Pub Date : 2018-10-01DOI: 10.1109/ICICOS.2018.8621685
Albertus Dwiyoga Widiantoro, E. M. Dukut, Cecilia Titiek Murniati
Educative games are trending among students. Games can be used to support student learning. Difficulties in completing a TOEFL (Test of English as a Foreign Language) can be helped by doing game exercises that are similar to the actual test conditions. The TOEFL learning method while playing the game becomes an interesting project, to see how students can use the game experience to master the skills needed for doing a TOEFL. In this study a mobile educative game application is created to understand the TOEFL test that has many features. The Tommy & Pokina TOEFL-Like App Game is one game that is expected to be used to improve students' TOEFL abilities. In seeing the results of the students' TOEFL as game players, the integration of games with information systems makes it easy for teachers to get the information quickly. This article shares how the information system help facilitate fast and good data management of the students' TOEFL results.
{"title":"Information System for Game TOEFL like App","authors":"Albertus Dwiyoga Widiantoro, E. M. Dukut, Cecilia Titiek Murniati","doi":"10.1109/ICICOS.2018.8621685","DOIUrl":"https://doi.org/10.1109/ICICOS.2018.8621685","url":null,"abstract":"Educative games are trending among students. Games can be used to support student learning. Difficulties in completing a TOEFL (Test of English as a Foreign Language) can be helped by doing game exercises that are similar to the actual test conditions. The TOEFL learning method while playing the game becomes an interesting project, to see how students can use the game experience to master the skills needed for doing a TOEFL. In this study a mobile educative game application is created to understand the TOEFL test that has many features. The Tommy & Pokina TOEFL-Like App Game is one game that is expected to be used to improve students' TOEFL abilities. In seeing the results of the students' TOEFL as game players, the integration of games with information systems makes it easy for teachers to get the information quickly. This article shares how the information system help facilitate fast and good data management of the students' TOEFL results.","PeriodicalId":438473,"journal":{"name":"2018 2nd International Conference on Informatics and Computational Sciences (ICICoS)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134229276","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 : 2018-10-01DOI: 10.1109/ICICOS.2018.8621646
R. Ramadhiani, M. Yan, G. Hertono, B. Handari
The problem of portfolio optimization is a research topic that is quite widely discussed in the financial sector. The first model in this problem is the mean-variance model that focuses on expected return and risk without considering the constraints contained in the real problem. In this paper, a portfolio optimization model with real constraints which is commonly known as the Mean-Variance Cardinality Constrained Portfolio Optimization (MVCCPO) model is considered. The e-New Local Search based Multi-objective Optimization Algorithm (e-NSLS) and Multi-objective Covariance based Artificial Bee Colony (M -CABC) algorithm are used to solve portfolio optimization problem on datasets involving up to 225 assets. Obtained results are compared with the unconstrained efficient frontier of the corresponding data sets. The numerical simulations state that e-NSLS algorithm gives a better solution than M-CABC, where the solutions produced by e-NSLS are nearer to the corresponding unconstrained efficient frontier than the solutions generated by M-CABC.
