Pub Date : 2017-09-01DOI: 10.1109/KCIC.2017.8228452
Hilmy Assodiky, I. Syarif, T. Badriyah
Most of cardiovascular disorders or diseases can be prevented, but death continues to rise due to improper treatment because of misdiagnose. One of cardiovascular diseases is Arrhythmia. It is sometimes difficult to observe electrocardiogram (ECG) recording for Arrhythmia detection. Therefore, it needs a good learning method to be applied in the computer as a way to help the detection of Arrhythmia. There is a powerful approach in Machine Learning, named Deep Learning. It starts to be widely used for Speech Recognition, Bioinformatics, Computer Vision, and many others. This research used the Deep Learning to classify the Arrhythmia data. We compared the result to other popular machine learning algorithm, such as Naive Bayes, K-Nearest Neighbor, Artificial Neural Network, and Support Vector Machine. Our experiment showed that Deep Learning algorithm achieved the best accuracy, which was 76,51%.
{"title":"Deep learning algorithm for arrhythmia detection","authors":"Hilmy Assodiky, I. Syarif, T. Badriyah","doi":"10.1109/KCIC.2017.8228452","DOIUrl":"https://doi.org/10.1109/KCIC.2017.8228452","url":null,"abstract":"Most of cardiovascular disorders or diseases can be prevented, but death continues to rise due to improper treatment because of misdiagnose. One of cardiovascular diseases is Arrhythmia. It is sometimes difficult to observe electrocardiogram (ECG) recording for Arrhythmia detection. Therefore, it needs a good learning method to be applied in the computer as a way to help the detection of Arrhythmia. There is a powerful approach in Machine Learning, named Deep Learning. It starts to be widely used for Speech Recognition, Bioinformatics, Computer Vision, and many others. This research used the Deep Learning to classify the Arrhythmia data. We compared the result to other popular machine learning algorithm, such as Naive Bayes, K-Nearest Neighbor, Artificial Neural Network, and Support Vector Machine. Our experiment showed that Deep Learning algorithm achieved the best accuracy, which was 76,51%.","PeriodicalId":117148,"journal":{"name":"2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC)","volume":"48 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":"124317554","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 : 2017-09-01DOI: 10.1109/KCIC.2017.8228582
Nur Ashar Aditiya, Zaqiatud Darojah, D. Sanggar, Muhammad Rizky Dharmawan
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.
{"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}
Pub Date : 2017-09-01DOI: 10.1109/KCIC.2017.8228455
Yesta Medya Mahardhika, Amang Sudarsono, Ali Ridho Barakbah
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).
{"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}
Pub Date : 2017-09-01DOI: 10.1109/KCIC.2017.8228451
Zaqiatud Darojah, E. S. Ningrum, D. Purnomo
In the previous study, we have investigated that the Extended Kalman Filter (EKF) has the excellennt performance and very fast learning as the training of Feedforward Neural Network (FNN). In the expansion of Kalman filter algorithm for nonlinear estimation, the Unscented Kalman Filter (UKF) was proposed. Enlightened the UKF is superior to EKF, in this study, we investigate the UKF algorithm as the training of FNN for voice classification application. Simulation results show that the UKF has also very excellence performance. The training process need only 2 epochs with the average performance rates in training data is 100% and the average performance rates in the testing data is 94.49%. These results are the same as the EKF-based FNN and the Levenberg-Marquardt Backpropagation but differ in the required training epoch.
{"title":"The training of feedforward neural network using the unscented Kalman filter for voice classification application","authors":"Zaqiatud Darojah, E. S. Ningrum, D. Purnomo","doi":"10.1109/KCIC.2017.8228451","DOIUrl":"https://doi.org/10.1109/KCIC.2017.8228451","url":null,"abstract":"In the previous study, we have investigated that the Extended Kalman Filter (EKF) has the excellennt performance and very fast learning as the training of Feedforward Neural Network (FNN). In the expansion of Kalman filter algorithm for nonlinear estimation, the Unscented Kalman Filter (UKF) was proposed. Enlightened the UKF is superior to EKF, in this study, we investigate the UKF algorithm as the training of FNN for voice classification application. Simulation results show that the UKF has also very excellence performance. The training process need only 2 epochs with the average performance rates in training data is 100% and the average performance rates in the testing data is 94.49%. These results are the same as the EKF-based FNN and the Levenberg-Marquardt Backpropagation but differ in the required training epoch.","PeriodicalId":117148,"journal":{"name":"2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC)","volume":"54 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":"124179053","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 : 2017-09-01DOI: 10.1109/KCIC.2017.8228592
Renovita Edelani, Ali Ridho Barakbah, T. Harsono, Amang Sudarsono
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.
