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2018 International Conference on Computer, Control, Informatics and its Applications (IC3INA)最新文献

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Analysis of Big Data Development in Jakarta Smart City 雅加达智慧城市大数据发展分析
2018 International Conference on Computer, Control, Informatics and its Applications (IC3INA) Pub Date : 2018-11-01 DOI: 10.1109/IC3INA.2018.8629523
Nur Izzuddin, A. S. Arifin
{"title":"Analysis of Big Data Development in Jakarta Smart City","authors":"Nur Izzuddin, A. S. Arifin","doi":"10.1109/IC3INA.2018.8629523","DOIUrl":"https://doi.org/10.1109/IC3INA.2018.8629523","url":null,"abstract":"The Jakarta Smart City Unit is a work unit of the Jakarta Provincial Government in Indonesia with the main duty and function of implementing smart city, one of its efforts using big data technology. The role of big data technology in the development of smart city is very important, because it can be the basis of the steps in determining the policies of the local government. This study discusses and analyses the implementation of big data technology in the Provincial Government of Jakarta by using tool gap analysis based on interview data and supporting data. From the results, it is concluded that the use of big data technology in Jakarta Smart City still requires improvement and development, based on the gap found in the elements of data management, infrastructure, governance, human resources, and data analysis in the big data operations. This study also formulates proposals for improving existing gaps and sets the order of priority of these improvements using the Capability, Accessibility, Readiness, and Leverage (CARL) method.","PeriodicalId":179466,"journal":{"name":"2018 International Conference on Computer, Control, Informatics and its Applications (IC3INA)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122646145","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
Animating DoF Mechanism on Simulation of a Satellite Trajectory around a Planet in Three Celestial Bodies System by IFS Fractal Model 用IFS分形模型模拟三天体系统卫星绕行星轨道的动态自由度
2018 International Conference on Computer, Control, Informatics and its Applications (IC3INA) Pub Date : 2018-11-01 DOI: 10.1109/IC3INA.2018.8629505
Tedjo Darmanto
{"title":"Animating DoF Mechanism on Simulation of a Satellite Trajectory around a Planet in Three Celestial Bodies System by IFS Fractal Model","authors":"Tedjo Darmanto","doi":"10.1109/IC3INA.2018.8629505","DOIUrl":"https://doi.org/10.1109/IC3INA.2018.8629505","url":null,"abstract":"An interesting mechanism of DoF on three celestial bodies system can be simulated and animated by means of animation of non-metamorphic in IFS fractal model. A technique called the shifting centroid technique is used to simplify the complexity of animation method on the DoF mechanism. Another simplification is the use of two kinds of a spiral object as a multi-object to represent the celestial body. The animation to exhibit the satellite trajectory in the system can be accomplished by means of the partitioned-random iteration algorithm","PeriodicalId":179466,"journal":{"name":"2018 International Conference on Computer, Control, Informatics and its Applications (IC3INA)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125010540","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
Speech Emotion Recognition for Indonesian Language Using Long Short-Term Memory 基于长短期记忆的印尼语语音情感识别
2018 International Conference on Computer, Control, Informatics and its Applications (IC3INA) Pub Date : 2018-11-01 DOI: 10.1109/IC3INA.2018.8629525
Jeremia Jason Lasiman, D. Lestari
{"title":"Speech Emotion Recognition for Indonesian Language Using Long Short-Term Memory","authors":"Jeremia Jason Lasiman, D. Lestari","doi":"10.1109/IC3INA.2018.8629525","DOIUrl":"https://doi.org/10.1109/IC3INA.2018.8629525","url":null,"abstract":"This paper presents an extended research of emotion recognition system for Indonesian language. In this research we use Indonesian Emotional Corpus with four emotions classes (anger, contentment, happiness, sadness) and neutral class. As all previous researches for emotion recognition for Indonesian language are using SVM, we are using SVM as baseline. Support Vector Machine (SVM), Feed Forward Neural Network (FFNN) and Long Short-Term Memory (LSTM) are experimented to model emotions. Experiment result shows that LSTM outperform SVM and FFNN. LSTM obtain 65.9% for average F1 measure with using acoustic and lexical feature, making it 5% higher than the best SVM in this experiment.","PeriodicalId":179466,"journal":{"name":"2018 International Conference on Computer, Control, Informatics and its Applications (IC3INA)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132282475","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: 9
Support Vector Machine-based Detection of Pak Choy Leaves Conditions Using RGB and HIS Features 基于RGB和HIS特征的支持向量机白菜叶条件检测
2018 International Conference on Computer, Control, Informatics and its Applications (IC3INA) Pub Date : 2018-11-01 DOI: 10.1109/IC3INA.2018.8629540
A. Ramdan, B. Sugiarto, P. Rianto, E. Prakasa, H. Pardede
