2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC)最新文献_第2页

Deep learning algorithm for arrhythmia detection 心律失常检测的深度学习算法

2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC) Pub Date : 2017-09-01 DOI: 10.1109/KCIC.2017.8228452

Hilmy Assodiky, I. Syarif, T. Badriyah

引用次数: 11

Fault diagnosis system of rotating machines using continuous wavelet transform and Artificial Neural Network 基于连续小波变换和人工神经网络的旋转机械故障诊断系统

2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC) Pub Date : 2017-09-01 DOI: 10.1109/KCIC.2017.8228582

Nur Ashar Aditiya, Zaqiatud Darojah, D. Sanggar, Muhammad Rizky Dharmawan

{"title":"Fault diagnosis system of rotating machines using continuous wavelet transform and Artificial Neural Network","authors":"Nur Ashar Aditiya, Zaqiatud Darojah, D. Sanggar, Muhammad Rizky Dharmawan","doi":"10.1109/KCIC.2017.8228582","DOIUrl":"https://doi.org/10.1109/KCIC.2017.8228582","url":null,"abstract":"In this paper using a machine with a motor configuration that is connected with 3 discs. Performance of a machine can be known by analyzing the vibrations that occur in the machine. Vibration that occurs on the machine may be normal or abnormal. Abnormal vibrations on a machine can cause severe damage. This abnormal vibration can be caused by the mass distribution of rotation no longer exists in the centerline. This technique of identifying vibrations can use a combination of Continuous Wavelet Transform (CWT) and Artificial Neural Network (ANN) methods. The vibration signal is sampled to be transformed using CWT, so the data of Continuous Wavelet Coefficient (CWC) is obtained. The Feature Extraction method is used to extract the Continuous Wavelet Transform data into several types. Root Mean Square (RMS), Kurtosis, and Power Spectrum Density (PSD) are Feature Extraction types used as Artificial Neural Network inputs to identify abnormal vibrations in the machine. The Artificial Neural Network (ANN) intelligently classifies the fault from machine vibrations. CWT and ANN combinations are able to classify the damage by 99.72% accuracy.","PeriodicalId":117148,"journal":{"name":"2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC)","volume":"2008 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125606442","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

An implementation of Botnet dataset to predict accuracy based on network flow model 基于网络流模型的僵尸网络数据集预测精度的实现

2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC) Pub Date : 2017-09-01 DOI: 10.1109/KCIC.2017.8228455

Yesta Medya Mahardhika, Amang Sudarsono, Ali Ridho Barakbah

{"title":"An implementation of Botnet dataset to predict accuracy based on network flow model","authors":"Yesta Medya Mahardhika, Amang Sudarsono, Ali Ridho Barakbah","doi":"10.1109/KCIC.2017.8228455","DOIUrl":"https://doi.org/10.1109/KCIC.2017.8228455","url":null,"abstract":"Botnet is a malicious software that can perform malicious activities, such as (Distributed Denial of Services) DDoS, spamming, phishing, key logging, click fraud, steal personal information and important data, etc. Botnets can replicate themselves without user consent. Several systems of botnet detection have been done by using a machine learning method with feature selection approach. Currently, the creation of dataset feature based on network flow, Domain Name System (DNS) traffic and content based that represent botnet behavior. Unfortunately the dataset for botnet detection is dummy dataset, to implement in machine learning needs extractor tool which is very expensive to buy. Therefore we create our own features extractor. In this paper we propose network flow using connection logs approach on the dataset. First of all we made the data model using pair of source IP (Internet Protocol), destination IP and source port, destination port in a period time to extract new features. To predict the accuracy, the extracted features will be validated using K-Fold Cross Validation with number of k= 10. The results of the validation with six various types of botnet shows the high Precision=98.70%, F-Measure=99.40%, Recall=98.80%, and Accuracy=98.80% for Rule Induction algorithm, while K-Nearest Neighbor is the most stable than all algorithms that achieve precision, Recall, F-measure and accuracy to 98.10% and high speed (50 ms).","PeriodicalId":117148,"journal":{"name":"2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC)","volume":"133 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133921177","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 training of feedforward neural network using the unscented Kalman filter for voice classification application 基于无气味卡尔曼滤波的前馈神经网络训练在语音分类中的应用

