2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom)最新文献_第2页

EEMD-CNN based Method for Compound Fault Diagnosis of Bearing 基于EEMD-CNN的轴承复合故障诊断方法

2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom) Pub Date : 2022-06-16 DOI: 10.1109/CyberneticsCom55287.2022.9865477

Anubhuti Singh, Arun C. S. Kumar

引用次数: 0

User Experience Analysis on e-Wallet Using a Combination of Heuristic Evaluation and UMUX 基于启发式评价和UMUX的电子钱包用户体验分析

2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom) Pub Date : 2022-06-16 DOI: 10.1109/CyberneticsCom55287.2022.9865427

Havinda Rosita Faradina, Tenia Wahyuningrum, Novian Adi Prasetyo, Iqsyahiro Kresna A

{"title":"User Experience Analysis on e-Wallet Using a Combination of Heuristic Evaluation and UMUX","authors":"Havinda Rosita Faradina, Tenia Wahyuningrum, Novian Adi Prasetyo, Iqsyahiro Kresna A","doi":"10.1109/CyberneticsCom55287.2022.9865427","DOIUrl":"https://doi.org/10.1109/CyberneticsCom55287.2022.9865427","url":null,"abstract":"Usability is the level of ease of users in using the interface on a system. Usability can be measured using expert judgment or user testing. One of the techniques in usability measurement that can be is Heuristic Evaluation (measured by the expert) and Usability Metric for User Experience or as known as UMUX (measured by user). Heuristic Evaluation is an interface evaluation process that aims to measure an interface's usability, efficiency, and effectiveness based on ten heuristic rules. Meanwhile, UMUX is a short-level instrument method or rating level used to collect quantitative user data about the usability of an application. Therefore, the combination of expert judgment and user assessment will provide rich and complementary findings. In this study, we used “CARDS” as the research object. “CARDS” is a digital card application or e-wallet used to pay bills, top-up card balances, online stores, and Payment Point Online Banks. This study aims to improve the quality of service to users of the “CARDS” application by testing the user experience. The result shows that the UMUX score is not equal to 74, so it is necessary to make improvements, with recommendations from experts by the Heuristic Evaluation method based on the lowest assessment score, namely the Consistency and Standards category.","PeriodicalId":178279,"journal":{"name":"2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom)","volume":"11 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120849544","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

Forecasting the Automobile and Parts Product Export Values using Time Series Analysis 用时间序列分析预测汽车及零部件产品出口价值

2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom) Pub Date : 2022-06-16 DOI: 10.1109/CyberneticsCom55287.2022.9865604

Jakkaphan Whasphuttisit, Watchareewan Jitsakul

{"title":"Forecasting the Automobile and Parts Product Export Values using Time Series Analysis","authors":"Jakkaphan Whasphuttisit, Watchareewan Jitsakul","doi":"10.1109/CyberneticsCom55287.2022.9865604","DOIUrl":"https://doi.org/10.1109/CyberneticsCom55287.2022.9865604","url":null,"abstract":"This research aims to study the suitable time series analysis to forecast the automobile and parts product export values over the next 12 months. The time series data source gathers from the Government Open Data of Thailand official website during January 2013 to December 2021, 108 months in total. The experiment starts with creation, comparison, selection, verification, and forecasting. Time series analysis has considered five methods: Trend Analysis, Moving Average, Decomposition, Single Exponential Smoothing, and Double Exponential Smoothing. We use mean absolute present error (MAPE), mean absolute deviation (MAD), and mean squared deviation (MSD) to compare and select the least value. The result showed that Moving Average had the best performance. Then we used the Moving Average to verify and forecast over the next 12 months. However, it was found that the forecast values obtained were constant for the entire 12 months, so the moving average is unused for forecasting. The Moving Average has the least mean absolute present error (MAPE) at 0.2420. Therefore, we have used Decomposition which is a suitable performance in the second order of forecasting. It is forecast and has a trend value. Moreover, the Decomposition method has the least mean absolute present error (MAPE) at 0.1832.","PeriodicalId":178279,"journal":{"name":"2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124431942","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

Betta Fish Image Classification Using Artificial Neural Networks with Gabor Extraction Features 基于Gabor提取特征的人工神经网络对斗鱼图像进行分类

2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom) Pub Date : 2022-06-16 DOI: 10.1109/CyberneticsCom55287.2022.9865509