{"title":"Implementation of e-New Local Search based Multiobjective Optimization Algorithm and Multiobjective Co-variance based Artificial Bee Colony Algorithm in Stocks Portfolio Optimization Problem","authors":"R. Ramadhiani, M. Yan, G. Hertono, B. Handari","doi":"10.1109/ICICOS.2018.8621646","DOIUrl":"https://doi.org/10.1109/ICICOS.2018.8621646","url":null,"abstract":"The problem of portfolio optimization is a research topic that is quite widely discussed in the financial sector. The first model in this problem is the mean-variance model that focuses on expected return and risk without considering the constraints contained in the real problem. In this paper, a portfolio optimization model with real constraints which is commonly known as the Mean-Variance Cardinality Constrained Portfolio Optimization (MVCCPO) model is considered. The e-New Local Search based Multi-objective Optimization Algorithm (e-NSLS) and Multi-objective Covariance based Artificial Bee Colony (M -CABC) algorithm are used to solve portfolio optimization problem on datasets involving up to 225 assets. Obtained results are compared with the unconstrained efficient frontier of the corresponding data sets. The numerical simulations state that e-NSLS algorithm gives a better solution than M-CABC, where the solutions produced by e-NSLS are nearer to the corresponding unconstrained efficient frontier than the solutions generated by M-CABC.","PeriodicalId":438473,"journal":{"name":"2018 2nd International Conference on Informatics and Computational Sciences (ICICoS)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115811352","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 : 2018-10-01DOI: 10.1109/ICICOS.2018.8621819
I. Tangkawarow, R. Sarno, A. Fauzan
Business process model simulation is modeled based on Standard Operational Procedure (SOP). Event log is a collection of cases which every element refers to an activity, case, and time of event or process in an information system. This research was conducted in order to simulate business process model utilizing discrete event approach (DES) and process mining paradigm to simulate information from the event log. We obtained event logs from Badan Pengelola Pajak dan Retribusi Daerah (BP2RD or Regional Tax and Retribution Agency) in business tax determination process. Event logs need to be simulate to reflect the performance in real situation. After that we use the existing event log to forecast the performance in indicate years. We utilize exponential smoothing method to forecast the number of business actors for the subsequent years. At the end, we compare existing performance and forecasting performance. This research aims to calculate tax determination performance for business actors according to existing business process, analyze existing performance with DES and analyzing future performance by forecasting business actors' growth. The result of the research shows the differences of performance evaluation between existing and forecasting event log, forecasting log occurs increment 615 logs or 60.12% with execution time decrease to 95.39% each trace.
{"title":"Evaluation the Performance of Tax Determination Using Discrete Event Simulation","authors":"I. Tangkawarow, R. Sarno, A. Fauzan","doi":"10.1109/ICICOS.2018.8621819","DOIUrl":"https://doi.org/10.1109/ICICOS.2018.8621819","url":null,"abstract":"Business process model simulation is modeled based on Standard Operational Procedure (SOP). Event log is a collection of cases which every element refers to an activity, case, and time of event or process in an information system. This research was conducted in order to simulate business process model utilizing discrete event approach (DES) and process mining paradigm to simulate information from the event log. We obtained event logs from Badan Pengelola Pajak dan Retribusi Daerah (BP2RD or Regional Tax and Retribution Agency) in business tax determination process. Event logs need to be simulate to reflect the performance in real situation. After that we use the existing event log to forecast the performance in indicate years. We utilize exponential smoothing method to forecast the number of business actors for the subsequent years. At the end, we compare existing performance and forecasting performance. This research aims to calculate tax determination performance for business actors according to existing business process, analyze existing performance with DES and analyzing future performance by forecasting business actors' growth. The result of the research shows the differences of performance evaluation between existing and forecasting event log, forecasting log occurs increment 615 logs or 60.12% with execution time decrease to 95.39% each trace.","PeriodicalId":438473,"journal":{"name":"2018 2nd International Conference on Informatics and Computational Sciences (ICICoS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126109173","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 : 2018-10-01DOI: 10.1109/ICICOS.2018.8621670
Rahadian Kurniawan, Dhomas Hatta Fudholi, I. Muhimmah, A. Kurniawardhani, Indrayanti
We evaluate the characteristic of the normal epithelial cervical cell in Pap Smear images, using feature analysis. The evaluation affects the determination of proper pap smear image determination. This study aims to analyze the performance of feature selection on data classification and discovering features which significantly affect the classification of the normal epithelial cervical cell. Feature selection process has been done to 54 features in the nuclei area and the cytoplasm of the cervical epithelial cell, using Feature Subset Selection. Furthermore, we compare the performance of two classification methods: K-Nearest Neighbors (KNN) and Backpropagation. Both methods resulting in the same 12 features to differentiate between normal cervical cells. The classification accuracies for both methods are 92.29% for KNN and 91.51% for Backpropagation.
