2021 2nd International Conference on Innovative and Creative Information Technology (ICITech)最新文献

Analyzing Public Opinion Based on Emotion Labeling Using Transformers 基于情感标签的舆情分析

2021 2nd International Conference on Innovative and Creative Information Technology (ICITech) Pub Date : 2021-09-23 DOI: 10.1109/ICITech50181.2021.9590110

M. T. Anwar, Al Kautsar Permana, Laksmi Ambarwati, Desy Agustin

{"title":"Analyzing Public Opinion Based on Emotion Labeling Using Transformers","authors":"M. T. Anwar, Al Kautsar Permana, Laksmi Ambarwati, Desy Agustin","doi":"10.1109/ICITech50181.2021.9590110","DOIUrl":"https://doi.org/10.1109/ICITech50181.2021.9590110","url":null,"abstract":"This research aimed to do sentiment analysis by conducting text classification targeting six basic human emotions (fear, anger, joy, sadness, disgust, and surprise) using state-of-the-art Natural Language Processing (NLP) technique called ‘Transformers'. More than 1000 tweet data are obtained from Twitter on the issue of the mudik prohibition policy issued by the government of Indonesia in May 2021. The result showed that most people are feeling sad (47%) and surprised (24%) about the mudik prohibition policy. The sad feeling is related to the publics' inability to come back to their hometown and missing their families there. Whereas the ‘surprised’ feelings are due to the contradiction of the mudik prohibition policy with other policies such as the opening of tourist attractions and malls. Our result also showed that the model can accurately predict and have high confidence in predicting the emotions even when the texts do not contain obvious words that are strongly associated with certain emotions. The average confidence score on the prediction is pretty high at 0.82 with most of the predictions having a confidence score higher than 0.95.","PeriodicalId":429669,"journal":{"name":"2021 2nd International Conference on Innovative and Creative Information Technology (ICITech)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127667062","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

Monocular Vision Obstacle Avoidance UAV: A Deep Reinforcement Learning Method 单目视觉避障无人机:一种深度强化学习方法

2021 2nd International Conference on Innovative and Creative Information Technology (ICITech) Pub Date : 2021-09-23 DOI: 10.1109/ICITech50181.2021.9590178

Zhihan Xue, T. Gonsalves

引用次数: 1

Machine Learning Approach to Find Students' Best Place to Study: Home vs Hostel 寻找学生最佳学习地点的机器学习方法:家庭与旅馆

2021 2nd International Conference on Innovative and Creative Information Technology (ICITech) Pub Date : 2021-09-23 DOI: 10.1109/ICITech50181.2021.9590130

Jarin Nooder, Ashrarfi Mahbuba, Shayla Sharmin, Nazmun Nessa Moon, Lamisha Haque Poushy, Salauddin Ahmed Bhuiyan, Samia Nawshin

引用次数: 0

Movie Recommendation System using Weighted Average Approach 基于加权平均方法的电影推荐系统

2021 2nd International Conference on Innovative and Creative Information Technology (ICITech) Pub Date : 2021-09-23 DOI: 10.1109/ICITech50181.2021.9590147

Christ Zefanya Omega, Hendry

引用次数: 0

Detection of Stop Line Violations Using the Hough Transform 基于Hough变换的停车线违规检测

2021 2nd International Conference on Innovative and Creative Information Technology (ICITech) Pub Date : 2021-09-23 DOI: 10.1109/ICITech50181.2021.9590189

D. K. Larasati, Iwan Setvawan

引用次数: 1

A Comprehensive Analysis of Machine Learning Methods for Air Pollution Forecasting 空气污染预测的机器学习方法综合分析

2021 2nd International Conference on Innovative and Creative Information Technology (ICITech) Pub Date : 2021-09-23 DOI: 10.1109/ICITech50181.2021.9590113

J. Mohith, Divij Kulshrestha, K. Jothi

引用次数: 2

Identification of Rice Leaf Disease Using Convolutional Neural Network Based on Android Mobile Platform 基于Android移动平台的卷积神经网络水稻叶病识别

2021 2nd International Conference on Innovative and Creative Information Technology (ICITech) Pub Date : 2021-09-23 DOI: 10.1109/ICITech50181.2021.9590188

