Pub Date : 2020-09-19DOI: 10.1109/iSemantic50169.2020.9234289
Hanifah Dwindasari, R. Sarno
The majority of Indonesia’s population are Muslim, hence, the market for Sharia banking should be more dominant than conventional banking. However, the market share of Sharia banking in Indonesia is still relatively small, i.e. less than 8% of the total population. Some studies have found that awareness of Sharia banking among Muslims is high but the importance of using the product is low. The purpose of the present study is to find out potential user interest in Sharia banks, more specifically the Sharia Bank, by investigating the relationship between behavioral intention and two control variables as well as a number of latent variables that are affected most. These variables describe behavioral intention and use behavior. The result shows that high significant variables to be influential of behavioral intention for the age groups 21-30 years, 31-40 years and >40 years are perceived trust and perceived risk. Women aged >40 years are more interested than other age groups. The results obtained can help Sharia banks in Indonesia to improve strategies in the market share.
{"title":"Analysing Public Interest in Sharia Banking Using Utaut2 Method","authors":"Hanifah Dwindasari, R. Sarno","doi":"10.1109/iSemantic50169.2020.9234289","DOIUrl":"https://doi.org/10.1109/iSemantic50169.2020.9234289","url":null,"abstract":"The majority of Indonesia’s population are Muslim, hence, the market for Sharia banking should be more dominant than conventional banking. However, the market share of Sharia banking in Indonesia is still relatively small, i.e. less than 8% of the total population. Some studies have found that awareness of Sharia banking among Muslims is high but the importance of using the product is low. The purpose of the present study is to find out potential user interest in Sharia banks, more specifically the Sharia Bank, by investigating the relationship between behavioral intention and two control variables as well as a number of latent variables that are affected most. These variables describe behavioral intention and use behavior. The result shows that high significant variables to be influential of behavioral intention for the age groups 21-30 years, 31-40 years and >40 years are perceived trust and perceived risk. Women aged >40 years are more interested than other age groups. The results obtained can help Sharia banks in Indonesia to improve strategies in the market share.","PeriodicalId":345558,"journal":{"name":"2020 International Seminar on Application for Technology of Information and Communication (iSemantic)","volume":"8 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":"121319157","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/iSemantic50169.2020.9234291
Saeful Fahmi, Lia Purnamawati, G. F. Shidik, Muljono Muljono, A. Z. Fanani
In the learning management system, there are reviews from students of the learning process that has been done in a period. In this case, we use the review dataset to conduct sentiment analysis. The challenge of this dataset is the number of words that contain abbreviations and are not standard. So it challenges us to test the level of accuracy in the sentiment analysis process using several classification methods and sastrawi stemmer. Sastrawi stemmer is used to reduce features without changing the meaning data, Basic function of sastrawi is change words in the basic and eliminate nonessential or non-standard words with filtering concept. In the classification process, we use the SVM-PSO algorithm and compare it with other popular classification methods such as SVM, Naive Bayes and KNN. SVM-PSO is a combination of algorithms that is good to handle data with large dimensions and binary classification types. This is our reason for using SVM-PSO as the main classifer. Experimental results show that the use of sastrawi stemmer can reduce features by 32.58%. The accuracy of the classification process using SVM-PSO of 82.27% (with sastrawi stemmer) and 82.09% (without sastrawi stemmer), these results indicate that sastrawi stemmer influences the results of classification. SVM-PSO classification method has the highest level of accuracy compared to other classification methods, namely Naive Bayes gets an accuracy of 69.73%, K-NN gets an accuracy of 77.67% and SVM gets an accuracy of 81.52%. Based on the experimental results, SVM-PSO method has the best accuracy than any other method, and Sastrawi stemmer influences the level of accuracy.
