Pub Date : 2019-12-01DOI: 10.1109/ISRITI48646.2019.9034641
Tiara Putri Ananda, W. Budianto, Ibnu Fajar Alam, Gunawan Wang
The study aims to know the application of Knowledge Management in national utility company, PT. PLN. Data collection is done through conducting interviews to several stakeholders / employees of PT. PLN that has retired and some still active. The data collection method uses focus group discussion, where it examines a two-way communication between director and employees to discuss the effective way to improve corporate performance. The findings are developed and selected idea are gathered in a special portal for knowledge sharing activities or knowledge of company. It is known as Knowledge Management System (KMS). The article addresses several important factors that leads to implementation and enables to change productivity and increase competitive advantage.
{"title":"Effective Use of the Knowledge Management System in Improving Organizational Performance (Case Study in National Energy Company)","authors":"Tiara Putri Ananda, W. Budianto, Ibnu Fajar Alam, Gunawan Wang","doi":"10.1109/ISRITI48646.2019.9034641","DOIUrl":"https://doi.org/10.1109/ISRITI48646.2019.9034641","url":null,"abstract":"The study aims to know the application of Knowledge Management in national utility company, PT. PLN. Data collection is done through conducting interviews to several stakeholders / employees of PT. PLN that has retired and some still active. The data collection method uses focus group discussion, where it examines a two-way communication between director and employees to discuss the effective way to improve corporate performance. The findings are developed and selected idea are gathered in a special portal for knowledge sharing activities or knowledge of company. It is known as Knowledge Management System (KMS). The article addresses several important factors that leads to implementation and enables to change productivity and increase competitive advantage.","PeriodicalId":367363,"journal":{"name":"2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120975386","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}
Hate speeches are defined as utterances, writings, actions, or performances that are intended to incite violence or prejudice against a person on the basis of the characteristics of a particular group that he or she is representing, such as race, ethnicity. In this study, we built a hate speech classification model using word representation with continous bag of words (CBOW) and fastText algorithm. This algorithms was chosen, because it is able to achieve a good performance, specially in the case of rare words by making use of character level information. Based on this result, we can see that there is no single, universal variations that outperform other. But in general, models that use pre-trained vectors from Wiki outperform models that do not use pre-trained vectors.
{"title":"Hate Speech and Abusive Language Classification using fastText","authors":"Guntur Budi Herwanto, Annisa Maulida Ningtyas, Kurniawan Eka Nugraha, I. Nyoman Prayana Trisna","doi":"10.1109/ISRITI48646.2019.9034560","DOIUrl":"https://doi.org/10.1109/ISRITI48646.2019.9034560","url":null,"abstract":"Hate speeches are defined as utterances, writings, actions, or performances that are intended to incite violence or prejudice against a person on the basis of the characteristics of a particular group that he or she is representing, such as race, ethnicity. In this study, we built a hate speech classification model using word representation with continous bag of words (CBOW) and fastText algorithm. This algorithms was chosen, because it is able to achieve a good performance, specially in the case of rare words by making use of character level information. Based on this result, we can see that there is no single, universal variations that outperform other. But in general, models that use pre-trained vectors from Wiki outperform models that do not use pre-trained vectors.","PeriodicalId":367363,"journal":{"name":"2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127497002","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 : 2019-12-01DOI: 10.1109/isriti48646.2019.9034558
{"title":"ISRITI 2019 Author Index","authors":"","doi":"10.1109/isriti48646.2019.9034558","DOIUrl":"https://doi.org/10.1109/isriti48646.2019.9034558","url":null,"abstract":"","PeriodicalId":367363,"journal":{"name":"2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115261796","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 : 2019-12-01DOI: 10.1109/ISRITI48646.2019.9034585
Meizar Raka Rimadana, S. Kusumawardani, P. Santosa, Maximillian Sheldy Ferdinand Erwianda
Prediction of student academic performance is an important aspect in the learning process. This study applies several machine learning models in predicting student academic performance using Time Management Skills data obtained from Time Structure Questionnaire (TSQ). Previously, some other data has been used as a feature in making predictions, but TSQ result had never been used before as a feature, even though it may shows the conditions of how students use their time in learning. Five different machine learning models were trained using TSQ data to predict student academic performance. In addition, student English performance is also predicted in the same way as a comparison As a result, the Linear Support Vector Machine model can predict student academic performance with 80% accuracy and English performance with 84% accuracy using TSQ data.
