Pub Date : 2020-09-19DOI: 10.1109/iSemantic50169.2020.9234258
Desanty Ridzky, R. Sarno
Technology development in Indonesia has increasingly progressed and provided business opportunities for businesses to meet customer's needs. The presence of e-commerce that have been widely spread in Indonesia is one of the examples of the technological progress. Indonesia already has an e-commerce online travel agent that prioritized user's needs to make it easier for the user to make an online reservation more efficient and effective. Traveloka and Tiket.com are an e-commerce online travel agents with many downloader in Indonesia, in choosing an online travel agent, users are certainly influenced by several factors identify by using UTAUT2 model. The results of this study indicate the use of Traveloka for users is influenced by perceived security, price value, and habit factors, while Tiket.com is influenced by facilitating conditions, performance expectancy, and habit. Companies could focus on these factors in terms of increasing the desire of users to use online travel agents.
{"title":"UTAUT2 model for analyzing factors influencing user in using Online Travel Agent","authors":"Desanty Ridzky, R. Sarno","doi":"10.1109/iSemantic50169.2020.9234258","DOIUrl":"https://doi.org/10.1109/iSemantic50169.2020.9234258","url":null,"abstract":"Technology development in Indonesia has increasingly progressed and provided business opportunities for businesses to meet customer's needs. The presence of e-commerce that have been widely spread in Indonesia is one of the examples of the technological progress. Indonesia already has an e-commerce online travel agent that prioritized user's needs to make it easier for the user to make an online reservation more efficient and effective. Traveloka and Tiket.com are an e-commerce online travel agents with many downloader in Indonesia, in choosing an online travel agent, users are certainly influenced by several factors identify by using UTAUT2 model. The results of this study indicate the use of Traveloka for users is influenced by perceived security, price value, and habit factors, while Tiket.com is influenced by facilitating conditions, performance expectancy, and habit. Companies could focus on these factors in terms of increasing the desire of users to use online travel agents.","PeriodicalId":345558,"journal":{"name":"2020 International Seminar on Application for Technology of Information and Communication (iSemantic)","volume":"43 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133238460","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 : 2020-09-19DOI: 10.1109/iSemantic50169.2020.9234224
L. Gumilar, Mokhammad Sholeh
Power plants using fossil fuel are always used to meet the needs of the load at any time. However fossil fuels can cause air pollution. An alternative that can be used to reduce fossil energy is renewable energy source type of wind power plant (WPP). Currently WPP is only limited to reducing the use of fossil fuels and it has not been able to replace fossil energy sources. Because the power generated by WPP is still small capacity. For this reason, it is necessary to carry out continuous research on WPP to increase output power. This paper aims to determine the impact of the horizontal wind turbine blade length to output power and torque. In the method, the length of the turbine blade is varied, so there is also a change in the sweep area value. The effect of swept area, wind speed, turbine rotation speed, and Cp on the output power and torque is presented in curve. The results of the simulation are the highest power and torque at the blade length of 5 m. The highest power obtained is 69.74 kW, and the highest turbine torque is 66.9 kN.m. The longer the turbine blade, the higher the wind turbine power and torque.
{"title":"Impact of Blade Length on the Horizontal Wind Turbine Output Power and Torque","authors":"L. Gumilar, Mokhammad Sholeh","doi":"10.1109/iSemantic50169.2020.9234224","DOIUrl":"https://doi.org/10.1109/iSemantic50169.2020.9234224","url":null,"abstract":"Power plants using fossil fuel are always used to meet the needs of the load at any time. However fossil fuels can cause air pollution. An alternative that can be used to reduce fossil energy is renewable energy source type of wind power plant (WPP). Currently WPP is only limited to reducing the use of fossil fuels and it has not been able to replace fossil energy sources. Because the power generated by WPP is still small capacity. For this reason, it is necessary to carry out continuous research on WPP to increase output power. This paper aims to determine the impact of the horizontal wind turbine blade length to output power and torque. In the method, the length of the turbine blade is varied, so there is also a change in the sweep area value. The effect of swept area, wind speed, turbine rotation speed, and Cp on the output power and torque is presented in curve. The results of the simulation are the highest power and torque at the blade length of 5 m. The highest power obtained is 69.74 kW, and the highest turbine torque is 66.9 kN.m. The longer the turbine blade, the higher the wind turbine power and torque.","PeriodicalId":345558,"journal":{"name":"2020 International Seminar on Application for Technology of Information and Communication (iSemantic)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125690346","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 : 2020-09-19DOI: 10.1109/iSemantic50169.2020.9234270
Paminto Agung Christianto, E. Sediyono, I. Sembiring
The successful rate of In Vitro Fertilization (IVF) in Indonesia is around 29%, which is considered low. One of the factors causing the IVF failure is anxiety. About 77.7% of IVF patients have a high anxiety, and 83.3% of IVF patients experienced an IVF failure. The long duration of waiting the answer from the fertility doctors become one cause of the anxiety in IVF patients. On the other hand, the fertility doctors themselves have other responsibilities that cause IVF patients questions are not able to be given immediately. The study focused on the problems of the IVF patients and the fertility doctors.This research uses triangulation to obtain valid data and include challenges in developing health sector systems. To test the effect of IVF patients on the proposed intelligent system, Anova testing has been carried out resulting in a value of F = 9,902 and a Coefficient test that produces a value of t = 3,147, so the test results provide evidence that IVF patient feedback has an effect on improving the quality of the intelligent system handling IVF patients post embryo transfer. The final results of the research have provided a case-based reasoning (CBR) modification recommendation for an intelligent system for handling IVF patients after embryo transfer.