{"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}
Pub Date : 2017-09-01DOI: 10.1109/KCIC.2017.8228594
M. Shodiq, D. Kusuma, M. Rifqi, Ali Ridho Barakbah, T. Harsono
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.
{"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}
Pub Date : 2017-09-01DOI: 10.1109/KCIC.2017.8228588
Ikut Tri Handoyo, Adnan Ardhian, Muhammad Ihsan Mas, Nitto Sahadi, D. I. Sensuse, Elin Cahyaningsih
The proliferation and popularity of online programming forums raises the question of what affects and how do those factors affect the users' level of trust towards the knowledge that is contained within online programming forums. This study uses elements of existing studies that examine trust in online communities, using “community dimensions” of shared consciousness, shared tradition, and obligation to society as latent variables. The indicator variables are the “value dimensions” of social networking, community engagement, knowledge use, and impression management. The factor that affects knowledge trust the most is knowledge use, with a path coefficient of 0.57. The hypotheses regarding shared consciousness is rejected, and individual factors seem to affect knowledge use more than community factors.
{"title":"Knowledge trust in online programming communities","authors":"Ikut Tri Handoyo, Adnan Ardhian, Muhammad Ihsan Mas, Nitto Sahadi, D. I. Sensuse, Elin Cahyaningsih","doi":"10.1109/KCIC.2017.8228588","DOIUrl":"https://doi.org/10.1109/KCIC.2017.8228588","url":null,"abstract":"The proliferation and popularity of online programming forums raises the question of what affects and how do those factors affect the users' level of trust towards the knowledge that is contained within online programming forums. This study uses elements of existing studies that examine trust in online communities, using “community dimensions” of shared consciousness, shared tradition, and obligation to society as latent variables. The indicator variables are the “value dimensions” of social networking, community engagement, knowledge use, and impression management. The factor that affects knowledge trust the most is knowledge use, with a path coefficient of 0.57. The hypotheses regarding shared consciousness is rejected, and individual factors seem to affect knowledge use more than community factors.","PeriodicalId":117148,"journal":{"name":"2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC)","volume":"18 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":"125114938","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 : 2017-09-01DOI: 10.1109/KCIC.2017.8228580
M. Choiri, A. Basuki, A. Bagus, S. Sukaridhoto, M. Jannah
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.
{"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}
Pub Date : 2017-09-01DOI: 10.1109/KCIC.2017.8228579
D. Susanto, S. Irdoni, M. A. Rasyid
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.
{"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}
Melanoma is skin cancer that attacks a pigment human cell who raise melanin cell and causing death if unknown early. According to from Abramson Cancer Center, There are 76.690 new cases melanoma in the United States on 2013. Dermoscopy is one of a current use, but need a special expertise to detect a cancer melanoma. This research proposes a new approach to detecting melanoma using ABCD rule which is Including in the methods of dermoscopy and using STOLZ Algorithm to given weight in detection. The detection is conducted on the image which was taken with mobile device camera and testing process is performed on mobile. The technique used to preprocessing is using OpenCV to take extraction for sampling suspect skin melanoma. The result from this application showing output in form TDS score and classification result which is appropriate from input picture. The result of this application shown value TDS and classification hypothesis suspected melanoma or normal mole from cameras smartphone used.
{"title":"Detection melanoma cancer using ABCD rule based on mobile device","authors":"Hardi Firmansyah, Entin Martiana Kusumaningtyas, Fadilah Fahrul Hardiansyah","doi":"10.1109/KCIC.2017.8228575","DOIUrl":"https://doi.org/10.1109/KCIC.2017.8228575","url":null,"abstract":"Melanoma is skin cancer that attacks a pigment human cell who raise melanin cell and causing death if unknown early. According to from Abramson Cancer Center, There are 76.690 new cases melanoma in the United States on 2013. Dermoscopy is one of a current use, but need a special expertise to detect a cancer melanoma. This research proposes a new approach to detecting melanoma using ABCD rule which is Including in the methods of dermoscopy and using STOLZ Algorithm to given weight in detection. The detection is conducted on the image which was taken with mobile device camera and testing process is performed on mobile. The technique used to preprocessing is using OpenCV to take extraction for sampling suspect skin melanoma. The result from this application showing output in form TDS score and classification result which is appropriate from input picture. The result of this application shown value TDS and classification hypothesis suspected melanoma or normal mole from cameras smartphone used.","PeriodicalId":117148,"journal":{"name":"2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC)","volume":"68 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":"126374419","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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