2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC) Pub Date : 2017-09-01 DOI: 10.1109/KCIC.2017.8228451

Zaqiatud Darojah, E. S. Ningrum, D. Purnomo

引用次数: 5

Association analysis of earthquake distribution in Indonesia for spatial risk mapping 印度尼西亚地震分布的关联分析用于空间风险制图

2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC) Pub Date : 2017-09-01 DOI: 10.1109/KCIC.2017.8228592

Renovita Edelani, Ali Ridho Barakbah, T. Harsono, Amang Sudarsono

{"title":"Association analysis of earthquake distribution in Indonesia for spatial risk mapping","authors":"Renovita Edelani, Ali Ridho Barakbah, T. Harsono, Amang Sudarsono","doi":"10.1109/KCIC.2017.8228592","DOIUrl":"https://doi.org/10.1109/KCIC.2017.8228592","url":null,"abstract":"Indonesia is an earthquake-prone country which surrounded by tectonic plate boundary and ring of fire area. In 2016, there are 14 times of earthquake rates ≥ 5 Richter on average per month occurred in Indonesia. Because the high rates of earthquake in Indonesia connected in earthquake tectonic plate boundary, it is important to analyze a causal-effect relationship between earthquake hit in several regions. This paper proposes a new system for causal-effect relationship analysis of earthquake data distribution in Indonesia. This system presented an automatic spatio-temporal cluster-based earthquake data distribution and developed an association-mining of the data projected to provinces for risk-mapping of region in Indonesia. The system has 3 main features: (1) Subspace Earthquake Data Selection, (2) Automatic Spatio-Temporal Clustering, (3) Association Mining for Earthquake Data Distribution, (4) Causal-Effect Relationship Visualization, and (5) Risk-Mapping Earthquake Analysis. We applied our system with seismic data in Indonesia taken from 1963–2016. The results of our experiment was found the interesting patterns relation from the association of earthquake distribution in Indonesia. Provinces with strong relation are Maluku, North Maluku and North Sulawesi that always appear as a rule in every experiments period and give each other the risk of the earthquake.","PeriodicalId":117148,"journal":{"name":"2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124065699","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

Spatial analisys of magnitude distribution for earthquake prediction using neural network based on automatic clustering in Indonesia 基于自动聚类的神经网络地震预测震级分布空间分析

2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC) Pub Date : 2017-09-01 DOI: 10.1109/KCIC.2017.8228594

M. Shodiq, D. Kusuma, M. Rifqi, Ali Ridho Barakbah, T. Harsono

{"title":"Spatial analisys of magnitude distribution for earthquake prediction using neural network based on automatic clustering in Indonesia","authors":"M. Shodiq, D. Kusuma, M. Rifqi, Ali Ridho Barakbah, T. Harsono","doi":"10.1109/KCIC.2017.8228594","DOIUrl":"https://doi.org/10.1109/KCIC.2017.8228594","url":null,"abstract":"A spatial analysis of magnitude distribution is presented in this paper to identify the optimal number of clusters based on seismic data of all region in Indonesia. The data were obtained from Indonesian Agency for Meteorological, Climatological and Geophysics (BMKG) and United States Geological Survey's (USGS). Clustering process consist of two steps: finding the global optimum number of clusters using Valley Tracing and clustering the dataset based on Hierarchical K-means. The optimal number of cluster obtained is 6 cluster. A model of Artificial Neural Networks (ANNs) is implemented for selected cluster to conduct an earthquake prediction. The architecture of the neural network model is composed of seven inputs, two hidden layers with thirty-two nodes each and one output. Back propagation training method and sigmoid activation function are applied. The input values are related to the b-value, the Bath's law, and the Omori-Utsu's law. The ANNs prototype predicts earthquake which is equal or larger than the given threshold magnitude during the next five days after an earthquake occurrence. Statistical tests are provided using two threshold values (5.5 and 6). The ANNs result showed that the proposed model gave better performance to predict earthquake that equal or larger than 6 Richter's scale magnitude. Finally, the result were compared to other ANNs model showing quantitatively and qualitatively better results.","PeriodicalId":117148,"journal":{"name":"2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127951263","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