{"title":"Case-Based Reasoning Modifications for Intelligent Systems in Handling In Vitro Fertilization (IVF) Patients Post Embryo Transfer","authors":"Paminto Agung Christianto, E. Sediyono, I. Sembiring","doi":"10.1109/iSemantic50169.2020.9234270","DOIUrl":"https://doi.org/10.1109/iSemantic50169.2020.9234270","url":null,"abstract":"The successful rate of In Vitro Fertilization (IVF) in Indonesia is around 29%, which is considered low. One of the factors causing the IVF failure is anxiety. About 77.7% of IVF patients have a high anxiety, and 83.3% of IVF patients experienced an IVF failure. The long duration of waiting the answer from the fertility doctors become one cause of the anxiety in IVF patients. On the other hand, the fertility doctors themselves have other responsibilities that cause IVF patients questions are not able to be given immediately. The study focused on the problems of the IVF patients and the fertility doctors.This research uses triangulation to obtain valid data and include challenges in developing health sector systems. To test the effect of IVF patients on the proposed intelligent system, Anova testing has been carried out resulting in a value of F = 9,902 and a Coefficient test that produces a value of t = 3,147, so the test results provide evidence that IVF patient feedback has an effect on improving the quality of the intelligent system handling IVF patients post embryo transfer. The final results of the research have provided a case-based reasoning (CBR) modification recommendation for an intelligent system for handling IVF patients after embryo transfer.","PeriodicalId":345558,"journal":{"name":"2020 International Seminar on Application for Technology of Information and Communication (iSemantic)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131407387","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 : 2020-09-19DOI: 10.1109/iSemantic50169.2020.9234268
Putri Damayanti, Dini Yuniasri, R. Sarno, Aziz Fajar, Dewi Rahmawati
Corpus callosum integrates left and right hemispheres of human brain. There are several methods for segmenting corpus callosum, but the existing algorithms need several steps to segment images. Therefore, we propose a simple method using level set method to segment corpus callosum. We use level set method as it can handle the structure of the brain easily. This method provides a numerical solution for processing changes in topological contours by representing a curve or surface as a zero level to a higher hyper-dimensional surface. This experiment shows that by implementing level set method to segment the corpus callosum produces Dice Similarity Coefficient (DSC) value of 85.14%.
