2018 International Conference on Advanced Computer Science and Information Systems (ICACSIS)最新文献_第4页

ICACSIS 2018 Welcome Message from Dean Fasilkom Fasilkom院长致2018年ICACSIS欢迎辞

2018 International Conference on Advanced Computer Science and Information Systems (ICACSIS) Pub Date : 2018-10-01 DOI: 10.1109/icacsis.2018.8618233

引用次数: 0

The Importance of Computer Science in Industry 4.0 计算机科学在工业4.0中的重要性

2018 International Conference on Advanced Computer Science and Information Systems (ICACSIS) Pub Date : 2018-10-01 DOI: 10.1109/ICACSIS.2018.8618217

H. T. Y. Achsan, W. Wibowo, Heryudi Ganesha, M. Achsan, W. T. Putri

{"title":"The Importance of Computer Science in Industry 4.0","authors":"H. T. Y. Achsan, W. Wibowo, Heryudi Ganesha, M. Achsan, W. T. Putri","doi":"10.1109/ICACSIS.2018.8618217","DOIUrl":"https://doi.org/10.1109/ICACSIS.2018.8618217","url":null,"abstract":"Since coined by a German researcher in 2011, Industry 4.0 has piqued the interests of many researchers. The number of scientific publications related to Industry 4.0 or Fourth Industrial Revolution increases tremendously. Unfortunately, 90% of those publications have not been reviewed thus hindering many to track the progress and trends of research in this field. This article aims to address the most subject areas, top productive countries, the most influential authors, the most productive and influential journals/proceedings, the most used/influential keywords, and research trends related to Industry 4.0 based on documents indexed by Scopus. The method used is scientometrics and linear regression. It is revealed that Germany, China and Italy are the most prolific countries, but US, Portugal and UK are the most impactful countries. Researchers from China also dominate the top ten of most influential authors. The study prediction shows that Cyber Physical Systems (CPS), Internet of Things (IoT), Intelligent/Smart Manufacturing, Automation, and Big Data will dominate researches in 2018 and the next two years.","PeriodicalId":207227,"journal":{"name":"2018 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123684664","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

Deep Structured Convolutional Neural Network for Tomato Diseases Detection 基于深度结构卷积神经网络的番茄病害检测

2018 International Conference on Advanced Computer Science and Information Systems (ICACSIS) Pub Date : 2018-10-01 DOI: 10.1109/ICACSIS.2018.8618169

Endang Suryawati, Rika Sustika, R. S. Yuwana, Agus Subekti, H. Pardede

{"title":"Deep Structured Convolutional Neural Network for Tomato Diseases Detection","authors":"Endang Suryawati, Rika Sustika, R. S. Yuwana, Agus Subekti, H. Pardede","doi":"10.1109/ICACSIS.2018.8618169","DOIUrl":"https://doi.org/10.1109/ICACSIS.2018.8618169","url":null,"abstract":"Plant diseases outbreaks can cause significant threat to food security. Early detection of the diseases using machine learning could avoid such disaster. Currently, deep learning, which is a recent technology in machine learning, gained much popularity for object recognition tasks. Convolutional neural network (CNN) is one major techniques for object identification in deep learning. In this paper, we evaluate the effect of different depth of CNN architectures on the detection accuracies of the plant diseases detection. Various CNN architectures with different depth are investigated. They are simple CNN baseline (with two layer of convolutional layers), AlexNet (with five convolutional layers), and VGGNet (with 13 convolutional layers). We also evaluate GoogleNet architectures. Unlike previously mentioned architectures, GoogleNet use convolutional layers with various resolutions to be concantenated with each other, emphasizing the effect on not only the deep architecture but also a wide one. The experimental results suggest that CNN with deeper architecture, i.e. VGGNet, outperforms others, indicating that having deeper architectures may be more benefit for this task.","PeriodicalId":207227,"journal":{"name":"2018 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","volume":"12 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132463414","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: 47

Proposed User Interface Generation for Software Product Lines Engineering 建议的用户界面生成软件产品线工程

2018 International Conference on Advanced Computer Science and Information Systems (ICACSIS) Pub Date : 2018-10-01 DOI: 10.1109/ICACSIS.2018.8618199

S. Sakinah, H. Fadhlillah, A. Azurat, M. R. Setyautami

引用次数: 0

Numerical Methods for Retrieval and Adaptation in Nagao’s EBMT model Nagao EBMT模型检索与自适应的数值方法

2018 International Conference on Advanced Computer Science and Information Systems (ICACSIS) Pub Date : 2018-10-01 DOI: 10.1109/ICACSIS.2018.8618226