{"title":"Sentiment Analysis of Student Review in Learning Management System Based on Sastrawi Stemmer and SVM-PSO","authors":"Saeful Fahmi, Lia Purnamawati, G. F. Shidik, Muljono Muljono, A. Z. Fanani","doi":"10.1109/iSemantic50169.2020.9234291","DOIUrl":"https://doi.org/10.1109/iSemantic50169.2020.9234291","url":null,"abstract":"In the learning management system, there are reviews from students of the learning process that has been done in a period. In this case, we use the review dataset to conduct sentiment analysis. The challenge of this dataset is the number of words that contain abbreviations and are not standard. So it challenges us to test the level of accuracy in the sentiment analysis process using several classification methods and sastrawi stemmer. Sastrawi stemmer is used to reduce features without changing the meaning data, Basic function of sastrawi is change words in the basic and eliminate nonessential or non-standard words with filtering concept. In the classification process, we use the SVM-PSO algorithm and compare it with other popular classification methods such as SVM, Naive Bayes and KNN. SVM-PSO is a combination of algorithms that is good to handle data with large dimensions and binary classification types. This is our reason for using SVM-PSO as the main classifer. Experimental results show that the use of sastrawi stemmer can reduce features by 32.58%. The accuracy of the classification process using SVM-PSO of 82.27% (with sastrawi stemmer) and 82.09% (without sastrawi stemmer), these results indicate that sastrawi stemmer influences the results of classification. SVM-PSO classification method has the highest level of accuracy compared to other classification methods, namely Naive Bayes gets an accuracy of 69.73%, K-NN gets an accuracy of 77.67% and SVM gets an accuracy of 81.52%. Based on the experimental results, SVM-PSO method has the best accuracy than any other method, and Sastrawi stemmer influences the level of accuracy.","PeriodicalId":345558,"journal":{"name":"2020 International Seminar on Application for Technology of Information and Communication (iSemantic)","volume":"255 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":"121324066","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/iSemantic50169.2020.9234220
Nafa Zulfa, Dini Yuniasri, Putri Damayanti, D. Herumurti, A. Yunanto
User Interface (UI) and User Experience (UX) are aspects that cannot be separated from video game development. In this study, we have improved the UI and UX of the Bomberman game and compared it to the UI and UX of the original Bomberman game. In evaluating the UI and UX results, we use the Game Experience Questionnaire Method (GEQ). There are six aspects used in the GEQ method, i.e., aspects of the challenge, competition, immersion, playfulness, social experiment, and enjoyment. We use 65 respondents to rate the UI and UX of the original Bomberman game. The questionnaire's results were taken into consideration in improving the UI and UX of the game to be developed. GEQ also provided to get the result of the improvement game. Likert scale calculation concluded that the 62 respondents rated the success of the Bomberman game development with UI and UX that has been improved. The 62 respondents indicated that the UI and UX of games provide more fun and enjoyable than the original Bomberman game.
{"title":"The Effect of UI and UX Enhancement on Bomberman Game Based on Game Experience Questionnaire (GEQ)","authors":"Nafa Zulfa, Dini Yuniasri, Putri Damayanti, D. Herumurti, A. Yunanto","doi":"10.1109/iSemantic50169.2020.9234220","DOIUrl":"https://doi.org/10.1109/iSemantic50169.2020.9234220","url":null,"abstract":"User Interface (UI) and User Experience (UX) are aspects that cannot be separated from video game development. In this study, we have improved the UI and UX of the Bomberman game and compared it to the UI and UX of the original Bomberman game. In evaluating the UI and UX results, we use the Game Experience Questionnaire Method (GEQ). There are six aspects used in the GEQ method, i.e., aspects of the challenge, competition, immersion, playfulness, social experiment, and enjoyment. We use 65 respondents to rate the UI and UX of the original Bomberman game. The questionnaire's results were taken into consideration in improving the UI and UX of the game to be developed. GEQ also provided to get the result of the improvement game. Likert scale calculation concluded that the 62 respondents rated the success of the Bomberman game development with UI and UX that has been improved. The 62 respondents indicated that the UI and UX of games provide more fun and enjoyable than the original Bomberman game.","PeriodicalId":345558,"journal":{"name":"2020 International Seminar on Application for Technology of Information and Communication (iSemantic)","volume":"46 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":"127513968","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/iSemantic50169.2020.9234222
De Rosal Ignatius Moses Setiadi, Dewangga Satriya Rahardwika, E. H. Rachmawanto, Christy Atika Sari, A. Susanto, Ibnu Utomo Wahyu Mulyono, Erna Zuni Astuti, A. Fahmi
This research aims to analyze the effect of feature selection on the accuracy of music genre classification using support vector machine with radial basis function kernel as a classifier. In this research, the music dataset from Spotify is used, which is one of the best-selling music streaming platforms today. The selected feature is metadata because it is considered to have simpler processing than audio feature extraction. The music contained in the Spotify dataset also has complete metadata so that the metadata feature can be used properly. At the feature selection stage, some features are combined in different combination groups (FC1, FC2, FC3, FC4). The classification results prove each feature combination has an accuracy result that has a significant difference, where the best accuracy is 80% and the lowest is 67%. Where the combination of FC1 and FC2 features produces the same accuracy of 80%, but because FC2 has a smaller number of features, so the FC2 combination is recommended because with fewer features, so logically the computing time is shorter.