{"title":"Corpus Callosum Segmentation from Brain MRI Images Based on Level Set Method","authors":"Putri Damayanti, Dini Yuniasri, R. Sarno, Aziz Fajar, Dewi Rahmawati","doi":"10.1109/iSemantic50169.2020.9234268","DOIUrl":"https://doi.org/10.1109/iSemantic50169.2020.9234268","url":null,"abstract":"Corpus callosum integrates left and right hemispheres of human brain. There are several methods for segmenting corpus callosum, but the existing algorithms need several steps to segment images. Therefore, we propose a simple method using level set method to segment corpus callosum. We use level set method as it can handle the structure of the brain easily. This method provides a numerical solution for processing changes in topological contours by representing a curve or surface as a zero level to a higher hyper-dimensional surface. This experiment shows that by implementing level set method to segment the corpus callosum produces Dice Similarity Coefficient (DSC) value of 85.14%.","PeriodicalId":345558,"journal":{"name":"2020 International Seminar on Application for Technology of Information and Communication (iSemantic)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117325515","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 : 2020-09-19DOI: 10.1109/iSemantic50169.2020.9234199
De Rosal Ignatius Moses Setiadi, Dewangga Satriya Rahardwika, E. H. Rachmawanto, Christy Atika Sari, Candra Irawan, Desi Purwanti Kusumaningrum, Nuri, Swapaka Listya Trusthi
Music recommendations are one of the important things, such as music streaming platforms. Classification of music genres is one of the important initial stages in the process of music recommendation based on genre. Many music classifications are proposed by extracting audio features that require a not light computing process. This research aims to analyze and test the performance of music genre classification based on metadata using three different classifiers, namely Support Vector Machine (SVM) with radial kernel base function (RBF), K Nearest Neighbors (K-NN), and Naïve Bayes (NB). The Spotify music dataset was chosen because it has complete metadata on each of its music. Based on the results of tests conducted by the SVM classifier has the best classification performance with 80% accuracy, then followed by KNN with 77.18% and NB with 76.08%. The accuracy results are relatively the same as music classification based on audio feature extraction, so the classification with the extraction of metadata features can continue to be developed if the metadata in the dataset is well managed.
{"title":"Comparison of SVM, KNN, and NB Classifier for Genre Music Classification based on Metadata","authors":"De Rosal Ignatius Moses Setiadi, Dewangga Satriya Rahardwika, E. H. Rachmawanto, Christy Atika Sari, Candra Irawan, Desi Purwanti Kusumaningrum, Nuri, Swapaka Listya Trusthi","doi":"10.1109/iSemantic50169.2020.9234199","DOIUrl":"https://doi.org/10.1109/iSemantic50169.2020.9234199","url":null,"abstract":"Music recommendations are one of the important things, such as music streaming platforms. Classification of music genres is one of the important initial stages in the process of music recommendation based on genre. Many music classifications are proposed by extracting audio features that require a not light computing process. This research aims to analyze and test the performance of music genre classification based on metadata using three different classifiers, namely Support Vector Machine (SVM) with radial kernel base function (RBF), K Nearest Neighbors (K-NN), and Naïve Bayes (NB). The Spotify music dataset was chosen because it has complete metadata on each of its music. Based on the results of tests conducted by the SVM classifier has the best classification performance with 80% accuracy, then followed by KNN with 77.18% and NB with 76.08%. The accuracy results are relatively the same as music classification based on audio feature extraction, so the classification with the extraction of metadata features can continue to be developed if the metadata in the dataset is well managed.","PeriodicalId":345558,"journal":{"name":"2020 International Seminar on Application for Technology of Information and Communication (iSemantic)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125954534","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 : 2020-09-19DOI: 10.1109/iSemantic50169.2020.9234250
V. Maheswari, C. A. Sari, D. Setiadi, E. H. Rachmawanto
Principal Component Analysis (PCA) is a very popular facial recognition method. This research aims to analyze the PCA method, where various scenarios are tested to look for things that affect the results of recognition using this method. There are three datasets used in the testing phase, namely the private dataset, JAFFE, and Yale. The accuracy produced in the private dataset is 79%, 82%, 86%, and 85.33% with a variety of different scenarios, while in the JAFFE dataset the maximum recognition accuracy is 100% and in the last experiment on the Yale dataset, the accuracy is 85.33%. From various experiments that have been done, it is found that the things that affect accuracy are the number of people, training data, attributes used, lighting, and background. While facial expressions and gender do not prove to have a major influence on the recognition process, with a variety of facial expressions, the PCA method can still recognize faces well.