{"title":"Predicting Student Academic Performance using Machine Learning and Time Management Skill Data","authors":"Meizar Raka Rimadana, S. Kusumawardani, P. Santosa, Maximillian Sheldy Ferdinand Erwianda","doi":"10.1109/ISRITI48646.2019.9034585","DOIUrl":"https://doi.org/10.1109/ISRITI48646.2019.9034585","url":null,"abstract":"Prediction of student academic performance is an important aspect in the learning process. This study applies several machine learning models in predicting student academic performance using Time Management Skills data obtained from Time Structure Questionnaire (TSQ). Previously, some other data has been used as a feature in making predictions, but TSQ result had never been used before as a feature, even though it may shows the conditions of how students use their time in learning. Five different machine learning models were trained using TSQ data to predict student academic performance. In addition, student English performance is also predicted in the same way as a comparison As a result, the Linear Support Vector Machine model can predict student academic performance with 80% accuracy and English performance with 84% accuracy using TSQ data.","PeriodicalId":367363,"journal":{"name":"2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"159 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123021949","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 : 2019-12-01DOI: 10.1109/ISRITI48646.2019.9034672
Yuliana Setiowati, A. Djunaidy, D. Siahaan
In implicit opinion sentences containing implicit opinions, the use of traditional dictionary-based method is no longer effective, since there is no any opinion word serving as a clue to identify the sentiment value. This study proposes an approach to obtain pairs of aspect and opinion word from sentences containing implicit opinion. Two methods are developed this approach. The first method serves to separate clauses from a compound implicit opinion sentence and refine its corresponding parse-tree from clauses as a result of the separation. The second method determines the opinion word from the implicit opinion clause. This method uses co-occurrence of aspect and opinion word based on a corpus of explicit opinion sentences. In this study, an initial experiment was conducted using a data set containing 30 implicit opinion sentences with 76 clauses. The proposed approach is capable of detecting pairs of aspect and opinion from the given corpus of Bahasa Indonesia.
{"title":"Pair Extraction of Aspect and Implicit Opinion Word based on its Co-occurrence in Corpus of Bahasa Indonesia","authors":"Yuliana Setiowati, A. Djunaidy, D. Siahaan","doi":"10.1109/ISRITI48646.2019.9034672","DOIUrl":"https://doi.org/10.1109/ISRITI48646.2019.9034672","url":null,"abstract":"In implicit opinion sentences containing implicit opinions, the use of traditional dictionary-based method is no longer effective, since there is no any opinion word serving as a clue to identify the sentiment value. This study proposes an approach to obtain pairs of aspect and opinion word from sentences containing implicit opinion. Two methods are developed this approach. The first method serves to separate clauses from a compound implicit opinion sentence and refine its corresponding parse-tree from clauses as a result of the separation. The second method determines the opinion word from the implicit opinion clause. This method uses co-occurrence of aspect and opinion word based on a corpus of explicit opinion sentences. In this study, an initial experiment was conducted using a data set containing 30 implicit opinion sentences with 76 clauses. The proposed approach is capable of detecting pairs of aspect and opinion from the given corpus of Bahasa Indonesia.","PeriodicalId":367363,"journal":{"name":"2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114301653","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 : 2019-12-01DOI: 10.1109/ISRITI48646.2019.9034624
Arief Budhiman, S. Suyanto, A. Arifianto
Melanoma skin cancer is cancer that difficult to detect. In this study, have been done melanoma cancer classification using Convolutional Neural Network (CNN). CNN is a class of Deep Neural Network (Deep Learning) and commonly used to analyzing images data. A lot of data used on CNN can greatly affect accuracy. In this study, the objective is to get best ResNet model for classifying melanoma cancer and normal skin images. The dataset that used is ISIC 2018. ResNet is used because the model winning the ILSVRC competition at 2015. ResNet architecture model that used are ResNet 50, 40, 25, 10 and 7 models. The architecture trained using data train that has been augmented and undersampling. The validation result on each model calculated using F1 Score. After validation and F1 Score result from the model obtained, the result compared each other to select the best model. The best architecture is ResNet 50 without augmentation that gives a validation accuracy of 0.83 and f1 score of 0.46.