M. F. X. Cham, Radius Tanone, Hendra Alexander T Riadi

{"title":"Identification of Rice Leaf Disease Using Convolutional Neural Network Based on Android Mobile Platform","authors":"M. F. X. Cham, Radius Tanone, Hendra Alexander T Riadi","doi":"10.1109/ICITech50181.2021.9590188","DOIUrl":"https://doi.org/10.1109/ICITech50181.2021.9590188","url":null,"abstract":"Rice is a rice-producing plant that is susceptible to disease so it can make it difficult for farmers to identify the types of diseases that exist in rice leaves. On the other hand, farmers need convenience in identifying diseases that exist in rice leaves more effectively and efficiently. Seeing the development trend of deep learning and mobile android, we need an application that can help farmers to analyze diseases in leaves effectively and efficiently. This research was conducted in several stages including literature study, application design and manufacture, application testing and analysis as well as conclusion drawing and report writing. With deep learning technology, a Convolutional Neural Network (CNN) model was developed on Tensorflow lite and stored in the ML Kit service. Furthermore, the model can be embedded in a detection application built on the android mobile platform. This is to assist farmers in identifying healthy and unhealthy rice leaves. The results of the development of the algorithm and its application to an Android-based mobile application can run well where the level of accuracy generated from the model formed in classifying disease images on rice leaves in this study is 80%.","PeriodicalId":429669,"journal":{"name":"2021 2nd International Conference on Innovative and Creative Information Technology (ICITech)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114791806","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

A Novel Approach to Collect and Analyze Market Customer Behavior Data on Online Shop 一种收集和分析网上商店市场顾客行为数据的新方法

2021 2nd International Conference on Innovative and Creative Information Technology (ICITech) Pub Date : 2021-09-23 DOI: 10.1109/ICITech50181.2021.9590161

Ramos Somya, E. Winarko, Sigit Privanta

{"title":"A Novel Approach to Collect and Analyze Market Customer Behavior Data on Online Shop","authors":"Ramos Somya, E. Winarko, Sigit Privanta","doi":"10.1109/ICITech50181.2021.9590161","DOIUrl":"https://doi.org/10.1109/ICITech50181.2021.9590161","url":null,"abstract":"Online shopping activities are currently experiencing a significant increase due to the development of the reach of the internet services and the changed activities from offline to online. The data analysis generated from online shopping activities is necessary to determine the right sales strategy. One of the types of data that needs to be analyzed is market consumer behavior data in online shops, generated in the form of clickstream data. Currently, there was not any research that examines the determination of clickstream data components, clickstream data recording mechanism, and how to analyze clickstream data components in online shops properly. This paper describes the architecture of the proposed online shop application, the module to record the clickstream data, and the method to analyze the clickstream data. Based on our evaluation, eight clickstream data components were successfully recorded in the database using Asynchronous JavaScript and XML (AJAX) technology. The clickstream data component that contains market customer behavior data is Multi-Criteria Decision Making (MCDM) data and has been analyzed using the Simple Additive Weighting (SAW) ranking method.","PeriodicalId":429669,"journal":{"name":"2021 2nd International Conference on Innovative and Creative Information Technology (ICITech)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116971086","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

Edge Detection and Grey Level Co-Occurrence Matrix (GLCM) Algorithms for Fingerprint Identification 指纹识别的边缘检测和灰度共生矩阵算法

2021 2nd International Conference on Innovative and Creative Information Technology (ICITech) Pub Date : 2021-09-23 DOI: 10.1109/ICITech50181.2021.9590134

Edy Winarno, W. Hadikurniawati, Setyawan Wibisono, Anindita Septiarini

引用次数: 2

Detection of HOG Features on Tuberculosis X-Ray Results Using SVM and KNN 基于SVM和KNN的肺结核x射线HOG特征检测

2021 2nd International Conference on Innovative and Creative Information Technology (ICITech) Pub Date : 2021-09-23 DOI: 10.1109/ICITech50181.2021.9590186

Arif Ridho Lubis, S. Prayudani, Y. Fatmi, Al-Khowarizmi, Julham, Y. Y. Lase

{"title":"Detection of HOG Features on Tuberculosis X-Ray Results Using SVM and KNN","authors":"Arif Ridho Lubis, S. Prayudani, Y. Fatmi, Al-Khowarizmi, Julham, Y. Y. Lase","doi":"10.1109/ICITech50181.2021.9590186","DOIUrl":"https://doi.org/10.1109/ICITech50181.2021.9590186","url":null,"abstract":"Image processing is one of the sciences in image processing which can involve several other techniques such as data mining techniques, in this case the detection of an image. Images are generally carried out classification which results in accurate detection wherein the detection of an image is carried out by extracting the features so that the image can be recognized by computation. One of the extract features that are superior and easy to apply in computational techniques is HOG (Histogram OF Oriented Gradients). The HOG feature can be useful in helping detect images in the form of Tuberculosis xray. After extracting the features, then the classification is carried out using 2 methods that are good for learning levels such as KNN (K-Nearest Neighbor) and SVM (Support Vector Machine). The results of this paper in the detection of HOG Tuberculosis X-ray with KNN for positive images got an accuracy of 77.95% while the negative ones got an accuracy of 78.65%. The results of HOG detection on Tuberculosis X-ray results with SVM on images that were positive got an accuracy of 65.75% while those who were negative were 79.39%.","PeriodicalId":429669,"journal":{"name":"2021 2nd International Conference on Innovative and Creative Information Technology (ICITech)","volume":"67 24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127396712","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