{"title":"Feature Analysis of Normal Epithelial Cervical Cell Characteristics in Pap Smear Images","authors":"Rahadian Kurniawan, Dhomas Hatta Fudholi, I. Muhimmah, A. Kurniawardhani, Indrayanti","doi":"10.1109/ICICOS.2018.8621670","DOIUrl":"https://doi.org/10.1109/ICICOS.2018.8621670","url":null,"abstract":"We evaluate the characteristic of the normal epithelial cervical cell in Pap Smear images, using feature analysis. The evaluation affects the determination of proper pap smear image determination. This study aims to analyze the performance of feature selection on data classification and discovering features which significantly affect the classification of the normal epithelial cervical cell. Feature selection process has been done to 54 features in the nuclei area and the cytoplasm of the cervical epithelial cell, using Feature Subset Selection. Furthermore, we compare the performance of two classification methods: K-Nearest Neighbors (KNN) and Backpropagation. Both methods resulting in the same 12 features to differentiate between normal cervical cells. The classification accuracies for both methods are 92.29% for KNN and 91.51% for Backpropagation.","PeriodicalId":438473,"journal":{"name":"2018 2nd International Conference on Informatics and Computational Sciences (ICICoS)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123749060","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 : 2018-10-01DOI: 10.1109/ICICOS.2018.8621762
H. A. Wibawa, I. Malik, N. Bahtiar
Chronic Kidney Disease (CKD) prevalence is going to increase year by year. CKD prediction can be used as one of references for further treatment. The success of CKD prediction usually depend on classifier selected. This paper proposes and evaluates Kernel-based Extreme Learning Machine to predict Chronic Kidney Disease. Subsequently, various kernel-based ELM were evaluated. We compared the performance of four kernels-based ELM, namely RBF-ELM, Linear-ELM, Polynomial-ELM, Wavelet-ELM and the performance of standard ELM. The result showed that radial basis function extrem learning machine (RBF -ELM) was higher than those from the other tested and give the best prediction sensitivity and specificity of 99.38% and 100% respectively
{"title":"Evaluation of Kernel-Based Extreme Learning Machine Performance for Prediction of Chronic Kidney Disease","authors":"H. A. Wibawa, I. Malik, N. Bahtiar","doi":"10.1109/ICICOS.2018.8621762","DOIUrl":"https://doi.org/10.1109/ICICOS.2018.8621762","url":null,"abstract":"Chronic Kidney Disease (CKD) prevalence is going to increase year by year. CKD prediction can be used as one of references for further treatment. The success of CKD prediction usually depend on classifier selected. This paper proposes and evaluates Kernel-based Extreme Learning Machine to predict Chronic Kidney Disease. Subsequently, various kernel-based ELM were evaluated. We compared the performance of four kernels-based ELM, namely RBF-ELM, Linear-ELM, Polynomial-ELM, Wavelet-ELM and the performance of standard ELM. The result showed that radial basis function extrem learning machine (RBF -ELM) was higher than those from the other tested and give the best prediction sensitivity and specificity of 99.38% and 100% respectively","PeriodicalId":438473,"journal":{"name":"2018 2nd International Conference on Informatics and Computational Sciences (ICICoS)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131589598","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 : 2018-10-01DOI: 10.1109/ICICOS.2018.8621640
Rizky Ade Putranto, S. Suryono, J. E. Suseno
This research introduces new technology in monitoring postpartum health services. This research proposes a system that helps display patient medical record data during the postpartum period. The system uses Cloud Computing (CC) technology for quick calculation of the classification of the nutritional status of infants during postpartum. Database management of nutritional status classification uses virtualization and Web Service (WS) who can manage the resources needed to support multitasking performance. The information obtained is then processed using the method Forward Chaining (FC) to model the patient's condition based on variables of vital signs and classification of nutritional status of children based on Anthropometry Index. The system created has advantages over previous research, using Cloud Computing (CC) Platform as a Service (PaaS) technology. This system is faster and more efficient in reading the monitoring area for classification of infant nutritional status. Data calculation simulations show the initial results that the output obtained is as expected.