{"title":"Support Vector Machine-based Detection of Pak Choy Leaves Conditions Using RGB and HIS Features","authors":"A. Ramdan, B. Sugiarto, P. Rianto, E. Prakasa, H. Pardede","doi":"10.1109/IC3INA.2018.8629540","DOIUrl":"https://doi.org/10.1109/IC3INA.2018.8629540","url":null,"abstract":"Vegetables are good sources to meet the needs for protein, vitamins, minerals for human. One of popular choices for vegetables in Indonesia is Pak Choy (Bassica rapa). Good quality vegetables is usually identified by the color and the shape of the leaves. Therefore an automatic system to detect the quality of the leaves is needed. In this paper, we propose a proper method to detect the quality of the Pak Choy leaves using machine learning. Monitoring the quality of Pak Choy leaves with the naked eye is usually conducted based on the color of the leaves. The healthy leaves are usually characterized by green color while the unhealthy leaves are usually have a green color with the yellow spot. Based on these observations, we develop the system using color intensity features such as RGB and HSI and Support Vector Machine (SVM) as the classifiers. Our system achieves accuracy of 92.5% using linear kernels.","PeriodicalId":179466,"journal":{"name":"2018 International Conference on Computer, Control, Informatics and its Applications (IC3INA)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132228611","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
Unsupervised Convolutional Autoencoder-Based Feature Learning for Automatic Detection of Plant Diseases 基于无监督卷积自编码器的植物病害自动检测特征学习
2018 International Conference on Computer, Control, Informatics and its Applications (IC3INA) Pub Date : 2018-11-01 DOI: 10.1109/IC3INA.2018.8629518
H. Pardede, Endang Suryawati, Rika Sustika, Vicky Zilvan
{"title":"Unsupervised Convolutional Autoencoder-Based Feature Learning for Automatic Detection of Plant Diseases","authors":"H. Pardede, Endang Suryawati, Rika Sustika, Vicky Zilvan","doi":"10.1109/IC3INA.2018.8629518","DOIUrl":"https://doi.org/10.1109/IC3INA.2018.8629518","url":null,"abstract":"Developing an automatic detector of plant diseases is one of application fields in machine learning. Ground-truth diagnoses of plant diseases which are conducted by experts in laboratory tests are often inapplicable for fast and cheap implementations. Using machine learning approaches, the images of leaves or fruits are used as input data. From the data, we design discriminative features that are good for diseases classification. However, finding suitable features from the images are often challenging due to high intra-variability and inter-variability of the data. In this paper, we present an unsupervised feature learning algorithm using the convolutional autoencoder for detection of plant diseases. The use of convolutional autoencoder has two main advantages. First, the use of handcrafted features is not necessary as the network itself may learn to produce discriminative features. Secondly, the procedure is conducted in an unsupervised manner and hence, no labeling of the data are required. Here, we use the output of the autoencoder as inputs to SVM-based classifiers for automatic detection of plant diseases. The method indicates to be better than conventional autoencoder with more hidden layers.","PeriodicalId":179466,"journal":{"name":"2018 International Conference on Computer, Control, Informatics and its Applications (IC3INA)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131610802","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
On Empirical Evaluation of Deep Architectures for Indonesian POS Tagging Problem 印尼语词性标注问题的深度体系结构实证评价
2018 International Conference on Computer, Control, Informatics and its Applications (IC3INA) Pub Date : 2018-11-01 DOI: 10.1109/IC3INA.2018.8629531
R. S. Yuwana, Endang Suryawati, H. Pardede
{"title":"On Empirical Evaluation of Deep Architectures for Indonesian POS Tagging Problem","authors":"R. S. Yuwana, Endang Suryawati, H. Pardede","doi":"10.1109/IC3INA.2018.8629531","DOIUrl":"https://doi.org/10.1109/IC3INA.2018.8629531","url":null,"abstract":"Models with deep architectures have been state-of-the-arts technologies in many natural language problems such as text classification, name entity recognition, language models, and Part-of-Speech (POS) tagging. Usually, the models are trained with large number of data to produce satisfactory results. In Indonesian POS tagging problems, we must deal with small number of data. In this paper, we evaluate models with deep architectures for Indonesian POS tagging problems to find the best structures for Indonesian POS tagging. Models with various number of hidden layers are investigated. We also investigate the effect of adding a regularization method such as dropout on the performance. The experimental results show that the model with 2 hidden layers shows to have better accuracy than the models with deeper structures.","PeriodicalId":179466,"journal":{"name":"2018 International Conference on Computer, Control, Informatics and its Applications (IC3INA)","volume":"156 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115746194","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
Optimal Decision of Reservation Policy for Private Parking Sharing System 私人停车场共享系统预约策略的最优决策
2018 International Conference on Computer, Control, Informatics and its Applications (IC3INA) Pub Date : 2018-11-01 DOI: 10.1109/IC3INA.2018.8629527
S. Chou, Z. Arifin, A. Dewabharata
