2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)最新文献_第8页

Hate Speech Detection on Twitter Using BERT Algorithm 基于BERT算法的Twitter仇恨语音检测

2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE) Pub Date : 2023-02-16 DOI: 10.1109/ICCoSITE57641.2023.10127831

Adine Nayla, C. Setianingsih, B. Dirgantoro

{"title":"Hate Speech Detection on Twitter Using BERT Algorithm","authors":"Adine Nayla, C. Setianingsih, B. Dirgantoro","doi":"10.1109/ICCoSITE57641.2023.10127831","DOIUrl":"https://doi.org/10.1109/ICCoSITE57641.2023.10127831","url":null,"abstract":"Hate speech on one social media platform, Twitter, is uncommon. Users on the Twitter platform can freely obtain, exchange information, and express opinions. This is one of the main factors that a person can be exposed to hate speech on Twitter. Victims who are exposed to hate speech may suffer from mental health disorders because most victims of hate speech are attacked verbally or emotionally. However, the lack of countermeasures against the detection of hate speech on the Twitter social media platform is still rare. In this study, a simulation was carried out using the website, along with testing and analyzing the detection of hate speech. The test is done by inputting a text on the hate speech website, and then the website will do a preprocessing and analyze this text using the BERT algorithm to classify whether the word is hate speech or not. The training results found that the detection of hate speech on Twitter user accounts using the BERT Algorithm has a 78.69% accuracy, a 78.90% precision, a 78.69% recall, and a 78.77% F1 score against the classification of hate speech groups. Thus users will more easily detect hate speech on Twitter by using the hate speech website.","PeriodicalId":256184,"journal":{"name":"2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128226589","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

Synthesis of IoT Sensor Telemetry Data for Smart Home Edge-IDS Evaluation 面向智能家居边缘ids评估的物联网传感器遥测数据综合

2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE) Pub Date : 2023-02-16 DOI: 10.1109/ICCoSITE57641.2023.10127781

Sasirekha Gvk, Amulya Bangari, M. Rao, Jyotsna L. Bapat, D. Das

引用次数: 0

Detection of Kidney Cysts of Kidney Ultrasound Image using Hybrid Method: KNN, GLCM, and ANN Backpropagation 利用KNN、GLCM和ANN反向传播混合方法检测肾脏超声图像中的肾囊肿

2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE) Pub Date : 2023-02-16 DOI: 10.1109/ICCoSITE57641.2023.10127703

Mardison, Yuhandri

引用次数: 1

Aspect-based Sentiment Analysis using Long Short-term Memory Model for Leveraging Restaurant Service Management 基于长短期记忆模型的情感分析在餐饮服务管理中的应用

2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE) Pub Date : 2023-02-16 DOI: 10.1109/ICCoSITE57641.2023.10127708

Y. Heryadi, B. Wijanarko, Dina Fitria Murad, C. Tho, Kiyota Hashimoto

{"title":"Aspect-based Sentiment Analysis using Long Short-term Memory Model for Leveraging Restaurant Service Management","authors":"Y. Heryadi, B. Wijanarko, Dina Fitria Murad, C. Tho, Kiyota Hashimoto","doi":"10.1109/ICCoSITE57641.2023.10127708","DOIUrl":"https://doi.org/10.1109/ICCoSITE57641.2023.10127708","url":null,"abstract":"In general, the hospitality industry has been acknowledged as a major sector that gives a high contribution to economic development in many countries including Indonesia. For that reason, many initiatives have been implemented to help the growth of the hospitality industry in many countries including Indonesia to rebound from the harsh impact of the Covid-19 Pandemic. One such initiative is improving restaurant services as the main sector of the hospitality industry. This paper presents empirical results of sentiment analysis as a means to assess the quality of restaurant services as the first step to improving service quality. In particular, this study explores the aspect-based sentiment analysis method to identify some aspects of restaurant service which need improvement by learning the polarity of customers toward the restaurant services without having to meet the customers directly. By using the aspect-based sentiment analysis method, the customer sentiments comprising opinions, sentiments, evaluations, attitudes, and emotions from restaurant service can be analyzed using customers’ online reviews as input. The main experiment findings showed that the Long Short-term Memory model can achieve high performance in predicting aspect polarization in restaurant service reviews. Other findings suggest that Sigmoid as an activation function achieved 0.97 average training accuracy and 0.69 average testing accuracy giving a better performance to the model in comparison to ReLU, Tanh, and ELU activation functions.","PeriodicalId":256184,"journal":{"name":"2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126003375","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

Comparative Models of Price Estimation Using Multiple Linear Regression and Random Forest Methods 基于多元线性回归和随机森林方法的价格估算比较模型

