2016 International Conference on Advanced Computer Science and Information Systems (ICACSIS)最新文献

What features of online review affects readers' intention to travel? 在线评论的哪些特点会影响读者的旅游意愿?

2016 International Conference on Advanced Computer Science and Information Systems (ICACSIS) Pub Date : 2016-10-01 DOI: 10.1109/ICACSIS.2016.7872786

Rahmi Johanes, A. Pinem, A. Hidayanto, M. R. Shihab

{"title":"What features of online review affects readers' intention to travel?","authors":"Rahmi Johanes, A. Pinem, A. Hidayanto, M. R. Shihab","doi":"10.1109/ICACSIS.2016.7872786","DOIUrl":"https://doi.org/10.1109/ICACSIS.2016.7872786","url":null,"abstract":"Online review is becoming more popular among the public when considering the purchase of a product or service. This also applies in tourism industry when potential tourists were making decisions about travel destination. This research aims to identify online review elements/features that will affect readers' intention to travel. Hence, the model was designed by categorized online review characteristics namely system, information and informant characteristic. These characteristics then influence perceived informativeness, destination image and informant expertise and trustworthiness viewed in reader viewpoint and eventually affect reader intention to travel. Analysis of the model was conducted by using PLS SEM with 189 valid questionnaires. The result of this study proved that some elements/features of online review influence reader intention to travel. They are quality and quantity of the reviews and destination indicator which are available on the website. However, this study suggest that current features are not sufficient to explain reviewer credibility, thus other features need to be added to the website to serve this purpose.","PeriodicalId":267924,"journal":{"name":"2016 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122618746","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

The use of data mining classification technique to fill in structural positions in bogor local government 利用数据挖掘分类技术对茂物地方政府的结构性职位进行填充

2016 International Conference on Advanced Computer Science and Information Systems (ICACSIS) Pub Date : 2016-10-01 DOI: 10.1109/ICACSIS.2016.7872797

Tosan Wiar Ramdhani, B. Purwandari, Y. Ruldeviyani

{"title":"The use of data mining classification technique to fill in structural positions in bogor local government","authors":"Tosan Wiar Ramdhani, B. Purwandari, Y. Ruldeviyani","doi":"10.1109/ICACSIS.2016.7872797","DOIUrl":"https://doi.org/10.1109/ICACSIS.2016.7872797","url":null,"abstract":"The human resources of Bogor local government are managed by human resources and training division, which is called Badan Kepegawaian Pendidikan dan Pelatihan (BKPP). BKPP form a team called Badan Pertimbangan Jabatan dan Kepangkatan (Baperjakat), which are responsible for promoting, rotating and dismissing local government employees from structural positions below the Echelon IIA positions. Baperjakat have problems on constructing the draft of structural government positions. These processes were done manually, even though BKPP have a human resources information systems called SIMPEG. The main purpose of this research is to identify patterns to fill in structural positions in Bogor Local Government. 62 Classifications algortithms were tested using 3 data mining tools with 7 data sets and 7 human resources attributes to identify filling structural position patterns. The classification process yields Classification Rule with Unbiased Interaction Selection and Estimation (CRUISE) as the best algorithm in echelon class. Its average accuracy is 95.7% for each echelon level.","PeriodicalId":267924,"journal":{"name":"2016 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122658193","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

A water flow algorithm based optimization model for road traffic engineering 基于水流算法的道路交通工程优化模型

2016 International Conference on Advanced Computer Science and Information Systems (ICACSIS) Pub Date : 2016-10-01 DOI: 10.1109/ICACSIS.2016.7872734

D. N. Utama, F. A. Zaki, I. J. Munjeri, N. Putri

引用次数: 12

Using logistic regression method to classify tweets into the selected topics 使用逻辑回归方法将推文分类为选定的主题

2016 International Conference on Advanced Computer Science and Information Systems (ICACSIS) Pub Date : 2016-10-01 DOI: 10.1109/ICACSIS.2016.7872727

S. Indra, Liza Wikarsa, Mcomp Bcs, Rinaldo Turang

{"title":"Using logistic regression method to classify tweets into the selected topics","authors":"S. Indra, Liza Wikarsa, Mcomp Bcs, Rinaldo Turang","doi":"10.1109/ICACSIS.2016.7872727","DOIUrl":"https://doi.org/10.1109/ICACSIS.2016.7872727","url":null,"abstract":"Topics about health, music, sport, and technology are widely discussed in social network sites, especially in Twitter. Sharing information about those topics can enrich one's knowledge as well as increase the awareness of the current trends pertinent to the area of interests. Hence, this research aims to develop a web-based application that can classify tweets of netizens into these four categories of topics using one of machine learning methods called Logistic Regression. There are four main processes applied in this application that are fetching tweets, preprocessing, text feature extraction and machine learning. There are 1800 labeled tweets for each topic used as training data. Several processes were done in the pre-processing phase, including removal of URLs, punctuation, and stop words, tokenization, and stemming. Later, the application automatically converted the pre-processed tweets into set of features vector using Bag of Words. The set of features vector was applied to the Logistic Regression algorithm for the classification task. The trained classifier was then evaluated using 1800 tweets with 450 for each topic. Using Confusion Matrix, the results showed the accuracy of tweets classification into the selected topics is 92% which is considered very high.","PeriodicalId":267924,"journal":{"name":"2016 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123984068","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: 35