{"title":"Study Analysis of Human Face Recognition using Principal Component Analysis","authors":"V. Maheswari, C. A. Sari, D. Setiadi, E. H. Rachmawanto","doi":"10.1109/iSemantic50169.2020.9234250","DOIUrl":"https://doi.org/10.1109/iSemantic50169.2020.9234250","url":null,"abstract":"Principal Component Analysis (PCA) is a very popular facial recognition method. This research aims to analyze the PCA method, where various scenarios are tested to look for things that affect the results of recognition using this method. There are three datasets used in the testing phase, namely the private dataset, JAFFE, and Yale. The accuracy produced in the private dataset is 79%, 82%, 86%, and 85.33% with a variety of different scenarios, while in the JAFFE dataset the maximum recognition accuracy is 100% and in the last experiment on the Yale dataset, the accuracy is 85.33%. From various experiments that have been done, it is found that the things that affect accuracy are the number of people, training data, attributes used, lighting, and background. While facial expressions and gender do not prove to have a major influence on the recognition process, with a variety of facial expressions, the PCA method can still recognize faces well.","PeriodicalId":345558,"journal":{"name":"2020 International Seminar on Application for Technology of Information and Communication (iSemantic)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126946363","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 : 2020-09-19DOI: 10.1109/iSemantic50169.2020.9234292
Arif Nugroho, Agustinus Bimo Gumelar, Eko Mulyanto Yuniarno, M. Purnomo
Measurement is a process to find out the object quantity in a certain unit. Accelerometer sensor is an inertial measurement unit that can be used to measure the motion states of certain objects either static or dynamic. The accelerometer as a measurement tool must be reliable and valid in expressing the value. So, the accelerometer must be calibrated first before being used to measure the motion state of the object. In this paper, we propose the polynomial curve fitting method for calibrating the accelerometer sensor. Basically, this accelerometer sensor works based on the Analog to Digital Converter (ADC) principle where it converts the tilt of the sensor to the corresponding voltage. It should be noted that this accelerometer consists of a triple-axis where all of the axes have the same input-output value. Hence, by collecting the data that contains a number of the tilts of the sensor and the corresponding voltages, it is possible to generate the mathematical model that maps the tilts of the accelerometer sensor to the corresponding voltages. From the experiment, we can generate the five-order polynomials model that can be used to predict the new value that approximates the ground-truth value. It can be proved by measuring the Mean Absolute Error (MAE) score of the polynomial curve fitting between the ground-truth value and the prediction value. As a result, the Mean Absolute Error (MAE) score for each of the axes is 0.57. It indicates that our proposed method based on the polynomial curve fitting has been successfully applied for calibrating the accelerometer sensor.
{"title":"Accelerometer Calibration Method Based on Polynomial Curve Fitting","authors":"Arif Nugroho, Agustinus Bimo Gumelar, Eko Mulyanto Yuniarno, M. Purnomo","doi":"10.1109/iSemantic50169.2020.9234292","DOIUrl":"https://doi.org/10.1109/iSemantic50169.2020.9234292","url":null,"abstract":"Measurement is a process to find out the object quantity in a certain unit. Accelerometer sensor is an inertial measurement unit that can be used to measure the motion states of certain objects either static or dynamic. The accelerometer as a measurement tool must be reliable and valid in expressing the value. So, the accelerometer must be calibrated first before being used to measure the motion state of the object. In this paper, we propose the polynomial curve fitting method for calibrating the accelerometer sensor. Basically, this accelerometer sensor works based on the Analog to Digital Converter (ADC) principle where it converts the tilt of the sensor to the corresponding voltage. It should be noted that this accelerometer consists of a triple-axis where all of the axes have the same input-output value. Hence, by collecting the data that contains a number of the tilts of the sensor and the corresponding voltages, it is possible to generate the mathematical model that maps the tilts of the accelerometer sensor to the corresponding voltages. From the experiment, we can generate the five-order polynomials model that can be used to predict the new value that approximates the ground-truth value. It can be proved by measuring the Mean Absolute Error (MAE) score of the polynomial curve fitting between the ground-truth value and the prediction value. As a result, the Mean Absolute Error (MAE) score for each of the axes is 0.57. It indicates that our proposed method based on the polynomial curve fitting has been successfully applied for calibrating the accelerometer sensor.","PeriodicalId":345558,"journal":{"name":"2020 International Seminar on Application for Technology of Information and Communication (iSemantic)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132765636","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}
Following the amount of data and file size, the dimensions of the features can also change, causing heavy usage load on computers by simple multiplication. As technology progressed, we generate clearer sound files, resulting in more High Definition (HD) data with a direct impact on its size. Since many records are critically needed for further analysis, reducing files count and sacrificing clearer sound files is not feasible. In selecting features that best represent humorous speech, we need to implement the Feature Selection (FS) techniques. The FS acts as helpers in computing features with more than ten features/attributes. The purpose of this research is to find the FS technique with the highest accuracy of Random Forest classification, specifically for humorous speech. Unlike the usual FS techniques, we chose to employ the heuristic-based FS techniques, namely, Particle Swarm Optimization, Ant Colony Optimization, Cuckoo Search, and Firefly Algorithm. We applied the FS techniques in WEKA, over their simplification of usage; also jAudio of GUI-based feature extraction for the same reason. Moreover, we used the speech data from the UR-FUNNY dataset, which comprised 10.000 sound clips of both humorous and non-humorous speech by TED Talks speakers.