2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE) Pub Date : 2023-02-16 DOI: 10.1109/ICCoSITE57641.2023.10127705

Denny Jean Crosss Sihombing, Desi C. Othernima, Jonson Manurung, J. Sagala

{"title":"Comparative Models of Price Estimation Using Multiple Linear Regression and Random Forest Methods","authors":"Denny Jean Crosss Sihombing, Desi C. Othernima, Jonson Manurung, J. Sagala","doi":"10.1109/ICCoSITE57641.2023.10127705","DOIUrl":"https://doi.org/10.1109/ICCoSITE57641.2023.10127705","url":null,"abstract":"The house is one of the essential humans needs as a place to gather and do activities with family, and shelter, as well as a means of investment. The growth rate of people's demand for housing, especially houses in an area, is influenced by the rate of population growth in that area. Some regions in Indonesia with a reasonably high population growth rate are Jakarta, Bogor, Depok, Tangerang, and Bekasi (Jabodetabek). On the other hand, property entrepreneurs must be able to project house prices because businesses engaged in the property sector are currently very competitive. This study aims to model and compare several machine learning methods to estimate house prices in Jabodetabek based on facilities, year of construction, location, land and building area, number of rooms, condition of house construction, and legality documents. This modeling uses Multiple Linear Regression and Random Forest methods. The results of the modeling evaluation where the Random Forest model has an accuracy rate of 95.6%, while the Multiple Linear Regression model has an accuracy rate of 75%.","PeriodicalId":256184,"journal":{"name":"2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126364281","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

Effect Difference Size of Tetrahedron Sun Tracker Based on Sensor for Energy Harvesting 基于能量收集传感器的四面体太阳跟踪器效应差大小

2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE) Pub Date : 2023-02-16 DOI: 10.1109/ICCoSITE57641.2023.10127761

Hari Anna Lastya, Y. Away, T. Tarmizi, I. D. Sara, M. Ikhsan

{"title":"Effect Difference Size of Tetrahedron Sun Tracker Based on Sensor for Energy Harvesting","authors":"Hari Anna Lastya, Y. Away, T. Tarmizi, I. D. Sara, M. Ikhsan","doi":"10.1109/ICCoSITE57641.2023.10127761","DOIUrl":"https://doi.org/10.1109/ICCoSITE57641.2023.10127761","url":null,"abstract":"This research is a new size of implementing a light sensor to detect sunlight on a dual-axis sun tracker based on tetrahedron geometry. The sun tracker is installed with different sizes of sun tracker based on tetrahedron geometry. The sun tracker can track the position of sunlight perpendicular to the solar cell which has the strongest light intensity. This study aims to energy harvesting and the Field of View generated. The method used to obtain voltage and current uses a control system with a proportional integral derivative (PID) algorithm and modifies the size of the sun tracker by adding a triangle height of the triangular tetrahedron. In addition, the light sensor used to track sunlight uses a phototransistor sensor. The result of energy harvesting is that the sun tracker using the proposed sensor collects 159.9% more energy than the previous sun tracker. The modification of the existing tetrahedron shape produces a Field of View (FOV) of 310° for the proposed sun tracker and FOV of 289.4° for the previous sun tracker.","PeriodicalId":256184,"journal":{"name":"2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128893765","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

Predicting the Success of Garment Sales on Transaction Data using the Classification Method with the Naïve Bayes Algorithm 基于Naïve贝叶斯算法的交易数据分类方法预测服装销售成功

2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE) Pub Date : 2023-02-16 DOI: 10.1109/ICCoSITE57641.2023.10127693

A. Sani, Samuel, Djaka Suryadi, Firman Noor Hasan, Ade Davy Wiranata, Siti Aisyah

{"title":"Predicting the Success of Garment Sales on Transaction Data using the Classification Method with the Naïve Bayes Algorithm","authors":"A. Sani, Samuel, Djaka Suryadi, Firman Noor Hasan, Ade Davy Wiranata, Siti Aisyah","doi":"10.1109/ICCoSITE57641.2023.10127693","DOIUrl":"https://doi.org/10.1109/ICCoSITE57641.2023.10127693","url":null,"abstract":"In facing business competition, one of which is the fast-growing garment business, companies must maintain the continuity of the business they run and meet consumer needs. Companies must be able to predict what items are selling well from processing previous transaction data so that the results can help the company know what goods must be produced in the following year to meet consumer needs. Because of that, this research reprocesses sales transaction data for 2020 to classify goods sold and not sold using the Naïve Bayes algorithm, a classification algorithm using probability and statistical methods proposed by British scientist Thomas Bayes. Sales transaction data for 2020 will be processed using existing processes in the Knowledge Discovery Database (KDD), such as data selection, preprocessing, transformation, data mining, and evaluation. The supporting application used to process sales transaction data is Knime. Based on the partition from three ranges of training data and data testing (70%:30% | 60%:40% | 50%:50%), the results of this study show are the dress and pants category shows the highest significant value; these dresses and pants need to be further increased in production for the coming year that the accuracy level from the confusion matrix with the Naïve Bayes algorithm is above 90%, which means the Naïve Bayes algorithm can be used to predict garment sales so that it can be a reference for companies to increase sales in the following years of goods that are classified as buyable by consumers using the Naïve Bayes algorithm.","PeriodicalId":256184,"journal":{"name":"2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114269339","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