黑色素瘤皮肤癌是一种难以发现的癌症。在本研究中，利用卷积神经网络(CNN)对黑色素瘤进行了癌症分类。CNN是深度神经网络(Deep Neural Network, Deep Learning)的一类，通常用于分析图像数据。CNN上使用的大量数据会极大地影响准确性。本研究的目的是获得最佳的ResNet模型用于黑色素瘤癌和正常皮肤图像的分类。使用的数据集是ISIC 2018。使用ResNet是因为该模型在2015年的ILSVRC竞赛中获胜。使用的ResNet架构模型有ResNet 50、40、25、10和7模型。该体系结构使用增强和欠采样的数据训练。使用F1 Score计算的每个模型的验证结果。验证后与模型得到的F1评分结果进行比较，选择最优模型。最好的体系结构是没有增强的ResNet 50，它的验证精度为0.83,f1分数为0.46。
{"title":"Melanoma Cancer Classification Using ResNet with Data Augmentation","authors":"Arief Budhiman, S. Suyanto, A. Arifianto","doi":"10.1109/ISRITI48646.2019.9034624","DOIUrl":"https://doi.org/10.1109/ISRITI48646.2019.9034624","url":null,"abstract":"Melanoma skin cancer is cancer that difficult to detect. In this study, have been done melanoma cancer classification using Convolutional Neural Network (CNN). CNN is a class of Deep Neural Network (Deep Learning) and commonly used to analyzing images data. A lot of data used on CNN can greatly affect accuracy. In this study, the objective is to get best ResNet model for classifying melanoma cancer and normal skin images. The dataset that used is ISIC 2018. ResNet is used because the model winning the ILSVRC competition at 2015. ResNet architecture model that used are ResNet 50, 40, 25, 10 and 7 models. The architecture trained using data train that has been augmented and undersampling. The validation result on each model calculated using F1 Score. After validation and F1 Score result from the model obtained, the result compared each other to select the best model. The best architecture is ResNet 50 without augmentation that gives a validation accuracy of 0.83 and f1 score of 0.46.","PeriodicalId":367363,"journal":{"name":"2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122176019","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 : 2019-12-01DOI: 10.1109/ISRITI48646.2019.9034601
Maghfirah Dinsyah Febriana, Z. Zainuddin, I. Nurtanio
The process of admitting High School Students in Makassar City produces a lot of student data, in the form of student learning activities data and also student profile data. This affects the search for information on the data. This study discusses the grouping of students towards Makassar City Public High Schools by utilizing the data mining process using clustering techniques. The algorithm used for cluster formation is the K-Means algorithm. K-Means is a nonhierarchical data clustering method that can group school data into several clusters based on the similarity of the data. Euclidean Distance is used to determine the distance of school points and address points for students. The proposed system is a zoning area determination system for acceptance of high school students on a noncircle basis using student data and school data. The data used are 22 school data and 1547 student data. The results of this study are used as a basis for decision making to determine optimal school zoning so that student distribution is evenly distributed based on the cluster formed. The aim is so that the data distribution does not overlap for schools that are close together so that schools that have the closest distance are grouped in one cluster.
