2020 4th International Conference on Informatics and Computational Sciences (ICICoS)最新文献

Mining Biological Information from 3D Medulloblastoma Cancerous Gene Expression Data Using TimesVector Triclustering Method 利用时间向量三聚类方法从成神经管细胞瘤癌基因表达数据中挖掘生物信息

2020 4th International Conference on Informatics and Computational Sciences (ICICoS) Pub Date : 2020-11-10 DOI: 10.1109/ICICoS51170.2020.9299108

Ika Marta Sari, S. Soemartojo, T. Siswantining, Devvi Sarwinda

{"title":"Mining Biological Information from 3D Medulloblastoma Cancerous Gene Expression Data Using TimesVector Triclustering Method","authors":"Ika Marta Sari, S. Soemartojo, T. Siswantining, Devvi Sarwinda","doi":"10.1109/ICICoS51170.2020.9299108","DOIUrl":"https://doi.org/10.1109/ICICoS51170.2020.9299108","url":null,"abstract":"Triclustering analysis is the development of clustering analysis and biclustering analysis. The purpose of triclustering study is to group three-dimensional data simultaneously. The three-dimensional data can be in the form of observations, attributes, and context. One of the approaches used in tricluster analysis, namely an approach based on sample patterns, is the TimesVector method. The TimesVector method aims to group data matrices that show the same or different patterns in three-dimensional data. The TimesVector method has a work step that starts with reducing the three-dimensional data matrix to a two-dimensional data matrix to minimize complexity in the grouping. In this method, the Spherical K-means algorithm will be used in cluster it. The next step is to identify the pattern of the groups generated in the Spherical K-means. The pattern referred to consists of three types, namely DEP (Differentiated Patterns), ODEP (Differentiated Patterns), and SEP (Differentiated Patterns). The TimesVector method was applied on gene expression data, namely medulloblastoma cancerous data carried out in 6 scenarios. Each scenario uses the same many clusters but different threshold values. The six scenarios’ results will be validated using the coverage value and the tricluster diffusion (TD) value. The application of the TimesVector method shows that using a threshold of 1.5 gives the most optimal results because it has a high coverage value and a low TD value. High-value coverage indicates the method’s ability to extract data, and a low TD value suggests that the resulting tricluster has a large volume and high coherence. The best tricluster results can be used by medical experts to perform further actions on medulloblastoma cancerous patients.","PeriodicalId":122803,"journal":{"name":"2020 4th International Conference on Informatics and Computational Sciences (ICICoS)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114144394","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

ICICoS 2020 TOC

2020 4th International Conference on Informatics and Computational Sciences (ICICoS) Pub Date : 2020-11-10 DOI: 10.1109/icicos51170.2020.9299097

引用次数: 0

ICICoS 2020 Cover Page ICICoS 2020封面

2020 4th International Conference on Informatics and Computational Sciences (ICICoS) Pub Date : 2020-11-10 DOI: 10.1109/icicos51170.2020.9299064

引用次数: 0

Simple MyUnet3D for BraTS Segmentation 简单的MyUnet3D BraTS分割

2020 4th International Conference on Informatics and Computational Sciences (ICICoS) Pub Date : 2020-11-10 DOI: 10.1109/ICICoS51170.2020.9299072

Agus Subhan Akbar, C. Fatichah, N. Suciati

引用次数: 3

Implementation of Textrank Algorithm in Product Review Summarization Textrank算法在产品评论摘要中的实现

2020 4th International Conference on Informatics and Computational Sciences (ICICoS) Pub Date : 2020-11-10 DOI: 10.1109/ICICoS51170.2020.9299005

M. R. Ramadhan, S. Endah, Aprinaldi Jasa Mantau

{"title":"Implementation of Textrank Algorithm in Product Review Summarization","authors":"M. R. Ramadhan, S. Endah, Aprinaldi Jasa Mantau","doi":"10.1109/ICICoS51170.2020.9299005","DOIUrl":"https://doi.org/10.1109/ICICoS51170.2020.9299005","url":null,"abstract":"Internet technology led to the emergence of Web 2.0 which increase the number of User Generated Content (UGC) on the network. Online product review is a form of UGC. The case study in this research is a review of handphone products. The large number of reviews will take long time to read and compare between existing product reviews, so we need a technique that online product review can be read quickly without losing of its important information. The technique that can be used is the text summarizing technique. Text summarization techniques produce simplified versions of texts. In general, text summarization can be divided into two types, namely extractive and abstractive summaries. This research used extractive summaries. One important component in the process of obtaining an extractive summary is sentence extraction. In this study, the algorithm used for sentence extraction is TextRank. The purpose of this study was to determine the performance of the TextRank algorithm with handphone product reviews data by implementing it in different data conditions based on the presence or absence of a stopword and typo. These data conditions are used to formulate test scenarios. Testing is done by calculating the Rouge-1 value which compares the summary of system and experts. Expert who involved in this study are 2. Expert 1 is a person with expertise in Indonesian and Expert 2 is someone who has the knowledge and understanding of mobile phones with various types and characteristics. From the test results obtained, Expert 1 gets the best results for scenario 2 where data conditions are there is typo and no stopword with an average value of Rouge-1 of 42.29% and Expert 2 gets the best results for scenario 3 where data conditions are no typo and there is stopword with an average value of Rouge-1 is 46.71%. The results shows that the TextRank algorithm is not able to produce a good summary for handphone product review dataset.","PeriodicalId":122803,"journal":{"name":"2020 4th International Conference on Informatics and Computational Sciences (ICICoS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122332680","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