Knowledge trust in online programming communities 在线编程社区中的知识信任

2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC) Pub Date : 2017-09-01 DOI: 10.1109/KCIC.2017.8228588

Ikut Tri Handoyo, Adnan Ardhian, Muhammad Ihsan Mas, Nitto Sahadi, D. I. Sensuse, Elin Cahyaningsih

引用次数: 0

Design and development virtual reality athletic — Virtual imagery to train sprinter's concentration 设计和开发虚拟现实运动-虚拟图像训练短跑运动员的注意力

2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC) Pub Date : 2017-09-01 DOI: 10.1109/KCIC.2017.8228580

M. Choiri, A. Basuki, A. Bagus, S. Sukaridhoto, M. Jannah

{"title":"Design and development virtual reality athletic — Virtual imagery to train sprinter's concentration","authors":"M. Choiri, A. Basuki, A. Bagus, S. Sukaridhoto, M. Jannah","doi":"10.1109/KCIC.2017.8228580","DOIUrl":"https://doi.org/10.1109/KCIC.2017.8228580","url":null,"abstract":"Sports activities are getting more and more attention from both government and society. In the athletics sport, in addition to agility and good ability, an athlete must have a strong mentality. Mental of the athlete is very critical on its performance. Most successful athletes achieve their peak achievement of 60% to 90% influenced by mental factors and the ability of athletes to master their psychological state, One of the elements of psychology that greatly affect is the concentration. Not a few athletes who have low concentration power, so it takes practice to improve the power of concentration. Therefore, the research is useful to help runners or athletes in training the power of concentration by utilizing virtual reality technology. We develop a training system for athletes by utilizing Virtual Reality (VR) that calculates head movement as concentration measurement. This system consists of VR hardware and VR environment. We have result from an experiment that said we could measure the concentration of an athlete by measuring how much the attention of an athlete is distracted. Therefore we develop a system that can simulate that. Virtual reality technology is chosen because it can deliver an immersive experience. By simulating the environment of the field during a sprinting game, it is expected that the runners will get used to the atmosphere and can concentrate on giving their best performance.","PeriodicalId":117148,"journal":{"name":"2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122808066","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

Attendance report plugin for E-learning applications in PENS: (Based on moodle) 用于pen电子学习应用的考勤报告插件:(基于moodle)

2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC) Pub Date : 2017-09-01 DOI: 10.1109/KCIC.2017.8228579

D. Susanto, S. Irdoni, M. A. Rasyid

{"title":"Attendance report plugin for E-learning applications in PENS: (Based on moodle)","authors":"D. Susanto, S. Irdoni, M. A. Rasyid","doi":"10.1109/KCIC.2017.8228579","DOIUrl":"https://doi.org/10.1109/KCIC.2017.8228579","url":null,"abstract":"The development of the Internet functions now affects many activities, talks, meetings, shopping, and learnings. E-Learning is part of the development models of learning that utilizes the internet. The current development model of online learning still needs support and new innovations. Politeknk Elektronika Negeri Surabaya (PENS) has an online learning application that uses the Moodle LMS. However, the LMS requires a plugin that is used to monitor activity of students' presence, that is when they are using the application. Thus, a Moodle plugin to monitor students' presence needs to be developed. This plugin has a function to record the login time of a user as a marker of the presence in eLearning. It obtains the data by using PHP code that was added to every page in Moodle. In addition to noting the value of logged in user, this plugin also notes certain activities performed by the user. Furthermore, the plugin processes the data and display it as a report with appropriate format as required by PENS. This plugin was examined of its ability to provide information about user's activities when using the eLearning application.","PeriodicalId":117148,"journal":{"name":"2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC)","volume":"272 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115904317","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: 4

Detection melanoma cancer using ABCD rule based on mobile device 基于移动设备的ABCD规则检测黑色素瘤

2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC) Pub Date : 2017-09-01 DOI: 10.1109/KCIC.2017.8228575

Hardi Firmansyah, Entin Martiana Kusumaningtyas, Fadilah Fahrul Hardiansyah

引用次数: 21