{"title":"Cloud Computing Medical Record Related Baby Nutrition Status Anthropometry Index During Postpartum","authors":"Rizky Ade Putranto, S. Suryono, J. E. Suseno","doi":"10.1109/ICICOS.2018.8621640","DOIUrl":"https://doi.org/10.1109/ICICOS.2018.8621640","url":null,"abstract":"This research introduces new technology in monitoring postpartum health services. This research proposes a system that helps display patient medical record data during the postpartum period. The system uses Cloud Computing (CC) technology for quick calculation of the classification of the nutritional status of infants during postpartum. Database management of nutritional status classification uses virtualization and Web Service (WS) who can manage the resources needed to support multitasking performance. The information obtained is then processed using the method Forward Chaining (FC) to model the patient's condition based on variables of vital signs and classification of nutritional status of children based on Anthropometry Index. The system created has advantages over previous research, using Cloud Computing (CC) Platform as a Service (PaaS) technology. This system is faster and more efficient in reading the monitoring area for classification of infant nutritional status. Data calculation simulations show the initial results that the output obtained is as expected.","PeriodicalId":438473,"journal":{"name":"2018 2nd International Conference on Informatics and Computational Sciences (ICICoS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133381077","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 : 2018-10-01DOI: 10.1109/ICICOS.2018.8621683
Fiker Aofa, P. S. Sasongko, Sutikno, Suhartono, Wildan Azka Adzani
Diabetes Mellitus (DM) is a health problem that is growing rapidly in Indonesia. According to the International Diabetes Federation (IDF) in 2013, DM patients in Indonesia were around 8.5 million people. Delay in recognizing the initial symptoms of DM can cause complications with other diseases and produce a more difficult treatment process that can even cause death. Early detection of DM is a way to detect the possibility of someone having DM. The problem under study is the clinical symptoms of DM which are observed only in outliser problems, where data can be categorized as a hot coding condition, where the data is in the form of ‘Yes' or ‘No’. In this study we conduced a comparison of several artificial neural network techniques for early detection of DM namely the Standart Backpropagation Neural Network (SBNN), SBNN with Adaptive Learning Rate (SBNN+ALR), SBNN with Particle Swarm Optimization (SBNN+PSO), or SBNN with Particle Swarm Optimization and Adaptive Learning Rate (SBNN+PSO+ALR). The variables used in this study are symptoms and factors supporting DM as many as 9 variables. Research data is taken from medical records at the Health Center (Puskesmas) Brebes. Distribution of training data and test data is determined by K-fold Cross Validation method. The results was showed that the best architecture is obtained SBNN+PSO+ALR. The SBNN+PSO+ALR architecture produced an average accuracy of 88,75%, sensitivity value of 82,5%, specificity value of 95% and Mean Squared Error (MSE) value of 0,02939 in only 30 epoch.
{"title":"Early Detection System Of Diabetes Mellitus Disease Using Artificial Neural Network Backpropagation With Adaptive Learning Rate And Particle Swarm Optimization","authors":"Fiker Aofa, P. S. Sasongko, Sutikno, Suhartono, Wildan Azka Adzani","doi":"10.1109/ICICOS.2018.8621683","DOIUrl":"https://doi.org/10.1109/ICICOS.2018.8621683","url":null,"abstract":"Diabetes Mellitus (DM) is a health problem that is growing rapidly in Indonesia. According to the International Diabetes Federation (IDF) in 2013, DM patients in Indonesia were around 8.5 million people. Delay in recognizing the initial symptoms of DM can cause complications with other diseases and produce a more difficult treatment process that can even cause death. Early detection of DM is a way to detect the possibility of someone having DM. The problem under study is the clinical symptoms of DM which are observed only in outliser problems, where data can be categorized as a hot coding condition, where the data is in the form of ‘Yes' or ‘No’. In this study we conduced a comparison of several artificial neural network techniques for early detection of DM namely the Standart Backpropagation Neural Network (SBNN), SBNN with Adaptive Learning Rate (SBNN+ALR), SBNN with Particle Swarm Optimization (SBNN+PSO), or SBNN with Particle Swarm Optimization and Adaptive Learning Rate (SBNN+PSO+ALR). The variables used in this study are symptoms and factors supporting DM as many as 9 variables. Research data is taken from medical records at the Health Center (Puskesmas) Brebes. Distribution of training data and test data is determined by K-fold Cross Validation method. The results was showed that the best architecture is obtained SBNN+PSO+ALR. The SBNN+PSO+ALR architecture produced an average accuracy of 88,75%, sensitivity value of 82,5%, specificity value of 95% and Mean Squared Error (MSE) value of 0,02939 in only 30 epoch.","PeriodicalId":438473,"journal":{"name":"2018 2nd International Conference on Informatics and Computational Sciences (ICICoS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131047138","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 : 2018-10-01DOI: 10.1109/ICICOS.2018.8621748