{"title":"Effect of Feature Selection on The Accuracy of Music Genre Classification using SVM Classifier","authors":"De Rosal Ignatius Moses Setiadi, Dewangga Satriya Rahardwika, E. H. Rachmawanto, Christy Atika Sari, A. Susanto, Ibnu Utomo Wahyu Mulyono, Erna Zuni Astuti, A. Fahmi","doi":"10.1109/iSemantic50169.2020.9234222","DOIUrl":"https://doi.org/10.1109/iSemantic50169.2020.9234222","url":null,"abstract":"This research aims to analyze the effect of feature selection on the accuracy of music genre classification using support vector machine with radial basis function kernel as a classifier. In this research, the music dataset from Spotify is used, which is one of the best-selling music streaming platforms today. The selected feature is metadata because it is considered to have simpler processing than audio feature extraction. The music contained in the Spotify dataset also has complete metadata so that the metadata feature can be used properly. At the feature selection stage, some features are combined in different combination groups (FC1, FC2, FC3, FC4). The classification results prove each feature combination has an accuracy result that has a significant difference, where the best accuracy is 80% and the lowest is 67%. Where the combination of FC1 and FC2 features produces the same accuracy of 80%, but because FC2 has a smaller number of features, so the FC2 combination is recommended because with fewer features, so logically the computing time is shorter.","PeriodicalId":345558,"journal":{"name":"2020 International Seminar on Application for Technology of Information and Communication (iSemantic)","volume":"91 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":"126178146","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/iSemantic50169.2020.9234265
T. Sutojo, A. Syukur, Supriadi Rustad, Guruh Fajar Shidik, Heru Agus Santoso, Purwanto Purwanto, Muljono Muljono
Machine learning is widely used in various fields, its ability to study data without having to determine the functional relationships that govern a system. However, small datasets often make it difficult for learning algorithms to make accurate predictions. To overcome this, an oversampling technique is needed. However, for the regression learning model this is not easy to do, because in regression to place synthesis data in a certain feature space must be accompanied by an appropriate target value, usually represented by an estimate function. Therefore in this paper oversampling is done by distributing synthetic data according to the Bus, Star, and Mesh topology, using the SMOTE (Synthetic Minority Over-sampling Technique) method. In the experiment, one of the ISE (Istanbul Stock Exchange) public datasets and one of the CF (Color Filter) real datasets were tested to measure the performance of the proposed oversampling technique. Besides, the results of experiments conducted on the same dataset using the MPV, FCM, and MMPV methods were used as a comparison. The results show that oversampling using the Bus, Star, or Mesh distribution results in better performance than without using oversampling. The ISE dataset tested using the proposed method has an average RMSE value smaller than the MPV, FCM, and MMPV methods. For CF datasets, the proposed method has an average RMSE value smaller than the MPV, FCM, and MMPV methods when the amount of training data is smaller than the amount of testing data.
{"title":"Investigating the Impact of Synthetic Data Distribution on the Performance of Regression Models to Overcome Small Dataset Problems","authors":"T. Sutojo, A. Syukur, Supriadi Rustad, Guruh Fajar Shidik, Heru Agus Santoso, Purwanto Purwanto, Muljono Muljono","doi":"10.1109/iSemantic50169.2020.9234265","DOIUrl":"https://doi.org/10.1109/iSemantic50169.2020.9234265","url":null,"abstract":"Machine learning is widely used in various fields, its ability to study data without having to determine the functional relationships that govern a system. However, small datasets often make it difficult for learning algorithms to make accurate predictions. To overcome this, an oversampling technique is needed. However, for the regression learning model this is not easy to do, because in regression to place synthesis data in a certain feature space must be accompanied by an appropriate target value, usually represented by an estimate function. Therefore in this paper oversampling is done by distributing synthetic data according to the Bus, Star, and Mesh topology, using the SMOTE (Synthetic Minority Over-sampling Technique) method. In the experiment, one of the ISE (Istanbul Stock Exchange) public datasets and one of the CF (Color Filter) real datasets were tested to measure the performance of the proposed oversampling technique. Besides, the results of experiments conducted on the same dataset using the MPV, FCM, and MMPV methods were used as a comparison. The results show that oversampling using the Bus, Star, or Mesh distribution results in better performance than without using oversampling. The ISE dataset tested using the proposed method has an average RMSE value smaller than the MPV, FCM, and MMPV methods. For CF datasets, the proposed method has an average RMSE value smaller than the MPV, FCM, and MMPV methods when the amount of training data is smaller than the amount of testing data.","PeriodicalId":345558,"journal":{"name":"2020 International Seminar on Application for Technology of Information and Communication (iSemantic)","volume":"1 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":"125331206","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/iSemantic50169.2020.9234303
Luthfi Atikah, Novrindah Alvi Hasanah, R. Sarno, Aziz Fajar, Dewi Rahmawati
Nowadays, many methods have been applied for brain segmentation on MRI data. This paper proposes a new method for brain segmentation using Adaptive Thresholding, K-Means Clustering, and Morphological Mathematics in MRI data. The adaptive threshold was chosen because the adaptive threshold method will vary across images to suit various lighting conditions and background changes. We segment the corpus callosum. This experiment shows that with the Adaptive Thresholding, K-Means Clustering, and Mathematical Morphology to segment the corpus callosum produces the highest Dice Similarity Coefficient (DSC) value of 0.757.