Indonesian Ethnicity Recognition Based on Face Image Using Uniform Local Binary Pattern (ULBP) and Color Histogram 基于统一局部二值模式(ULBP)和颜色直方图的印尼民族识别

2020 4th International Conference on Informatics and Computational Sciences (ICICoS) Pub Date : 2020-11-10 DOI: 10.1109/ICICoS51170.2020.9299103

Tiani Tiara Putri, Ema Rachmawati, F. Sthevanie

引用次数: 3

Prediction of Hotel Booking Cancellation using CRISP-DM 基于CRISP-DM的酒店预订取消预测

2020 4th International Conference on Informatics and Computational Sciences (ICICoS) Pub Date : 2020-11-10 DOI: 10.1109/ICICoS51170.2020.9299011

Zharfan Akbar Andriawan, Satriawan Rasyid Purnama, A. Darmawan, Ricko, A. Wibowo, A. Sugiharto, F. Wijayanto

{"title":"Prediction of Hotel Booking Cancellation using CRISP-DM","authors":"Zharfan Akbar Andriawan, Satriawan Rasyid Purnama, A. Darmawan, Ricko, A. Wibowo, A. Sugiharto, F. Wijayanto","doi":"10.1109/ICICoS51170.2020.9299011","DOIUrl":"https://doi.org/10.1109/ICICoS51170.2020.9299011","url":null,"abstract":"Online travel sales continue to increase every year. Recorded in 2019, digital transactions related to online travel reached USD 755.4 billion. One of the supports of the travel business is the tourism and hospitality industry. The online reservation system is one of the most attractive solutions in the hospitality industry. Cancellation of hotel bookings or reservations through the online system is currently one of the problems in the hotel management system. When the reservation has been canceled, the hotel will be harmed. Therefore, predicting whether a booking will be canceled or not using the help of data science is needed so that the hotel can minimize lost profits. Therefore, by using datasets related to hotel booking requests, a predictive analysis using the CRISP-DM framework is conducted. By first performing some data preparation processes, this study uses a tree-based algorithm to perform the prediction. The experiment produced that Random Forest model has the best value with an accuracy value of 0.8725 and it is found that the time difference between booking is made and arrival time is the most influential feature in predicting the level of cancellation.","PeriodicalId":122803,"journal":{"name":"2020 4th International Conference on Informatics and Computational Sciences (ICICoS)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125510837","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

Simulation Study using Average Difference Algorithm to Analyze the Outlierness Degree of Spatial Observations 利用平均差分算法分析空间观测异常度的模拟研究

2020 4th International Conference on Informatics and Computational Sciences (ICICoS) Pub Date : 2020-11-10 DOI: 10.1109/ICICoS51170.2020.9298999

Z. F. Pusdiktasari, Rahma Fitriani, E. Sumarminingsih

引用次数: 0

Classical Machine Learning Classification for Javanese Traditional Food Image 爪哇传统食物图像的经典机器学习分类

2020 4th International Conference on Informatics and Computational Sciences (ICICoS) Pub Date : 2020-11-10 DOI: 10.1109/ICICoS51170.2020.9299039

Puteri Khatya Fahira, Zulia Putri Rahmadhani, P. Mursanto, A. Wibisono, H. Wisesa

{"title":"Classical Machine Learning Classification for Javanese Traditional Food Image","authors":"Puteri Khatya Fahira, Zulia Putri Rahmadhani, P. Mursanto, A. Wibisono, H. Wisesa","doi":"10.1109/ICICoS51170.2020.9299039","DOIUrl":"https://doi.org/10.1109/ICICoS51170.2020.9299039","url":null,"abstract":"Indonesia is a culturally rich nation with more than three hundred ethnic groups. This sheer number of ethnic groups reflects the country’s diverse culture. One of the identities that could be associated with a group of people is its cuisine. As with the high number of ethnic groups, the diversity of Indonesian traditional food is also very high. However, the diversity of food is threatened by the current food systems, which could endanger food security of a population. To prevent this issue, a traditional food database system is created to monitor the food systems of each area in Indonesia. In this research, automatic traditional food classification is developed as one of the main features of this system. There were 17 Indonesian traditional foods from the Java area that were acquired and used as a dataset for this research. Several key features of the food dataset were extracted using various methods. The data were then classified using various machine learning algorithms. From the experiment, Random Forest classifier achieved the highest accuracy compared to other classical machine learning methods.","PeriodicalId":122803,"journal":{"name":"2020 4th International Conference on Informatics and Computational Sciences (ICICoS)","volume":"294 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132461966","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: 8

Xception Architecture Transfer Learning for Garbage Classification 面向垃圾分类的异常架构迁移学习

2020 4th International Conference on Informatics and Computational Sciences (ICICoS) Pub Date : 2020-11-10 DOI: 10.1109/ICICoS51170.2020.9299017

Rismiyati, S. Endah, Khadijah, Ilman Nabil Shiddiq

引用次数: 19