{"title":"Assessment of Humorous Speech by Automatic Heuristic-based Feature Selection","authors":"Derry Pramono Adi, Agustinus Bimo Gumelar, Ralin Pramasuri Arta Meisa, Siska Susilowati","doi":"10.1109/iSemantic50169.2020.9234228","DOIUrl":"https://doi.org/10.1109/iSemantic50169.2020.9234228","url":null,"abstract":"Following the amount of data and file size, the dimensions of the features can also change, causing heavy usage load on computers by simple multiplication. As technology progressed, we generate clearer sound files, resulting in more High Definition (HD) data with a direct impact on its size. Since many records are critically needed for further analysis, reducing files count and sacrificing clearer sound files is not feasible. In selecting features that best represent humorous speech, we need to implement the Feature Selection (FS) techniques. The FS acts as helpers in computing features with more than ten features/attributes. The purpose of this research is to find the FS technique with the highest accuracy of Random Forest classification, specifically for humorous speech. Unlike the usual FS techniques, we chose to employ the heuristic-based FS techniques, namely, Particle Swarm Optimization, Ant Colony Optimization, Cuckoo Search, and Firefly Algorithm. We applied the FS techniques in WEKA, over their simplification of usage; also jAudio of GUI-based feature extraction for the same reason. Moreover, we used the speech data from the UR-FUNNY dataset, which comprised 10.000 sound clips of both humorous and non-humorous speech by TED Talks speakers.","PeriodicalId":345558,"journal":{"name":"2020 International Seminar on Application for Technology of Information and Communication (iSemantic)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130070848","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 : 2020-09-19DOI: 10.1109/isemantic50169.2020.9234212
{"title":"[Front matter]","authors":"","doi":"10.1109/isemantic50169.2020.9234212","DOIUrl":"https://doi.org/10.1109/isemantic50169.2020.9234212","url":null,"abstract":"","PeriodicalId":345558,"journal":{"name":"2020 International Seminar on Application for Technology of Information and Communication (iSemantic)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127066034","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 : 2020-09-19DOI: 10.1109/iSemantic50169.2020.9234297
Manaris Simanjuntak, Muljono Muljono, G. F. Shidik, A. Zainul Fanani
In every domestic life always has its own problems, and every household must have their own conflict. Problems and conflicts that come in the life of the household can actually be part of the process to mature each other's partners, but sometimes the problems and conflicts that trigger divorce. Dataset from the UCI Repository, an evaluation and improvement process was carried out on the Backpropagation Neural Netwok(BPNN) algorithm and also performed a set of parameters are set and then validated, able to predict whether a married couple will divorce or not with sufficient results. high. And also do a process for some feature selection, then the value or rating of the most significant features will be processed on the Backpropagation Neural Network Algorithm and compare the results of the Gain Ratio, Information Gain, Relief and Correlation feature selection. This model underwent several validation processes so as to achieve a fairly high accuracy by using the Relief feature selection that is 99.41%.
{"title":"Evaluation Of Feature Selection for Improvement Backpropagation Neural Network in Divorce Predictions","authors":"Manaris Simanjuntak, Muljono Muljono, G. F. Shidik, A. Zainul Fanani","doi":"10.1109/iSemantic50169.2020.9234297","DOIUrl":"https://doi.org/10.1109/iSemantic50169.2020.9234297","url":null,"abstract":"In every domestic life always has its own problems, and every household must have their own conflict. Problems and conflicts that come in the life of the household can actually be part of the process to mature each other's partners, but sometimes the problems and conflicts that trigger divorce. Dataset from the UCI Repository, an evaluation and improvement process was carried out on the Backpropagation Neural Netwok(BPNN) algorithm and also performed a set of parameters are set and then validated, able to predict whether a married couple will divorce or not with sufficient results. high. And also do a process for some feature selection, then the value or rating of the most significant features will be processed on the Backpropagation Neural Network Algorithm and compare the results of the Gain Ratio, Information Gain, Relief and Correlation feature selection. This model underwent several validation processes so as to achieve a fairly high accuracy by using the Relief feature selection that is 99.41%.","PeriodicalId":345558,"journal":{"name":"2020 International Seminar on Application for Technology of Information and Communication (iSemantic)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133527902","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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