{"title":"Brain Segmentation using Adaptive Thresholding, K-Means Clustering and Mathematical Morphology in MRI Data","authors":"Luthfi Atikah, Novrindah Alvi Hasanah, R. Sarno, Aziz Fajar, Dewi Rahmawati","doi":"10.1109/iSemantic50169.2020.9234303","DOIUrl":"https://doi.org/10.1109/iSemantic50169.2020.9234303","url":null,"abstract":"Nowadays, many methods have been applied for brain segmentation on MRI data. This paper proposes a new method for brain segmentation using Adaptive Thresholding, K-Means Clustering, and Morphological Mathematics in MRI data. The adaptive threshold was chosen because the adaptive threshold method will vary across images to suit various lighting conditions and background changes. We segment the corpus callosum. This experiment shows that with the Adaptive Thresholding, K-Means Clustering, and Mathematical Morphology to segment the corpus callosum produces the highest Dice Similarity Coefficient (DSC) value of 0.757.","PeriodicalId":345558,"journal":{"name":"2020 International Seminar on Application for Technology of Information and Communication (iSemantic)","volume":"3 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":"114464198","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/iSemantic50169.2020.9234302
A. M. Nidhom, Azhar Ahmad Smaragdina, A. Putra, H. A. Syafrudie, H. Suswanto, Setiadi Cahyono Putro
This study aims at: (1) creating an intelligent application component framework for heutagogy; (2) developing the five essential elements of heutagogy, namely exploring, creating, collaborating, connecting and reflecting; (3) testing the effectiveness and efficiency of the heutagogy framework; and, lastly, (4) calculating the effect of all components on Indonesian engineering students. In this study, 300 engineering students from Universitas Negeri Malang (UM), Indonesia, were used to sample. Analysis of data using a descriptive approach using SPSS 24. The results of this study: (1) the basic structure for smart heutagogy applications in the known heutagogy components is the 45% simple UX design and UX component; (2) 5 essential elements have a fairly good percentage, namely reflect (90%), followed by connect (89.6%), create (88.7%), share (87.8%), collaborate (86.87) %) and explore (84.78%), all components have an average (88.7%) have a good framework for heutagogy applications; (3) The level of effectiveness and efficiency of the heutagogy framework is in the good category, ie from the instrument at 74% this allows the component to be embedded in the application; (4) The results of the flexibility test of all components are also at good intervals because they are at intervals > 5 of the Likert scale and the t test yields a significance of 0.538 which proves that all components affect the heutagogy framework of Engineering students in Indonesia.
