Crismon Snow Apple that has dark red skin. Recently, computers can automatically recognize them using digital image processing techniques such as Convolutional Neural Networks (CNN). In this paper, a CNN-based classification model of apple types is developed using 1856 apple images from three classes derived from the fruit-360 dataset on the Kaggle website, and its robustness is then examined. Two types of testing have been carried out in this study: testing five scenarios for sharing training data and testing five scenarios for robustness to noise. An examination based on 5-fold cross-validation shows that CNN is robust to decreasing the portion of training set size up to 50% to get high accuracy of 99.97% in classifying 50% testing set, which is better than previous models that use VGG16, faster R-CNN, and Tanh. Decreasing the portion training set to 40% and 30% reduces the accuracy to 95.97% and 95.29%, respectively. Adding low-level noises of 10% into the testing images decreases the accuracy slightly to 99.17%. However, high-level noises of 50% drastically make the accuracy drastically drops to 63.93%.","PeriodicalId":161240,"journal":{"name":"2021 International Seminar on Intelligent Technology and Its Applications (ISITIA)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126470398","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

Implementation of Light Detection and Ranging in Vehicle Braking System 车辆制动系统中光检测与测距的实现

2021 International Seminar on Intelligent Technology and Its Applications (ISITIA) Pub Date : 2021-07-21 DOI: 10.1109/ISITIA52817.2021.9502244

M. Rivai, D. Purwanto, A. Razak, Dony Hutabarat, Dava Aulia

引用次数: 2

Early Warning Pedestrian Crossing Intention From Its Head Gesture using Head Pose Estimation 基于头部姿态估计的行人过马路意向预警

2021 International Seminar on Intelligent Technology and Its Applications (ISITIA) Pub Date : 2021-07-21 DOI: 10.1109/ISITIA52817.2021.9502231

Muhammad Ilham Perdana, Wiwik Anggraeni, H. A. Sidharta, E. M. Yuniarno, M. Purnomo

{"title":"Early Warning Pedestrian Crossing Intention From Its Head Gesture using Head Pose Estimation","authors":"Muhammad Ilham Perdana, Wiwik Anggraeni, H. A. Sidharta, E. M. Yuniarno, M. Purnomo","doi":"10.1109/ISITIA52817.2021.9502231","DOIUrl":"https://doi.org/10.1109/ISITIA52817.2021.9502231","url":null,"abstract":"The development of the autonomous driving system is still a hot topic. Especially in the area of the interaction between the autonomous driving system and road crossing pedestrians. Recognizing the pedestrian crossing intention is one of the crucial topics for the smart autonomous driving system. The autonomous driving system must be safe enough for both its user and pedestrian. Many research, approach, and method has been developed to detect or predict pedestrian crossing intention. But unfortunately, many previous works predict the pedestrian crossing intention that already does cross the road. There is still few research about how to predict early pedestrian crossing intention. Usually when they want to cross the road or before they do cross the road, the pedestrian gives unique gestures like looking at the incoming vehicle. Thus, an early warning pedestrian crossing intention system has been developed using a different approach, by its head gesture. The pedestrian crossing intention could be predicted by classifying its head pose angle. Experiment show very well that the proposed method able to predict early pedestrian crossing intention by its head gesture and give an early warning sign to the autonomous driving system with the accuracy of the classification 97.2%. We believe our work could bring benefits to the autonomous driving system to increase the safeties of its system.","PeriodicalId":161240,"journal":{"name":"2021 International Seminar on Intelligent Technology and Its Applications (ISITIA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133528668","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: 3

Network Traffic Prediction Of Mobile Backhaul Capacity Using Time Series Forecasting 基于时间序列预测的移动回程容量网络流量预测

2021 International Seminar on Intelligent Technology and Its Applications (ISITIA) Pub Date : 2021-07-21 DOI: 10.1109/ISITIA52817.2021.9502256

Giovanni Abel Christian, Ihsan Pandu Wijaya, R. F. Sari

{"title":"Network Traffic Prediction Of Mobile Backhaul Capacity Using Time Series Forecasting","authors":"Giovanni Abel Christian, Ihsan Pandu Wijaya, R. F. Sari","doi":"10.1109/ISITIA52817.2021.9502256","DOIUrl":"https://doi.org/10.1109/ISITIA52817.2021.9502256","url":null,"abstract":"Telecommunication tower company provides Mobile Backhaul service to provide end to end solution from base station to customer’s core network. This case study is conducted in one of the telecommunication tower company and mobile backhaul services provider that provides fiber optic connections as physical interfaces and ethernet transport equipment to serve the customer. Customer use leased line capacity mechanism to provide their requirement on mobile backhaul connectivity. The bandwidth capacity may encounter an increase in daily or monthly usage, which requires the customer to upgrade their maximum capacity. As a service provider, PT Tower Bersama wish to predict the customer bandwidth utilization to discern when the customer needs to upgrade their mobile backhaul leased line capacity. The network traffic is modeled as a time series data. Fractionally Auto Regressive Integrated Moving Average (FARIMA) model and Artificial Neural Network (ANN) model are used to forecast the future network traffic. In terms of error FARIMA (4,0.2,1) model shows the least error with RMSE, MAE and MAPE are 11.762, 9.329 and 11.950 respectively. However, ANN-MLP model prediction result shows more similar pattern with the existing traffic with a slight difference in error with FARIMA model. The prediction model then applied to the interactive dashboard to determine client’s upgrade based on the forecasted traffic data.","PeriodicalId":161240,"journal":{"name":"2021 International Seminar on Intelligent Technology and Its Applications (ISITIA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130026171","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