Implementation of Convolutional Neural Network Algorithm to Pest Detection in Caisim 卷积神经网络算法在茜草害虫检测中的实现

2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE) Pub Date : 2023-02-16 DOI: 10.1109/ICCoSITE57641.2023.10127792

Cendekia Luthfieta Nazalia, P. Palupiningsih, B. Prayitno, Yudhi Purwanto

{"title":"Implementation of Convolutional Neural Network Algorithm to Pest Detection in Caisim","authors":"Cendekia Luthfieta Nazalia, P. Palupiningsih, B. Prayitno, Yudhi Purwanto","doi":"10.1109/ICCoSITE57641.2023.10127792","DOIUrl":"https://doi.org/10.1109/ICCoSITE57641.2023.10127792","url":null,"abstract":"High demand for caisim in Indonesia’s main export commodity must be accompanied by a good planting process. The obstacle faced is that farmers are currently able to apply pesticides when the caisim plants have holes due to being eaten by pests. This control can be a good step to maximize the yield of caisim farming. However, many farmers have not implemented proper control of pests, one of which is farmers in Kebon Raya Dempo, South Sumatera, Indonesia. The obstacles faced such as not being able to detect pests correctly and provide pesticides with precision. Motivated by CNN’s success in image classification, a learning-based approach has been carried out in this study to detect the presence of pests in caisim. The experimental results show differences in accuracy in each experiment with a dataset of 1000, consisting of 500 image data with pests and 500 without pests. The accuracy of the experiment A – CNN from Scratch is 48.33%, precision 1, recall 0.48, F1-score 0.65, experiment B – CNN from Scratch is 73.00% precision 1, recall 0.64, F1- score 0.78, experiment C–CNN from Scratch experiment is 92.00% precision 0.88, recall 0.96, F1-score 0.92. Of the 3 trials, experiment A – CNN from Scratch experienced underfitting, experiment B – CNN from Scratch overfitting, and the C – CNN experiment from Scratch can be used for pest detection in ciasim.","PeriodicalId":256184,"journal":{"name":"2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)","volume":"1878 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124011588","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

ICCoSITE 2023 Cover Page ICCoSITE 2023封面页

2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE) Pub Date : 2023-02-16 DOI: 10.1109/iccosite57641.2023.10127805

引用次数: 0

Link Prediction in Educational Graph Data to Predict Elective Course using Graph Convolutional Network Model 用图卷积网络模型预测教育图数据中的链接预测选修课

2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE) Pub Date : 2023-02-16 DOI: 10.1109/ICCoSITE57641.2023.10127670

Meilia Nur Indah Susanti, Y. Heryadi, Y. Rosmansyah, W. Budiharto

{"title":"Link Prediction in Educational Graph Data to Predict Elective Course using Graph Convolutional Network Model","authors":"Meilia Nur Indah Susanti, Y. Heryadi, Y. Rosmansyah, W. Budiharto","doi":"10.1109/ICCoSITE57641.2023.10127670","DOIUrl":"https://doi.org/10.1109/ICCoSITE57641.2023.10127670","url":null,"abstract":"Personalized learning has achieved the attention of many researchers in the Education field. Personalized learning is a teaching model in which students (learners) have a central role in the learning process. By using this approach, educational methods, and techniques are customized and adapted to be better suited for each learner, with their unique learning style, background, needs, and previous experiences. Based on what the learners have already learned, subjects have already known, and skills have already developed each student in a personalized learning process will get a \"learning plan\". This approach is different from a conventional approach or known as the \"one size fits all\" approach. The challenge of personalized learning is how to connect a learner’s previous knowledge, skills and with learning materials that will link that understanding with new knowledge. This paper presents a novelty technique to implement personalized learning by automating a predicted linkage between a student in higher education and elective courses based on previous learning achievement. In this study, Graph Convolutional Networks (GCNs) are used to address link prediction tasks between student and elective courses. The empirical results showed that the GCN model can be used to predict elective courses for a student with 62.5 % average accuracy.","PeriodicalId":256184,"journal":{"name":"2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)","volume":"30 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115929505","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