{"title":"Framework for Intelligent Application Heutagogy Based Education 3.0 and Lesson Study Components for VHS students","authors":"A. M. Nidhom, Azhar Ahmad Smaragdina, A. Putra, H. A. Syafrudie, H. Suswanto, Setiadi Cahyono Putro","doi":"10.1109/iSemantic50169.2020.9234302","DOIUrl":"https://doi.org/10.1109/iSemantic50169.2020.9234302","url":null,"abstract":"This study aims at: (1) creating an intelligent application component framework for heutagogy; (2) developing the five essential elements of heutagogy, namely exploring, creating, collaborating, connecting and reflecting; (3) testing the effectiveness and efficiency of the heutagogy framework; and, lastly, (4) calculating the effect of all components on Indonesian engineering students. In this study, 300 engineering students from Universitas Negeri Malang (UM), Indonesia, were used to sample. Analysis of data using a descriptive approach using SPSS 24. The results of this study: (1) the basic structure for smart heutagogy applications in the known heutagogy components is the 45% simple UX design and UX component; (2) 5 essential elements have a fairly good percentage, namely reflect (90%), followed by connect (89.6%), create (88.7%), share (87.8%), collaborate (86.87) %) and explore (84.78%), all components have an average (88.7%) have a good framework for heutagogy applications; (3) The level of effectiveness and efficiency of the heutagogy framework is in the good category, ie from the instrument at 74% this allows the component to be embedded in the application; (4) The results of the flexibility test of all components are also at good intervals because they are at intervals > 5 of the Likert scale and the t test yields a significance of 0.538 which proves that all components affect the heutagogy framework of Engineering students in Indonesia.","PeriodicalId":345558,"journal":{"name":"2020 International Seminar on Application for Technology of Information and Communication (iSemantic)","volume":"1 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":"128584617","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/iSemantic50169.2020.9234279
Dwi Sunaryono, S. Rochimah, R. Sarno, S. Sabilla, Irzal Ahmad Sabilla, Dewi Sekarini
Issues related to the use of artificial additives in food have become one of the point of discussions recently. One of the additives which is widely discussed is borax. The use of high doses of borax can cause serious diseases. Meatballs are one among many foods that is often found to contain borax. Irresponsible sellers added borax to the meatball mixture in order to have a chewier texture, more durable, and more attractive in terms of color. A medium is indeed needed to provide information about whether or not places selling meatballs may contain borax. Therefore, an application to detect the location of meatball sellers which may contain borax is proposed as a solution to the above problem. By utilizing the electronic nose technology, a device which can identify the components based on the odor. The content of borax is expected to be detected by utilizing one of the characteristics of the borax-contained meatball. In addition, this application utilizes the geotagging feature that can provide information on the location of meatball sellers which may use borax. This study was successfully implemented with an accuracy of 90% and a confidence level of 91%.
{"title":"Geotagging for Mapping Distribution of Meatballs Containing Borax based on Electronic Nose Detection","authors":"Dwi Sunaryono, S. Rochimah, R. Sarno, S. Sabilla, Irzal Ahmad Sabilla, Dewi Sekarini","doi":"10.1109/iSemantic50169.2020.9234279","DOIUrl":"https://doi.org/10.1109/iSemantic50169.2020.9234279","url":null,"abstract":"Issues related to the use of artificial additives in food have become one of the point of discussions recently. One of the additives which is widely discussed is borax. The use of high doses of borax can cause serious diseases. Meatballs are one among many foods that is often found to contain borax. Irresponsible sellers added borax to the meatball mixture in order to have a chewier texture, more durable, and more attractive in terms of color. A medium is indeed needed to provide information about whether or not places selling meatballs may contain borax. Therefore, an application to detect the location of meatball sellers which may contain borax is proposed as a solution to the above problem. By utilizing the electronic nose technology, a device which can identify the components based on the odor. The content of borax is expected to be detected by utilizing one of the characteristics of the borax-contained meatball. In addition, this application utilizes the geotagging feature that can provide information on the location of meatball sellers which may use borax. This study was successfully implemented with an accuracy of 90% and a confidence level of 91%.","PeriodicalId":345558,"journal":{"name":"2020 International Seminar on Application for Technology of Information and Communication (iSemantic)","volume":"58 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":"124670866","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/iSemantic50169.2020.9234266
Faiz Naufal Fadhlurrohman, Nurul Anisa Sri Winarsih, M. S. Rohman, Galuh Wilujeng Saraswati
In 1970, the world overcame the special energy crisis of petroleum. However, since then many people have used solar energy as an alternative energy source. As an alternative source, the use of solar power systems can easily spread throughout the world because of its low maintenance and ease of deployment. But the use of solar cells that have been integrated with the user’s will is not widely available, for example, the use of solar cells that are integrated with smartphones. In connection with this, it will be very easy if the process of monitoring or monitoring the use of solar panel power and the application of smart home technology is done with a computerized system, for example by using an Android-based application. When interacting with the Solar Panel Monitoring Application on Android Smartphones the user must get the same comfort by his experience using other systems. This writing aims to design a Solar Panel Monitoring Application system on android smartphones using the User-Centered Design (UCD) method. The user as the center of the design system development process is called the User-Centered Design (UCD) design philosophy. From the 8 parameters of usability goals and user experience, the average percentage stage is 81%. It means that the design of the system is good.
