Pub Date : 2018-10-01DOI: 10.1109/icacsis.2018.8618233
{"title":"ICACSIS 2018 Welcome Message from Dean Fasilkom","authors":"","doi":"10.1109/icacsis.2018.8618233","DOIUrl":"https://doi.org/10.1109/icacsis.2018.8618233","url":null,"abstract":"","PeriodicalId":207227,"journal":{"name":"2018 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","volume":"48 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":"127478789","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}
Pub Date : 2018-10-01DOI: 10.1109/ICACSIS.2018.8618217
H. T. Y. Achsan, W. Wibowo, Heryudi Ganesha, M. Achsan, W. T. Putri
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.
{"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}
Pub Date : 2018-10-01DOI: 10.1109/ICACSIS.2018.8618169
Endang Suryawati, Rika Sustika, R. S. Yuwana, Agus Subekti, H. Pardede
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.
{"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}
Pub Date : 2018-10-01DOI: 10.1109/ICACSIS.2018.8618199
S. Sakinah, H. Fadhlillah, A. Azurat, M. R. Setyautami
Software Product Line Engineering (SPLE) is an approach that enables user to create multiple products in a single development. The combination of features in a SPLE application causes variation in the user interface. It needs an adaptive user interface with each configuration of the selected features. Interaction Flow Modeling Language (IFML) is a modeling language of Object Management Group (OMG), used to model User Interface (UI) of an application. Using IFML as a modeling language, an abstract UI model will be created to model each feature of the SPLE application. This study uses AISCO (Adaptive Information System for Charity Organizations) as a real case study. This research aims to analyze SPLE application modeling using abstract UI model model and propose a new strategy to generate UI in SPLE. The result of this research is the process of generating UI using IFML in SPLE.
{"title":"Proposed User Interface Generation for Software Product Lines Engineering","authors":"S. Sakinah, H. Fadhlillah, A. Azurat, M. R. Setyautami","doi":"10.1109/ICACSIS.2018.8618199","DOIUrl":"https://doi.org/10.1109/ICACSIS.2018.8618199","url":null,"abstract":"Software Product Line Engineering (SPLE) is an approach that enables user to create multiple products in a single development. The combination of features in a SPLE application causes variation in the user interface. It needs an adaptive user interface with each configuration of the selected features. Interaction Flow Modeling Language (IFML) is a modeling language of Object Management Group (OMG), used to model User Interface (UI) of an application. Using IFML as a modeling language, an abstract UI model will be created to model each feature of the SPLE application. This study uses AISCO (Adaptive Information System for Charity Organizations) as a real case study. This research aims to analyze SPLE application modeling using abstract UI model model and propose a new strategy to generate UI in SPLE. The result of this research is the process of generating UI using IFML in SPLE.","PeriodicalId":207227,"journal":{"name":"2018 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","volume":"65 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":"132641890","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}
Pub Date : 2018-10-01DOI: 10.1109/ICACSIS.2018.8618226
Kun He, T. Zhao, Y. Lepage
We build an example-based machine translation system. It is an instance of case-based reasoning for machine translation. We introduce numerical methods instead of symbolic methods in two steps: retrieval and adaptation. For retrieval, we test three different approaches to define similarity between sentences. For adaptation, we use neural networks to solve analogies between sentences across languages. Oracle experiments allow to identify the best retrieval technique and to estimate the possibilities of such an approach. The system could place itself between a statistical and a neural machine translation systems on a task with not so large data.
{"title":"Numerical Methods for Retrieval and Adaptation in Nagao’s EBMT model","authors":"Kun He, T. Zhao, Y. Lepage","doi":"10.1109/ICACSIS.2018.8618226","DOIUrl":"https://doi.org/10.1109/ICACSIS.2018.8618226","url":null,"abstract":"We build an example-based machine translation system. It is an instance of case-based reasoning for machine translation. We introduce numerical methods instead of symbolic methods in two steps: retrieval and adaptation. For retrieval, we test three different approaches to define similarity between sentences. For adaptation, we use neural networks to solve analogies between sentences across languages. Oracle experiments allow to identify the best retrieval technique and to estimate the possibilities of such an approach. The system could place itself between a statistical and a neural machine translation systems on a task with not so large data.","PeriodicalId":207227,"journal":{"name":"2018 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","volume":"1 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":"131032320","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}
Pub Date : 2018-10-01DOI: 10.1109/ICACSIS.2018.8618166
Ulfah Aprilliani, Zuherman Rustam
Abstract–Osteoarthritis is a disease of knee joint, indicated from the biochemical changes and thinning of the knee joint cartilage, which can be seen using T2Map MRI and Density-weighted Protons sequence. these tools detect the thickness changes that occur in the cartilage layes which can identify the presence of osteoarthritis and its severity. However, the immediacy of the result of these tools, whether the patient has osteoarthritis or not, is quite low. This paper presents the classification of osteoarthritis disease into three classes of severity using the random forest method. This model can be used to predict the accuracy of osteoarthritis data by 86,96% in diagnosing the disease. The data of 33 patients with osteoarthritis in Cipto Mangunkusumo National Hospital of Indonesia were used.