{"title":"School zoning system using K-Means algorithm for high school students in Makassar City","authors":"Maghfirah Dinsyah Febriana, Z. Zainuddin, I. Nurtanio","doi":"10.1109/ISRITI48646.2019.9034601","DOIUrl":"https://doi.org/10.1109/ISRITI48646.2019.9034601","url":null,"abstract":"The process of admitting High School Students in Makassar City produces a lot of student data, in the form of student learning activities data and also student profile data. This affects the search for information on the data. This study discusses the grouping of students towards Makassar City Public High Schools by utilizing the data mining process using clustering techniques. The algorithm used for cluster formation is the K-Means algorithm. K-Means is a nonhierarchical data clustering method that can group school data into several clusters based on the similarity of the data. Euclidean Distance is used to determine the distance of school points and address points for students. The proposed system is a zoning area determination system for acceptance of high school students on a noncircle basis using student data and school data. The data used are 22 school data and 1547 student data. The results of this study are used as a basis for decision making to determine optimal school zoning so that student distribution is evenly distributed based on the cluster formed. The aim is so that the data distribution does not overlap for schools that are close together so that schools that have the closest distance are grouped in one cluster.","PeriodicalId":367363,"journal":{"name":"2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129166319","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 : 2019-12-01DOI: 10.1109/ISRITI48646.2019.9034664
Raya Rahadian, S. Suyanto
One of the challenges in computer vision is age classification. There have been many methods used to classify someone age from the image of their faces. Convolutional neural network (CNN) gives a high accuracy but it cannot be used on many layers. Therefore, a residual technique is applied on convolutional neural network then named residual neural network. In this paper, some Residual Networks are applied to develop an age classification with face image using the Adience dataset that has 19,370 face images from 2,284 individuals grouped into eight categories: 0-2, 4-6, 8-13, 15-20, 25-32, 38-43, 48-53, and 60-100 years. Three techniques: cyclical learning rate, data augmentation, and transfer learning are observed. Six training scenarios are performed to select the best model. Experimental results show that Resnet34 is the best model with an average F1 score of 0.792 that is achieved by data augmentation, transfer learning, and trained on the image with size 224 x 224 pixels.
{"title":"Deep Residual Neural Network for Age Classification with Face Image","authors":"Raya Rahadian, S. Suyanto","doi":"10.1109/ISRITI48646.2019.9034664","DOIUrl":"https://doi.org/10.1109/ISRITI48646.2019.9034664","url":null,"abstract":"One of the challenges in computer vision is age classification. There have been many methods used to classify someone age from the image of their faces. Convolutional neural network (CNN) gives a high accuracy but it cannot be used on many layers. Therefore, a residual technique is applied on convolutional neural network then named residual neural network. In this paper, some Residual Networks are applied to develop an age classification with face image using the Adience dataset that has 19,370 face images from 2,284 individuals grouped into eight categories: 0-2, 4-6, 8-13, 15-20, 25-32, 38-43, 48-53, and 60-100 years. Three techniques: cyclical learning rate, data augmentation, and transfer learning are observed. Six training scenarios are performed to select the best model. Experimental results show that Resnet34 is the best model with an average F1 score of 0.792 that is achieved by data augmentation, transfer learning, and trained on the image with size 224 x 224 pixels.","PeriodicalId":367363,"journal":{"name":"2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129468683","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 : 2019-12-01DOI: 10.1109/ISRITI48646.2019.9034570
Lasmadi Lasmadi, Freddy Kurniawan, Denny Dermawan, G. Pratama
Mobile robot localization concerns estimating the position and heading of the robot relative to its environment. Basically, the mobile robot moves around without initial knowledge of the environment. Therefore, a scheme to handle it is necessary, such as the Kalman Filters. Rather than the Extended Kalman Filter, we choose to employ the sigma points approach. In this paper, we take into consideration the method proposed by Van Der Merwe to determine the sigma points in Unscented Kalman Filter. The simulation and results verify that the Unscented Kalman Filter works pretty well for locating the mobile robot.