Sandy Kurniawan, R. Kusumaningrum, Melnyi Ehonia Timu
Traveloka provides a space for its users to write reviews about their hotel reservation services. These reviews are very useful in informing hotel managers of the level of customer satisfaction. Sentiment analysis is a tool that can be used to analyse such reviews to determine whether they express opinions or not, so that the level of customer satisfaction can be measured based on the number of sentiments (positive or negative) contained in the opinions. In this research, the Naïve Bayes classifier was used to perform a hierarchical sentence sentiment analysis on hotel reviews obtained from Traveloka. In addition, two types of term weighting schemes were used for the feature extraction, namely, raw term frequency and TF-IDF. The results of this research indicated that it is better to use a hierarchical classification in sentiment analysis than a flat classification. The average F-measure value for the flat classification model was 75.18%, while for the hierarchical classification model it was 77.48%. These results showed that the use of a hierarchical classification in sentiment analysis improved the average performance of the classification model by 2.3%. The use of the raw term frequency feature extraction in a flat classification provided a higher F-measure value than the use of the TF-IDF feature extraction, with a margin of 3.9%. The average F-measure value for the flat classification using the raw term frequency feature extraction was 75.18%, while for the TF-IDF feature extraction it was 71.23%.
{"title":"Hierarchical Sentence Sentiment Analysis Of Hotel Reviews Using The Naïve Bayes Classifier","authors":"Sandy Kurniawan, R. Kusumaningrum, Melnyi Ehonia Timu","doi":"10.1109/ICICOS.2018.8621748","DOIUrl":"https://doi.org/10.1109/ICICOS.2018.8621748","url":null,"abstract":"Traveloka provides a space for its users to write reviews about their hotel reservation services. These reviews are very useful in informing hotel managers of the level of customer satisfaction. Sentiment analysis is a tool that can be used to analyse such reviews to determine whether they express opinions or not, so that the level of customer satisfaction can be measured based on the number of sentiments (positive or negative) contained in the opinions. In this research, the Naïve Bayes classifier was used to perform a hierarchical sentence sentiment analysis on hotel reviews obtained from Traveloka. In addition, two types of term weighting schemes were used for the feature extraction, namely, raw term frequency and TF-IDF. The results of this research indicated that it is better to use a hierarchical classification in sentiment analysis than a flat classification. The average F-measure value for the flat classification model was 75.18%, while for the hierarchical classification model it was 77.48%. These results showed that the use of a hierarchical classification in sentiment analysis improved the average performance of the classification model by 2.3%. The use of the raw term frequency feature extraction in a flat classification provided a higher F-measure value than the use of the TF-IDF feature extraction, with a margin of 3.9%. The average F-measure value for the flat classification using the raw term frequency feature extraction was 75.18%, while for the TF-IDF feature extraction it was 71.23%.","PeriodicalId":438473,"journal":{"name":"2018 2nd International Conference on Informatics and Computational Sciences (ICICoS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129656402","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 : 2018-09-02DOI: 10.1109/ICICOS.2018.8621687
Kuntoro Adi Nugroho
Optical Coherence Tomography allows ophthalmologist to obtain cross-section imaging of eye retina. Assisted with digital image analysis methods, effective disease detection could be performed. Various methods exist to extract feature from OCT images. The proposed study aims to compare the effectiveness of handcrafted and deep neural network features. The dataset consists of 32339 instances which are distributed in four classes. The feature extractors are Histogram of Oriented Gradient (HOG), Local Binary Pattern (LBP), DenseNet-169, and ResNet50. As a result, the deep neural network based methods outperformed the handcrafted feature with 88% and 89% accuracy for DenseNet and ResNet compared to 50 % and 42 % for HOG and LBP respectively. The deep neural network based methods also demonstrated better result on the under represented classes.