{"title":"Optimal Decision of Reservation Policy for Private Parking Sharing System","authors":"S. Chou, Z. Arifin, A. Dewabharata","doi":"10.1109/IC3INA.2018.8629527","DOIUrl":"https://doi.org/10.1109/IC3INA.2018.8629527","url":null,"abstract":"Private parking sharing system is one of the alternatives ways in urban areas related to reducing congestion, pollutions and resource dependency. In order to attract people to share their parking lot is by maximizing the revenue. This paper analyzes a reservation policy of revenue management system to maximize revenue in private parking sharing system. The strategy is by developing a policy to accept or reject booking arrival. We propose Expected Revenue Gap as a booking acceptance policy and compare with the First Come First Serve. We also compare the result with the deterministic case after all the demand known to the end of time horizon. We use the deterministic linear programming to solve the upper bound of optimum policy. Based on the simulation result, Expected Revenue Gap can improve the revenue more than First Come First Serve but less than the optimum policy under deterministic linear programming.","PeriodicalId":179466,"journal":{"name":"2018 International Conference on Computer, Control, Informatics and its Applications (IC3INA)","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116049970","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
Neuro-dynamic programming approach to optimal control of spreading of dengue viruses 登革热病毒传播最优控制的神经动态规划方法
2018 International Conference on Computer, Control, Informatics and its Applications (IC3INA) Pub Date : 2018-11-01 DOI: 10.1109/IC3INA.2018.8629508
A. Putri, Hartono
{"title":"Neuro-dynamic programming approach to optimal control of spreading of dengue viruses","authors":"A. Putri, Hartono","doi":"10.1109/IC3INA.2018.8629508","DOIUrl":"https://doi.org/10.1109/IC3INA.2018.8629508","url":null,"abstract":"The purpose of this research is to simulate the effectiveness of temephos and fumigation usage in reducing the growth of Aedes Aegypti as a vector for dengue viruses. The formulated optimization problem is then solved using neuro-dynamic programming method, a combination of artificial neural networks and dynamic programming.","PeriodicalId":179466,"journal":{"name":"2018 International Conference on Computer, Control, Informatics and its Applications (IC3INA)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122833017","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
Analysis of Kidney Ultrasound Images Characterization Using Statistical Moment Descriptor 基于统计矩描述子的肾脏超声图像特征分析
2018 International Conference on Computer, Control, Informatics and its Applications (IC3INA) Pub Date : 2018-11-01 DOI: 10.1109/IC3INA.2018.8629517
W. Ardiatna, A. H. Saputro, D. Soejoko
{"title":"Analysis of Kidney Ultrasound Images Characterization Using Statistical Moment Descriptor","authors":"W. Ardiatna, A. H. Saputro, D. Soejoko","doi":"10.1109/IC3INA.2018.8629517","DOIUrl":"https://doi.org/10.1109/IC3INA.2018.8629517","url":null,"abstract":"Commonly the abnormalities of renal diseases were diagnosed and described using morphological or radiological nomenclature. In distinguishing abnormalities, especially kidney diseases, it is important to characterize the ultrasound images more objectively. This study used The Statistical Moment Descriptor (SMD) with the pixel-level spatial distribution of B-mode ultrasound to analyze three types of the area such as the full kidney, the renal pelvis, and the cortex of fifty kidney ultrasound images. The significant SMD properties that can distinguish the normal from the abnormal ones with 95% confident level are the Mean (p=1.11e-04), the Median (p=0.0051), the Kurtosis (p=0.0053), the Standard Deviation (p=0.0082), and the Entropy (p=0.019) unlike Range (p= 0.091) and Skewness (p=0.1389). Renal pelvis and cortex areas plotting method is unable to distinguish the abnormalities for the CKD on some properties.","PeriodicalId":179466,"journal":{"name":"2018 International Conference on Computer, Control, Informatics and its Applications (IC3INA)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126892982","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
Comparison of Features for Strawberry Grading Classification with Novel Dataset 基于新数据集的草莓分级分类特征比较
2018 International Conference on Computer, Control, Informatics and its Applications (IC3INA) Pub Date : 2018-11-01 DOI: 10.1109/IC3INA.2018.8629534
Oka Mahendra, H. Pardede, Rika Sustika, R. B. S. Kusumo
{"title":"Comparison of Features for Strawberry Grading Classification with Novel Dataset","authors":"Oka Mahendra, H. Pardede, Rika Sustika, R. B. S. Kusumo","doi":"10.1109/IC3INA.2018.8629534","DOIUrl":"https://doi.org/10.1109/IC3INA.2018.8629534","url":null,"abstract":"This paper compares several image processing-based features for Support Vector Machine (SVM) to classify strawberries quality. We use binary class labels: good and damaged strawberries. We collect a new dataset taken from a webcam, consisting of 382 image data for good and 350 data for damaged-strawberries. The images are pre-processed with segmentation, resizing, and padding before their features are extracted. The features are used as inputs for SVM classifier. We evaluate each feature with cross-validation, and then we compared their accuracies. The compared features are Red Green Blue color models (RGB), Hue Saturation Value color model (HSV), RGB histogram, Scale-Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF), Oriented FAST and Rotated BRIEF (ORB), and Histogram of Oriented Gradients (HOG). The experimental results showed that SURF achieved the best accuracy. The dataset is published by the authors as a free GNU General Public License so that readers can do further research, either with a combination of existing features or research their own features to produce better accuracy.","PeriodicalId":179466,"journal":{"name":"2018 International Conference on Computer, Control, Informatics and its Applications (IC3INA)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125663779","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: 13
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