{"title":"Osteoarthritis Disease Prediction Based on Random Forest","authors":"Ulfah Aprilliani, Zuherman Rustam","doi":"10.1109/ICACSIS.2018.8618166","DOIUrl":"https://doi.org/10.1109/ICACSIS.2018.8618166","url":null,"abstract":"Abstract–Osteoarthritis is a disease of knee joint, indicated from the biochemical changes and thinning of the knee joint cartilage, which can be seen using T2Map MRI and Density-weighted Protons sequence. these tools detect the thickness changes that occur in the cartilage layes which can identify the presence of osteoarthritis and its severity. However, the immediacy of the result of these tools, whether the patient has osteoarthritis or not, is quite low. This paper presents the classification of osteoarthritis disease into three classes of severity using the random forest method. This model can be used to predict the accuracy of osteoarthritis data by 86,96% in diagnosing the disease. The data of 33 patients with osteoarthritis in Cipto Mangunkusumo National Hospital of Indonesia were used.","PeriodicalId":207227,"journal":{"name":"2018 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","volume":"1 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":"130230707","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}
Pub Date : 2018-10-01DOI: 10.1109/ICACSIS.2018.8618144
Alfan Nur Fauzan, Rahmatri Mardiko, Prayana Galih
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.
{"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}
Pub Date : 2018-10-01DOI: 10.1109/ICACSIS.2018.8618250
Sumarliyanti, P. W. Handayani, Q. Munajat
This study aims to analyze factors that affect customer satisfaction which will influence customer loyalty in Go-Food, an Online Delivery-Sourcing in Indonesia. Customer loyalty model was based on previous research which includes perceived value and satisfaction aspect. The antecedents of satisfaction were adopted from mobile service quality (M-S-QUAL). To validate the factors, 852 respondents' data were collected. The data were analyzed using Covariance-Based Structural Equation Model (CB-SEM) and processed in AMOS 22.0 tools. Based on the analysis, this study found six antecedents of satisfaction which are efficiency, content, fulfilment, responsiveness, contact, and billing. All the factors positively affect satisfaction. Among those factors, fulfilment factor has the strongest impact. Meanwhile, privacy and compensation were found not affecting customer satisfaction. These rmdings could be used to maintain customer loyalty of Go- Food by improving factors influencing satisfaction.
{"title":"Customer Loyalty in Go-Food: The Antecedent of Satisfaction","authors":"Sumarliyanti, P. W. Handayani, Q. Munajat","doi":"10.1109/ICACSIS.2018.8618250","DOIUrl":"https://doi.org/10.1109/ICACSIS.2018.8618250","url":null,"abstract":"This study aims to analyze factors that affect customer satisfaction which will influence customer loyalty in Go-Food, an Online Delivery-Sourcing in Indonesia. Customer loyalty model was based on previous research which includes perceived value and satisfaction aspect. The antecedents of satisfaction were adopted from mobile service quality (M-S-QUAL). To validate the factors, 852 respondents' data were collected. The data were analyzed using Covariance-Based Structural Equation Model (CB-SEM) and processed in AMOS 22.0 tools. Based on the analysis, this study found six antecedents of satisfaction which are efficiency, content, fulfilment, responsiveness, contact, and billing. All the factors positively affect satisfaction. Among those factors, fulfilment factor has the strongest impact. Meanwhile, privacy and compensation were found not affecting customer satisfaction. These rmdings could be used to maintain customer loyalty of Go- Food by improving factors influencing satisfaction.","PeriodicalId":207227,"journal":{"name":"2018 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","volume":"69 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":"121690091","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}
Pub Date : 2018-10-01DOI: 10.1109/ICACSIS.2018.8618181
Tommy Wijaya Sagala, Theresia Wati, Solikin, N. Budi, A. Hidayanto
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.
{"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}
Pub Date : 2018-10-01DOI: 10.1109/icacsis.2018.8618152
{"title":"ICACSIS 2018 Committees","authors":"","doi":"10.1109/icacsis.2018.8618152","DOIUrl":"https://doi.org/10.1109/icacsis.2018.8618152","url":null,"abstract":"","PeriodicalId":207227,"journal":{"name":"2018 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","volume":"52 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":"122876597","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}
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