{"title":"A Comparison of Handcrafted and Deep Neural Network Feature Extraction for Classifying Optical Coherence Tomography (OCT) Images","authors":"Kuntoro Adi Nugroho","doi":"10.1109/ICICOS.2018.8621687","DOIUrl":"https://doi.org/10.1109/ICICOS.2018.8621687","url":null,"abstract":"Optical Coherence Tomography allows ophthalmologist to obtain cross-section imaging of eye retina. Assisted with digital image analysis methods, effective disease detection could be performed. Various methods exist to extract feature from OCT images. The proposed study aims to compare the effectiveness of handcrafted and deep neural network features. The dataset consists of 32339 instances which are distributed in four classes. The feature extractors are Histogram of Oriented Gradient (HOG), Local Binary Pattern (LBP), DenseNet-169, and ResNet50. As a result, the deep neural network based methods outperformed the handcrafted feature with 88% and 89% accuracy for DenseNet and ResNet compared to 50 % and 42 % for HOG and LBP respectively. The deep neural network based methods also demonstrated better result on the under represented classes.","PeriodicalId":438473,"journal":{"name":"2018 2nd International Conference on Informatics and Computational Sciences (ICICoS)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132200701","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 : 2017-11-01DOI: 10.1109/ICICOS.2017.8276350
S. Adhy, Aditia Prasetio, B. Noranita, R. Saputra
Uncertain weather changes have the large potential impact if the community does not have the awareness of the importance of knowing the changes in the weather and the resulting impacts. Various technologies have been developed and utilized to solve these problems. WeMo application was an example of the development and utilization of Android-based technology that can be used to monitor weather data in the surrounding environment and as a space for students at Diponegoro University to share information related to weather changes. Applications that have been developed need to be tested to ensure that users can use the application easily and measure the extent to which users can use the application to achieve the goal. Therefore, this research does usability testing on WeMo Applications with respondents from different backgrounds. There are two approaches to usability testing, namely Questionnaire-based and Performance-based evaluation. The results of usability testing using the task completion mechanism show that the effectiveness score of the WeMo application was 93.33% and overall relative efficiency score reached 91.57%. Based on the results of the questionnaire, learnability WeMo application reaches 83.6% and satisfaction was 83.2% which means it belongs to the category of “good” or good in accordance with adjective ratings.
{"title":"Usability Testing of Weather Monitoring on Android Application","authors":"S. Adhy, Aditia Prasetio, B. Noranita, R. Saputra","doi":"10.1109/ICICOS.2017.8276350","DOIUrl":"https://doi.org/10.1109/ICICOS.2017.8276350","url":null,"abstract":"Uncertain weather changes have the large potential impact if the community does not have the awareness of the importance of knowing the changes in the weather and the resulting impacts. Various technologies have been developed and utilized to solve these problems. WeMo application was an example of the development and utilization of Android-based technology that can be used to monitor weather data in the surrounding environment and as a space for students at Diponegoro University to share information related to weather changes. Applications that have been developed need to be tested to ensure that users can use the application easily and measure the extent to which users can use the application to achieve the goal. Therefore, this research does usability testing on WeMo Applications with respondents from different backgrounds. There are two approaches to usability testing, namely Questionnaire-based and Performance-based evaluation. The results of usability testing using the task completion mechanism show that the effectiveness score of the WeMo application was 93.33% and overall relative efficiency score reached 91.57%. Based on the results of the questionnaire, learnability WeMo application reaches 83.6% and satisfaction was 83.2% which means it belongs to the category of “good” or good in accordance with adjective ratings.","PeriodicalId":438473,"journal":{"name":"2018 2nd International Conference on Informatics and Computational Sciences (ICICoS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124909588","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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