2022 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM)最新文献_第5页

Skincare Recommender System Using Neural Collaborative Filtering with Implicit Rating 基于隐式评分的神经协同过滤护肤推荐系统

2022 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM) Pub Date : 2022-11-22 DOI: 10.1109/CENIM56801.2022.10037471

Chaira Qalbyassalam, R. F. Rachmadi, A. Kurniawan

{"title":"Skincare Recommender System Using Neural Collaborative Filtering with Implicit Rating","authors":"Chaira Qalbyassalam, R. F. Rachmadi, A. Kurniawan","doi":"10.1109/CENIM56801.2022.10037471","DOIUrl":"https://doi.org/10.1109/CENIM56801.2022.10037471","url":null,"abstract":"Skincare products are essential cosmetics for women, especially in this modern era. Many e-commerce services provide a variety of skincare products in their catalogs. One problem with purchasing skincare products online is that users cannot try the product and depend on other customers' rating reviews. However, rating reviews on a scale of 1 to 5 are considered insufficient to represent product quality, and users need to read review texts written by other users to get more specific information about the quality of the product. This paper investigated NCF (Neural Collaborative Filtering) for skincare recommender systems. Instead of using explicit rating as usually used on standard recommender systems, we adapted the sentiment score as a rating which, in our experiments, proved can improve the classifier's performance. We collected 180,104 rows of data with 11 data attributes and 1,339 skincare products to evaluate our proposed method. Experiments on the dataset show that the proposed NCF with explicit ratings achieved an RMSE of 0.8033, and the NCF with implicit ratings achieved an RMSE of 0.4931.","PeriodicalId":118934,"journal":{"name":"2022 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124145434","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

Flight Delay Prediction for Mitigation of Airport Commercial Revenue Losses Using Machine Learning on Imbalanced Dataset 基于不平衡数据集机器学习的航班延误预测缓解机场商业收入损失

2022 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM) Pub Date : 2022-11-22 DOI: 10.1109/CENIM56801.2022.10037369

Rae Arun Sugara, D. Purwitasari

{"title":"Flight Delay Prediction for Mitigation of Airport Commercial Revenue Losses Using Machine Learning on Imbalanced Dataset","authors":"Rae Arun Sugara, D. Purwitasari","doi":"10.1109/CENIM56801.2022.10037369","DOIUrl":"https://doi.org/10.1109/CENIM56801.2022.10037369","url":null,"abstract":"Flight delay is one of the factors that affect the decline in customer satisfaction and airport revenue. In addition to influencing customer perceptions of airport services, flight delay also has an impact on decreasing airport revenue and operation. This study models a flight delay prediction, and the process is carried out using Decision Tree, Random Forest, Gradient Boosted Tree, and XGBoost Tree algorithms. This study has also used and merged the weather characteristic data as secondary data to the airport operational flight data. To anticipate the imbalanced class, several sampling techniques were applied. Synthetic Minority Over Sampling Technique (SMOTE), Random Over-Sampling (ROS), Random Under-Sampling (RUS), and combining ROS with RUS are being used. The result of processing the analysis is in the form of a model to predict the category of flight delay. The model has been evaluated by using the Confusion Matrix and Area Under ROC Curve (AUC) value. The result of this study shows the Random Forest classifier with the combination of ROS + RUS technique and data split ratio of 90:10 gave the highest accuracy, error rate, and AUC value as shown as 82.58%, 17.42%, and 81.1% respectively on data testing. The result of the flight delay prediction model is expected to be a strategic recommendation for determining airport policies in the future. By implementing the best strategy related to the airport operation, it could carry out commercial planning in order to optimize airport commercial revenue.","PeriodicalId":118934,"journal":{"name":"2022 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128292158","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

Classifying News Based on Indonesian News Using LightGBM 基于LightGBM的印尼新闻分类

2022 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM) Pub Date : 2022-11-22 DOI: 10.1109/CENIM56801.2022.10037401

Arif Ridho Lubis, S. Prayudani, Y. Fatmi, Okvi Nugroho

{"title":"Classifying News Based on Indonesian News Using LightGBM","authors":"Arif Ridho Lubis, S. Prayudani, Y. Fatmi, Okvi Nugroho","doi":"10.1109/CENIM56801.2022.10037401","DOIUrl":"https://doi.org/10.1109/CENIM56801.2022.10037401","url":null,"abstract":"There were several news categories that present editors with challenges. Some news categories, such megapolitan, national, celebrity, news, lifestyle, and economics, used vocabulary that was quite similar to that of the other categories. International required an editor to be familiar with the article's contents in order for it to be uploaded and placed in the proper category. We had to first digest the news before we could label it compared to other kinds of data. The text mining approach, which attempts to make text or documents may be processed so that it will aid in the process of news classification, will be used to categorize and determine the type of news in this context. The Light Gradient Boosted Machine (LightGBM) model was used in this study to increase the gradient point with a learning stage and obtain the optimal value. This model's training process was intended to be quick while consuming less storage space and processing information more accurately. The accuracy of the classifications made using a confusion matrix to quantify the findings of this investigation, which were news type classifications, was 86%.","PeriodicalId":118934,"journal":{"name":"2022 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133888806","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