Satria Hidayat, Aviv Yuniar Rahman, Istiadi

{"title":"Betta Fish Image Classification Using Artificial Neural Networks with Gabor Extraction Features","authors":"Satria Hidayat, Aviv Yuniar Rahman, Istiadi","doi":"10.1109/CyberneticsCom55287.2022.9865509","DOIUrl":"https://doi.org/10.1109/CyberneticsCom55287.2022.9865509","url":null,"abstract":"Betta fish also known as battling fish, is a type of freshwater fish that is well known among ornamental fish lovers. For this reason, the analyst proposes Betta Fish Picture Grouping Utilizing Artificial Neural Networks with the Color Gabor feature. The test results have 3 parameters, namely precision, recall, and accuracy. The level in comparison using a comparator between 50:50. The results obtained starting from the Gabor feature with CMYK precision color have test results reaching 37.94%. Then the recall has a value of 30.40% and accuracy in the existing accuracy reaches 56.71%. From the results of testing the Gabor feature with HSV precision color, reached 38.69%. Then the recall has value of 34.92% and accuracy in the existing accuracy reaches 54.69%. The Gabor feature with RGB precision reaching 39.40% at a 50:50. Then the recall has a value of 32.28% at a 50:50. The level of accuracy in the existing accuracy reaches 58.85% with a ratio of 50:50. From this it can be concluded that the Gabor feature with GRB color has the best accuracy value at a ratio of 50:50. The Gabor feature with RGB color is the best result in betta fish classification using Artificial Neural Networks.","PeriodicalId":178279,"journal":{"name":"2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130593454","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

Algorithm of Caries Level Image Classification Using Multilayer Perceptron Based Texture Features 基于纹理特征的多层感知机龋级图像分类算法

2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom) Pub Date : 2022-06-16 DOI: 10.1109/CyberneticsCom55287.2022.9865543

Y. Jusman, Anna Widyaningrum, Sartika Puspita

{"title":"Algorithm of Caries Level Image Classification Using Multilayer Perceptron Based Texture Features","authors":"Y. Jusman, Anna Widyaningrum, Sartika Puspita","doi":"10.1109/CyberneticsCom55287.2022.9865543","DOIUrl":"https://doi.org/10.1109/CyberneticsCom55287.2022.9865543","url":null,"abstract":"A number of patients with untreated caries only seek treatment at late stages when serious complications might have already developed and can lead to significant acute and chronic conditions with high cost of treatment. The purpose of this research is to be able to find out the level of caries based on X ray images by using image processing and machine learning methods. The image processing algorithm namely Gray Level Co-occurrence Matrix (GLCM) has been used to extract texture features and Multilayer Perceptron (MLP) methods to classify the X ray caries images. Lavenberg Marquard and Backpropagation Bayesian Regularization are used in this study. The conclusion obtained in this study is that the algorithm of classification using Multilayer Perceptron (MLP) based texture features can classify dental caries images in four classes. The best performance result is achieved the training accuracy of 99.20% and the testing accuracy of 98.30% by using Lavenberg Marquardt (LM) model with hidden layer 10. In Backpropagation Bayesian Regularization (BR), the best results are found in hidden layer 10 as well (Training: 100%, Testing: 100%).","PeriodicalId":178279,"journal":{"name":"2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121622510","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

Classifying the Swallow Nest Quality Using Support Vector Machine Based on Computer Vision 基于计算机视觉的支持向量机燕窝质量分类

2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom) Pub Date : 2022-06-16 DOI: 10.1109/CyberneticsCom55287.2022.9865498

Anindita Septiarini, Ferda Maulana, H. Hamdani, Rizqi Saputra, Tenia Wahyuningrum, Indra

{"title":"Classifying the Swallow Nest Quality Using Support Vector Machine Based on Computer Vision","authors":"Anindita Septiarini, Ferda Maulana, H. Hamdani, Rizqi Saputra, Tenia Wahyuningrum, Indra","doi":"10.1109/CyberneticsCom55287.2022.9865498","DOIUrl":"https://doi.org/10.1109/CyberneticsCom55287.2022.9865498","url":null,"abstract":"Swallow Nest is a valuable export commodity, particularly in Indonesia. It is produced when a swallow's saliva hardens and is frequently encountered in high-rise buildings. Swallow nests can be utilized to treat various ailments in the medical sector. The price of a swallow nest varies according to its quality, which is commonly classified into three grades: quality 1 (Q1), quality 2 (Q2), and quality 3 (Q3). Q1 is of the highest quality, while Q3 is of the lowest. Each grade has a different physical appearance. Currently, many people lack knowledge regarding the grade of a swallow nest. Therefore, a method is needed to automatically classify the quality of swallow nests based on computer vision. The proposed method consists of several main processes, including image acquisition, ROI detection, pre-processing, segmentation, feature extraction, and classification. The feature extraction was applied based on shapes, followed by the Support Vector Machine (SVM) implementation in the classification process. This process was performed with cross-validation using the k-fold values of 5. The performance evaluation was done using three parameters: precision, recall, and accuracy, by achieving the value of 90.6%, 89.3%, and 89.3%, respectively.","PeriodicalId":178279,"journal":{"name":"2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131413592","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

IoT and AI-enabled Physical Distance Monitoring Application to Prevent COVID19 Transmission 支持物联网和人工智能的物理距离监控应用，防止covid - 19传播