{"title":"User Interface Design for Solar Panel Monitoring System on Android Smartphones Using User-Centered Design Method","authors":"Faiz Naufal Fadhlurrohman, Nurul Anisa Sri Winarsih, M. S. Rohman, Galuh Wilujeng Saraswati","doi":"10.1109/iSemantic50169.2020.9234266","DOIUrl":"https://doi.org/10.1109/iSemantic50169.2020.9234266","url":null,"abstract":"In 1970, the world overcame the special energy crisis of petroleum. However, since then many people have used solar energy as an alternative energy source. As an alternative source, the use of solar power systems can easily spread throughout the world because of its low maintenance and ease of deployment. But the use of solar cells that have been integrated with the user’s will is not widely available, for example, the use of solar cells that are integrated with smartphones. In connection with this, it will be very easy if the process of monitoring or monitoring the use of solar panel power and the application of smart home technology is done with a computerized system, for example by using an Android-based application. When interacting with the Solar Panel Monitoring Application on Android Smartphones the user must get the same comfort by his experience using other systems. This writing aims to design a Solar Panel Monitoring Application system on android smartphones using the User-Centered Design (UCD) method. The user as the center of the design system development process is called the User-Centered Design (UCD) design philosophy. From the 8 parameters of usability goals and user experience, the average percentage stage is 81%. It means that the design of the system is good.","PeriodicalId":345558,"journal":{"name":"2020 International Seminar on Application for Technology of Information and Communication (iSemantic)","volume":"18 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":"130495781","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/iSemantic50169.2020.9234299
Resty Alfyani, Muljono
The heart is the most important organ for humans. The liver functions to neutralize toxins that are in the blood and regulate the composition of blood that contains fat, protein, sugar and other substances. The Hepatitis is the disease that attacks the liver caused by a virus. Hepatitis can be known by holding a laboratory test on the blood. The development of technology and information on hepatitis can be known by the classification and prediction methods. The purpose of this study was to improve the accuracy of the classification of naïve Bayes and KNN algorithms by taking public data from the UCI Repository with total of 155 data, having 19 attributes owned such as Age, Gender, Steroids, Antivirus, Fatigue, Malaise, Anorexia, Big Heart, Heart Company, Spleen, Spiders, Ascites, Varicose, Bilirubin, Alk Phosphate, Shot, Albumin, Protime, Histology, and Class (predictive attribute). Experiments use the confusion matrix to determine the value of accuracy, precision, and recall. The results obtained in experiments using Naïve Bayes algorithm are the level of accuracy of 74.19% and the average level of error 25.81% higher than the K-Nearest Neighbor algorithm the average value is 54.84% and the level of value an average error of 45.18%. From the results obtained that the K-Nearest Neighbor algorithm increases the value of accuracy and the average value of errors from previous studies.
{"title":"Comparison of Naïve Bayes and KNN Algorithms to understand Hepatitis","authors":"Resty Alfyani, Muljono","doi":"10.1109/iSemantic50169.2020.9234299","DOIUrl":"https://doi.org/10.1109/iSemantic50169.2020.9234299","url":null,"abstract":"The heart is the most important organ for humans. The liver functions to neutralize toxins that are in the blood and regulate the composition of blood that contains fat, protein, sugar and other substances. The Hepatitis is the disease that attacks the liver caused by a virus. Hepatitis can be known by holding a laboratory test on the blood. The development of technology and information on hepatitis can be known by the classification and prediction methods. The purpose of this study was to improve the accuracy of the classification of naïve Bayes and KNN algorithms by taking public data from the UCI Repository with total of 155 data, having 19 attributes owned such as Age, Gender, Steroids, Antivirus, Fatigue, Malaise, Anorexia, Big Heart, Heart Company, Spleen, Spiders, Ascites, Varicose, Bilirubin, Alk Phosphate, Shot, Albumin, Protime, Histology, and Class (predictive attribute). Experiments use the confusion matrix to determine the value of accuracy, precision, and recall. The results obtained in experiments using Naïve Bayes algorithm are the level of accuracy of 74.19% and the average level of error 25.81% higher than the K-Nearest Neighbor algorithm the average value is 54.84% and the level of value an average error of 45.18%. From the results obtained that the K-Nearest Neighbor algorithm increases the value of accuracy and the average value of errors from previous studies.","PeriodicalId":345558,"journal":{"name":"2020 International Seminar on Application for Technology of Information and Communication (iSemantic)","volume":"399 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120880607","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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