移动机器人定位涉及估计机器人相对于其环境的位置和方向。基本上，移动机器人在没有环境初始知识的情况下四处移动。因此，需要一种方案来处理它，如卡尔曼滤波器。而不是扩展卡尔曼滤波器，我们选择采用西格玛点的方法。本文考虑了Van Der Merwe提出的确定Unscented卡尔曼滤波器中sigma点的方法。仿真和结果验证了Unscented卡尔曼滤波对移动机器人定位的良好效果。
{"title":"Mobile Robot Localization via Unscented Kalman Filter","authors":"Lasmadi Lasmadi, Freddy Kurniawan, Denny Dermawan, G. Pratama","doi":"10.1109/ISRITI48646.2019.9034570","DOIUrl":"https://doi.org/10.1109/ISRITI48646.2019.9034570","url":null,"abstract":"Mobile robot localization concerns estimating the position and heading of the robot relative to its environment. Basically, the mobile robot moves around without initial knowledge of the environment. Therefore, a scheme to handle it is necessary, such as the Kalman Filters. Rather than the Extended Kalman Filter, we choose to employ the sigma points approach. In this paper, we take into consideration the method proposed by Van Der Merwe to determine the sigma points in Unscented Kalman Filter. The simulation and results verify that the Unscented Kalman Filter works pretty well for locating the mobile robot.","PeriodicalId":367363,"journal":{"name":"2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129109794","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 : 2019-12-01DOI: 10.1109/ISRITI48646.2019.9034613
Marcel
Storage was one of the crucial components that have a big impact on overall system performance, especially in a virtualization environment. The application of cache as storage performance accelerator was an option especially for small scale environments with limited resources and the usage of DAS-based storage scenarios (Direct-Attached Storage). Maximum performance obtained when the cache function was applied to both read and write operations (at write-back mode), but there was a limitation for RAM-based cache implementation on the DAS-based storage scenario with VMware-based platform which currently only supports write- through mode (acceleration was only for read operations). This paper tried to propose an alternative solution using a virtual NAS approach to apply RAM-based cache that runs in writeback mode at VMware platform on DAS-based storage scenario. Performance test performed using the workload simulation tool for single-cache and multi-cache implementation scenarios. The test results showed a significant performance improvement for read and write operations compared to baseline (without cache condition) within the scope of the workload simulation being performed. A single cache implementation scenario, the improvement range for read operation was between 27.71x - 98.02x, while write operation was between 21.94x - 63.21x better than baseline. In a multi-cache implementation scenario, the range of performance improvement for reading operations was between 110.37x - 431.43x, whereas for write operations it was in the range of 23.14x - 391.76x better than baseline.
{"title":"Implementation of RAM-based Cache at Write-back Mode Using Virtual-NAS for DAS-based Storage on VMware Platform","authors":"Marcel","doi":"10.1109/ISRITI48646.2019.9034613","DOIUrl":"https://doi.org/10.1109/ISRITI48646.2019.9034613","url":null,"abstract":"Storage was one of the crucial components that have a big impact on overall system performance, especially in a virtualization environment. The application of cache as storage performance accelerator was an option especially for small scale environments with limited resources and the usage of DAS-based storage scenarios (Direct-Attached Storage). Maximum performance obtained when the cache function was applied to both read and write operations (at write-back mode), but there was a limitation for RAM-based cache implementation on the DAS-based storage scenario with VMware-based platform which currently only supports write- through mode (acceleration was only for read operations). This paper tried to propose an alternative solution using a virtual NAS approach to apply RAM-based cache that runs in writeback mode at VMware platform on DAS-based storage scenario. Performance test performed using the workload simulation tool for single-cache and multi-cache implementation scenarios. The test results showed a significant performance improvement for read and write operations compared to baseline (without cache condition) within the scope of the workload simulation being performed. A single cache implementation scenario, the improvement range for read operation was between 27.71x - 98.02x, while write operation was between 21.94x - 63.21x better than baseline. In a multi-cache implementation scenario, the range of performance improvement for reading operations was between 110.37x - 431.43x, whereas for write operations it was in the range of 23.14x - 391.76x better than baseline.","PeriodicalId":367363,"journal":{"name":"2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116335779","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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