Kun He, T. Zhao, Y. Lepage

引用次数: 0

Osteoarthritis Disease Prediction Based on Random Forest 基于随机森林的骨关节炎疾病预测

2018 International Conference on Advanced Computer Science and Information Systems (ICACSIS) Pub Date : 2018-10-01 DOI: 10.1109/ICACSIS.2018.8618166

Ulfah Aprilliani, Zuherman Rustam

引用次数: 13

Protagoras: A Service for Tagging E-Commerce Products at Scale Protagoras:一种大规模标记电子商务产品的服务

2018 International Conference on Advanced Computer Science and Information Systems (ICACSIS) Pub Date : 2018-10-01 DOI: 10.1109/ICACSIS.2018.8618144

Alfan Nur Fauzan, Rahmatri Mardiko, Prayana Galih

{"title":"Protagoras: A Service for Tagging E-Commerce Products at Scale","authors":"Alfan Nur Fauzan, Rahmatri Mardiko, Prayana Galih","doi":"10.1109/ICACSIS.2018.8618144","DOIUrl":"https://doi.org/10.1109/ICACSIS.2018.8618144","url":null,"abstract":"Despite widespread adoption of machine learning to solve real world problems, the implementation of ML solutions in production environment is more complicated than it seems. It is quite straightforward to write machine learning codes these days but they are not designed to be deployed in production scale where millions of requests per day is a norm. In this paper, we describe our implementation of a ML service for large scale product tagging in e-commerce called Protagoras. The problem of tagging products can be seen as multi-label classification where the labels are product tags. By performing the classification within each product category, the precision can be increased and the inference can be performed faster. Protagoras combined the scalability and speed of microservice implementation in Golang and robust machine learning implementation in Python. We present the architecture of the system with all its components including API endpoints, job queue, database, and monitoring. The benchmark shows that, even with 1000 classifiers in one category, the average latency for online inference is below 300 millisecond. The throughput can be further maximized by replicating the service into multiple servers.","PeriodicalId":207227,"journal":{"name":"2018 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114562672","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

Customer Loyalty in Go-Food: The Antecedent of Satisfaction Go-Food中的顾客忠诚度:满意度的前提

2018 International Conference on Advanced Computer Science and Information Systems (ICACSIS) Pub Date : 2018-10-01 DOI: 10.1109/ICACSIS.2018.8618250

Sumarliyanti, P. W. Handayani, Q. Munajat

引用次数: 2

Analysis and Implementation Measurement of Semantic Similarity Using Content Management Information on WordNet 基于WordNet内容管理信息的语义相似度度量分析与实现

2018 International Conference on Advanced Computer Science and Information Systems (ICACSIS) Pub Date : 2018-10-01 DOI: 10.1109/ICACSIS.2018.8618181

Tommy Wijaya Sagala, Theresia Wati, Solikin, N. Budi, A. Hidayanto

{"title":"Analysis and Implementation Measurement of Semantic Similarity Using Content Management Information on WordNet","authors":"Tommy Wijaya Sagala, Theresia Wati, Solikin, N. Budi, A. Hidayanto","doi":"10.1109/ICACSIS.2018.8618181","DOIUrl":"https://doi.org/10.1109/ICACSIS.2018.8618181","url":null,"abstract":"In natural language processing (NLP), measuring semantic similarity plays an important role. The results of these measurements are often used as the basis for performing natural language processing tasks such as question answering, document classification, machine translation, and so on. This paper analyses the test results using the latest dataset on the implementation of content management utilization on WordNet in the form of taxonomy in measuring semantic similarity values. Further implementation results are compared with Gold Standard datasets for measured performance. The dataset used for testing is SimLex-999. In performance measurement, Pearson Correlation and Spearman Correlation are used. The use of these two correlations because each correlation has several advantages and disadvantages. Based on the test results, Seco Formula resulted in Pearson Correlation and Spearman Correlation of 0.583 and 0.582 respectively. While New Formula resulted in Pearson Correlation and Spearman Correlation respectively of 0.602 and 0.594. The correlation results show strong positive correlation relationship. Therefore, the method of information content in WordNet is feasible to be used to measure the value of semantic similarity.","PeriodicalId":207227,"journal":{"name":"2018 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124119387","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

ICACSIS 2018 Committees

2018 International Conference on Advanced Computer Science and Information Systems (ICACSIS) Pub Date : 2018-10-01 DOI: 10.1109/icacsis.2018.8618152

引用次数: 0