Road detection analysis based on corner adjacent features 基于拐角相邻特征的道路检测分析

2016 International Conference on Advanced Computer Science and Information Systems (ICACSIS) Pub Date : 2016-10-01 DOI: 10.1109/ICACSIS.2016.7892513

M.D. Enjat Munaja, D. H. Widyantoro, R. Munir

引用次数: 0

DVB-T signal analysis on passive coherent location system in single frequency network 单频网络中无源相干定位系统DVB-T信号分析

2016 International Conference on Advanced Computer Science and Information Systems (ICACSIS) Pub Date : 2016-10-01 DOI: 10.1109/ICACSIS.2016.7872719

Karel Juryca, J. Pidanic, Z. Nemec, H. Suhartanto

引用次数: 0

Intention to use smart city system based on social cognitive theory 意图使用基于社会认知理论的智慧城市系统

2016 International Conference on Advanced Computer Science and Information Systems (ICACSIS) Pub Date : 2016-10-01 DOI: 10.1109/ICACSIS.2016.7872747

W. R. Fitriani, Ikut Tri Handoyo, Puji Rahayu, D. I. Sensuse

引用次数: 2

Fetal head segmentation based on Gaussian elliptical path optimize by flower pollination algorithm and cuckoo search 基于高斯椭圆路径优化的胎头分割，采用传粉算法和布谷鸟搜索

2016 International Conference on Advanced Computer Science and Information Systems (ICACSIS) Pub Date : 2016-10-01 DOI: 10.1109/ICACSIS.2016.7872804

Ilham Kusuma, M. A. Ma'sum, H. Sanabila, H. Wisesa, W. Jatmiko, A. M. Arymurthy, B. Wiweko

{"title":"Fetal head segmentation based on Gaussian elliptical path optimize by flower pollination algorithm and cuckoo search","authors":"Ilham Kusuma, M. A. Ma'sum, H. Sanabila, H. Wisesa, W. Jatmiko, A. M. Arymurthy, B. Wiweko","doi":"10.1109/ICACSIS.2016.7872804","DOIUrl":"https://doi.org/10.1109/ICACSIS.2016.7872804","url":null,"abstract":"Number of maternal and infant mortality in Indonesia is high. This problem can be minimized by monitoring the fetal condition via ultrasound image. In addition, Indonesia have small number of obstetrics and gynecology compare to number of its population. Moreover, it is centralized in urban areas, so it is hard to monitor the condition of every babies in Indonesia. In order to resolve this problem, we have built fetal head monitoring system. Part of the system is to segment the fetal head in ultrasound image. In this paper, we examine nature optimization such as bat algorithm, cuckoo search, and flower pollination algorithm for optimizing Gaussian elliptical path for automatic fetal head segmentation. Experiment results shows that nature optimization Based Gaussian elliptical path (DoGEII-FPA and DoGEII-CS) has a minimum error compared to Gaussian elliptical path (DoGEll) which is optimized by Nelder-Mead. Interestingly, DoGEll-FPA and DoGEll-CS perform well from DoGEll-NM in different image.","PeriodicalId":267924,"journal":{"name":"2016 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127692852","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

Vehicle traffic monitoring using single camera and embedded systems 车辆交通监控采用单摄像头和嵌入式系统

2016 International Conference on Advanced Computer Science and Information Systems (ICACSIS) Pub Date : 2016-10-01 DOI: 10.1109/ICACSIS.2016.7872806

Rindra Wiska, M. Alhamidi, Novian Habibie, A. Wibisono, P. Mursanto, D. H. Ramdhan, M. F. Rachmadi, W. Jatmiko

引用次数: 3

Optimization of convolutional neural network using microcanonical annealing algorithm 基于微规范退火算法的卷积神经网络优化

2016 International Conference on Advanced Computer Science and Information Systems (ICACSIS) Pub Date : 2016-10-01 DOI: 10.1109/ICACSIS.2016.7872787

Vina Ayumi, L. M. R. Rere, M. I. Fanany, Aniati Murni Arymurthy

{"title":"Optimization of convolutional neural network using microcanonical annealing algorithm","authors":"Vina Ayumi, L. M. R. Rere, M. I. Fanany, Aniati Murni Arymurthy","doi":"10.1109/ICACSIS.2016.7872787","DOIUrl":"https://doi.org/10.1109/ICACSIS.2016.7872787","url":null,"abstract":"Convolutional neural network (CNN) is one of the most prominent architectures and algorithm in Deep Learning. It shows a remarkable improvement in the recognition and classification of objects. This method has also been proven to be very effective in a variety of computer vision and machine learning. As in other deep learning, however, training this approach is interesting yet challenging. Recently, some metaheuristic algorithms have been used to optimize CNN using Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing and Harmony Search. In this paper, another type of metaheuristic algorithms with different strategy has been proposed, i.e. Microcanonical Annealing to optimize Convolutional Neural Network. The performance of the proposed method is tested using the MNIST and CIFAR-10 datasets. Although experiment results of MNIST dataset indicate the increase in computation time (1.02x–1.38x), nevertheless this proposed method can considerably enhance the performance of the original CNN (up to 4.60%). On the CIFAR10 dataset, currently, state of the art is 96.53% using fractional pooling, while this proposed method achieves 99.14%.","PeriodicalId":267924,"journal":{"name":"2016 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115242084","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: 50