Fuzzy Logic in Suggesting The Appropriate University Major for Students According to Their Preferences 模糊逻辑在大学生择优专业中的应用

2022 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM) Pub Date : 2022-11-22 DOI: 10.1109/CENIM56801.2022.10037457

Aseel Abdalnabi, Suhad Daraghmeh, Amjad Hawash

引用次数: 0

Control Design of Quadcopter using Linear Quadratic Gaussian (LQG) 线性二次高斯(LQG)四轴飞行器控制设计

2022 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM) Pub Date : 2022-11-22 DOI: 10.1109/CENIM56801.2022.10037533

Mardlijah, Zahra Nur Alifah

引用次数: 0

Design And Engineering Of sEMG-Controlled Multi-Actuator Prosthesis For Disabled Wrist-Amputee 腕部残肢肌电控制多致动器假肢的设计与工程

2022 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM) Pub Date : 2022-11-22 DOI: 10.1109/CENIM56801.2022.10037349

Fadhli Ismail Hatta, E. A. Suprayitno, Rachmad Setiawan

引用次数: 0

An Embedded Computer Vision Method to Extract Percentage of Eye Close for Detecting Drowsiness of a Safety Driving System 一种用于安全驾驶系统睡意检测的嵌入式计算机视觉提取眼闭百分比方法

2022 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM) Pub Date : 2022-11-22 DOI: 10.1109/CENIM56801.2022.10037535

Sukma Firdaus, A. Arifin, N. Hermawan, Fatdiansyah

{"title":"An Embedded Computer Vision Method to Extract Percentage of Eye Close for Detecting Drowsiness of a Safety Driving System","authors":"Sukma Firdaus, A. Arifin, N. Hermawan, Fatdiansyah","doi":"10.1109/CENIM56801.2022.10037535","DOIUrl":"https://doi.org/10.1109/CENIM56801.2022.10037535","url":null,"abstract":"Various factors can cause accidents, but the main factor that dominates the causes of accidents is the driver's actions, especially continuing to drive in a state of drowsiness. To avoid accidents, a safety driving system is needed to inform the driver when in a drowsiness condition. This paper reports on the early stages of developing a safety driving system implemented in an embedded computer vision method. We calculated the perclos from the ear and the ear from eye landmarks. We obtained significant results from the perclos when the driver had driven for 3 hours. The average perclos for 3 hours is 0.152, while after more than 3 hours driving is 0.590. This result is significant in distinguishing the driver's condition, especially in developing rules for a safety driving system. The processing speed we obtained in extracting eye landmarks was 189.91 milliseconds at a speed of 10 fps. This speed is fast enough to detect drowsiness. Furthermore, developing a drowsiness detection system will involve a professional driver subject who works as a transporter and adding psychological signal characteristics such as ECG signal and driving behavior modality parameters in producing a multimodal based decision-making system.","PeriodicalId":118934,"journal":{"name":"2022 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122170907","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

An IoT Garbage Monitoring System for Effective Garbage Management 一种有效垃圾管理的物联网垃圾监控系统

2022 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM) Pub Date : 2022-11-22 DOI: 10.1109/CENIM56801.2022.10037387

Hanlin Cai, Jiaqi Hu, Zhengyang Li, W. H. Lim, M. Mokayef, C. Wong

引用次数: 2

Balinese Carving Ornaments Classification Using InceptionResnetV2 Architecture 使用InceptionResnetV2架构的巴厘岛雕刻饰品分类

2022 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM) Pub Date : 2022-11-22 DOI: 10.1109/CENIM56801.2022.10037265

M. W. A. Kesiman, K. T. Dermawan, I. G. M. Darmawiguna

引用次数: 1

Synthesis Ensemble Oversampling and Ensemble Tree-Based Machine Learning for Class Imbalance Problem in Breast Cancer Diagnosis 综合集成过采样和基于集成树的机器学习在乳腺癌诊断中的类失衡问题

2022 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM) Pub Date : 2022-11-22 DOI: 10.1109/CENIM56801.2022.10037251

Slamet Sudaryanto N, M. Purnomo, D. Purwitasari, E. M. Yuniarno

引用次数: 0