2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom) Pub Date : 2022-06-16 DOI: 10.1109/CyberneticsCom55287.2022.9865290

Mohammad Dwipa Furqan, A. Achmad, Wardi, M. Niswar

引用次数: 1

User Experience Evaluation Using Integration of Remote Usability Testing and Usability Evaluation Questionnaire Method 基于远程可用性测试和可用性评估问卷法的用户体验评估

2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom) Pub Date : 2022-06-16 DOI: 10.1109/CyberneticsCom55287.2022.9865664

Ajeng Fitria Rahmawati, Tenia Wahyuningrum, Ariq Cahya Wardhana, Anindita Septiari, Lasmedi Afuan

{"title":"User Experience Evaluation Using Integration of Remote Usability Testing and Usability Evaluation Questionnaire Method","authors":"Ajeng Fitria Rahmawati, Tenia Wahyuningrum, Ariq Cahya Wardhana, Anindita Septiari, Lasmedi Afuan","doi":"10.1109/CyberneticsCom55287.2022.9865664","DOIUrl":"https://doi.org/10.1109/CyberneticsCom55287.2022.9865664","url":null,"abstract":"Sinovi (Innovation Center Innovation System) is a website developed to manage the collection of innovation and HAKI owned by the ITTP academic community. Based on the results of initial observations made by interviewing the Center for Innovation and HAKI, although it has been done twice socialization of website use, there are still complaints and obstacles experienced by users. Based on these problems, the researchers conducted research on evaluating the user experience (UX) of the Sinovi website. This study aims to determine the system's performance based on user experience. The UX evaluation process uses methods moderated remote usability testing and user experience questionnaire (UEQ). The study's results using moderated usability testing showed a significant difference in completion rate between the two groups of users, with each group having values of 0.9560 and 0.8235. While the results of time-based efficiency tests showed that the average time-based efficiency between group A and group B has similarities with the values obtained, respectively are 0.1652 and 0.1259. The test results using UEQ show that the Sinovi website has managed to get a positive evaluation. Several categories were successfully obtained, including the “Attractiveness” category with a score of 1.967, the “Perspicuity” category with a score of 1.850, the “Efficiency” category with a score of 2.042, the “Dependability” category with a score of 1.825, and the “Stimulation” category with a score of 1,742. The overall user experience evaluation results show that Sinovi's website is already at a good user experience level but needs to improve to reduce the number of problems.","PeriodicalId":178279,"journal":{"name":"2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133179864","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: 4

E-government Public Complaints Text Classification Using Particle Swarm Optimization in Naive Bayes Algorithm 基于朴素贝叶斯算法粒子群优化的电子政务投诉文本分类

2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom) Pub Date : 2022-06-16 DOI: 10.1109/CyberneticsCom55287.2022.9865585

T. Hariguna, Sarmini, A. Hananto

引用次数: 0

Estimating Rice Production using Machine Learning Models on Multitemporal Landsat-8 Satellite Images (Case Study: Ngawi Regency, East Java, Indonesia) 基于Landsat-8多时相卫星图像的机器学习模型估算水稻产量(以印度尼西亚东爪哇Ngawi摄政为例)

2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom) Pub Date : 2022-06-16 DOI: 10.1109/CyberneticsCom55287.2022.9865364

A. Wijayanto, Salwa Rizqina Putri

{"title":"Estimating Rice Production using Machine Learning Models on Multitemporal Landsat-8 Satellite Images (Case Study: Ngawi Regency, East Java, Indonesia)","authors":"A. Wijayanto, Salwa Rizqina Putri","doi":"10.1109/CyberneticsCom55287.2022.9865364","DOIUrl":"https://doi.org/10.1109/CyberneticsCom55287.2022.9865364","url":null,"abstract":"To enhance sustainable food security, the cost-efficient data collection technology for estimating rice production in a major agriculture nation such as Indonesia is undoubtedly vital to support the existing official data collection. The current official data collection is still facing great challenges in terms of its high cost and laborious nature. This study aims to build machine learning-based models for rice production estimation by utilizing multitemporal Normalized Difference Vegetation Index (NDVI) data obtained from Landsat-8 remote sensing satellite imagery focusing on Ngawi Regency, East Java, Indonesia as a case study area. Our investigation reveals the quarterly changes in vegetation conditions of the rice fields can be captured through the NDVI value. Four different machine learning models are constructed and evaluated to process the satellite data. Support vector regression (SVR) was shown to obtain the best performance from 10-folds cross-validation with the average root mean square error (RMSE) of 6952.89 tons and has a quite high coefficient of determination (R2) score which is up to 0.9. The current estimation results provide an incentive to use satellite imagery data and machine learning models to support agricultural monitoring and decision-making.","PeriodicalId":178279,"journal":{"name":"2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom)","volume":"139 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133903309","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