Pub Date : 2021-10-20DOI: 10.1109/ICTS52701.2021.9609079
S. Arifin, A. S. Aisjah, Ferina Putri Suharsono
Fuzzy logic system (FLS) of Mamdani is a method that has the ability to reason similar to human abilities. In this paper is conduct the modelling of usage m-learning. The model systems is able to measure qualitative categories in modeling the usage of mobile-learning during the Covid-19 pandemic. Fuzzy logic system model for “perceptions of student behavior in usage m-learning”, with 4 variables, i.e. (i) Teacher Readiness-TR, (ii) Student Readiness - SR, (iii) Subjective Norms - NS, and (iv) Intention Behavioral - IB. The four variables are indicators that stated in the question instrument. The fourth variables is input modelling system. Each instrument with a grading answered, i.e.: strongly disagree (SA), disagree (D), neutral (N), agree (A), and strongly agree (SA). The model is structured into two subsystems. Output of sub-system 1 is TR, SR, NS and IB variables, and output of sub-system 2 is “Behavior of Usage m-learning (UB)”. Model system is design in 3 scenarios, to choose the best one. The difference of each scenarios is in the interval variations and number of membership functions of fuzzy logic system. The SLF model was tested on 546 respondents. The fuzzy model in 3 scenarios shows the Mean of Average Percentage error (MAPE) value in the range of 5 - 50%, while the test results using SEM (Structural Equation Modelling) software show the MAPE value is 12%.
{"title":"Modelling Usage M-Learning using Mamdani Fuzzy Logic System in along Covid-19 Pandemic at ITS - Indonesia","authors":"S. Arifin, A. S. Aisjah, Ferina Putri Suharsono","doi":"10.1109/ICTS52701.2021.9609079","DOIUrl":"https://doi.org/10.1109/ICTS52701.2021.9609079","url":null,"abstract":"Fuzzy logic system (FLS) of Mamdani is a method that has the ability to reason similar to human abilities. In this paper is conduct the modelling of usage m-learning. The model systems is able to measure qualitative categories in modeling the usage of mobile-learning during the Covid-19 pandemic. Fuzzy logic system model for “perceptions of student behavior in usage m-learning”, with 4 variables, i.e. (i) Teacher Readiness-TR, (ii) Student Readiness - SR, (iii) Subjective Norms - NS, and (iv) Intention Behavioral - IB. The four variables are indicators that stated in the question instrument. The fourth variables is input modelling system. Each instrument with a grading answered, i.e.: strongly disagree (SA), disagree (D), neutral (N), agree (A), and strongly agree (SA). The model is structured into two subsystems. Output of sub-system 1 is TR, SR, NS and IB variables, and output of sub-system 2 is “Behavior of Usage m-learning (UB)”. Model system is design in 3 scenarios, to choose the best one. The difference of each scenarios is in the interval variations and number of membership functions of fuzzy logic system. The SLF model was tested on 546 respondents. The fuzzy model in 3 scenarios shows the Mean of Average Percentage error (MAPE) value in the range of 5 - 50%, while the test results using SEM (Structural Equation Modelling) software show the MAPE value is 12%.","PeriodicalId":6738,"journal":{"name":"2021 13th International Conference on Information & Communication Technology and System (ICTS)","volume":"104 1","pages":"190-194"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80057283","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 : 2021-10-20DOI: 10.1109/ICTS52701.2021.9608497
Zakiul Fahmi Jailani, P. Verweij, J. T. van der Wal, R. Van Lammeren
Studying tourist interest on the Caribbean island Bonaire might be a good step to improving tourism management. Tourism brought Bonaire economic growth but also puts pressure on the island's natural ecosystem. Previous studies on tourist interest based on surveys are labour-intensive, time-consuming, and expensive. This paper explores whether the use of freely available social media data combined with automatic machine learning methods can function as a cheap and fast alternative to surveys. From 2003 to 2019, 13,706 geotagged Flickr data points assigned keywords, then weighted using TF-IDF (Term Frequency-Inverse Document Frequency), and finally clustered with DB-SCAN (Density-Based Spatial Clustering of Noise Applications). Two factors determine whether a cluster has an associated unique activity/interest: the most relevant and least relevant keywords. Eight identified clusters are useful for interpreting Bonaire tourists' interest: urban tourism; nature tourism around the lake; in-land natural tourism; conch shell and food; unique fishes; windsurf activity; cruise and ship; carnival, parade and singing. Tourism demand was forecasted using both Flickr and CBS (Centraal Bureau voor de Statistiek) data. Flickr data could show which continent the tourist came from in which seasons (Winter, Spring, Summer, Autumn) from 2015 to the end of 2021.
{"title":"A Machine Learning Approach to Study Tourist Interests and Predict Tourism Demand on Bonaire Island from Social Media Data: *Note: This research is based on the internship research report that has already uploaded to www.dcbd.nl","authors":"Zakiul Fahmi Jailani, P. Verweij, J. T. van der Wal, R. Van Lammeren","doi":"10.1109/ICTS52701.2021.9608497","DOIUrl":"https://doi.org/10.1109/ICTS52701.2021.9608497","url":null,"abstract":"Studying tourist interest on the Caribbean island Bonaire might be a good step to improving tourism management. Tourism brought Bonaire economic growth but also puts pressure on the island's natural ecosystem. Previous studies on tourist interest based on surveys are labour-intensive, time-consuming, and expensive. This paper explores whether the use of freely available social media data combined with automatic machine learning methods can function as a cheap and fast alternative to surveys. From 2003 to 2019, 13,706 geotagged Flickr data points assigned keywords, then weighted using TF-IDF (Term Frequency-Inverse Document Frequency), and finally clustered with DB-SCAN (Density-Based Spatial Clustering of Noise Applications). Two factors determine whether a cluster has an associated unique activity/interest: the most relevant and least relevant keywords. Eight identified clusters are useful for interpreting Bonaire tourists' interest: urban tourism; nature tourism around the lake; in-land natural tourism; conch shell and food; unique fishes; windsurf activity; cruise and ship; carnival, parade and singing. Tourism demand was forecasted using both Flickr and CBS (Centraal Bureau voor de Statistiek) data. Flickr data could show which continent the tourist came from in which seasons (Winter, Spring, Summer, Autumn) from 2015 to the end of 2021.","PeriodicalId":6738,"journal":{"name":"2021 13th International Conference on Information & Communication Technology and System (ICTS)","volume":"1 1","pages":"173-178"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77648707","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 : 2021-10-20DOI: 10.1109/ICTS52701.2021.9608955
Irfanur Ilham Febriansyah, Whika Cahyo Saputro, Galih Ridha Achmadi, Fadila Arisha, Dara Tursina, B. Pratomo, A. M. Shiddiqi
Wireless Sensor Network (WSN) has been used in the industrial world and the household. The increasing number of WSN-based smart home devices requires intensive monitoring and automation. Problems may arise when a fault occurs on these devices that result in misinterpretation of the data received. Existing approaches to fault detection and diagnosis have led to the development of fault diagnosis methods for large-scale data. One of the effective methods for fault diagnosis is the Multi-Scale Principal Component Analysis (MSPCA). This research implements a combination of MSPCA and Decision Tree to detect fault data and diagnose the type of fault cause. The classification of faults is based on significant changes in temperature, humidity, light, voltage, as measured from the Normal Profile extracted by the MSPCA. Experiment results showed that our method was able to determine faults with an accuracy score of 0.913.
{"title":"Outlier Detection and Decision Tree for Wireless Sensor Network Fault Diagnosis","authors":"Irfanur Ilham Febriansyah, Whika Cahyo Saputro, Galih Ridha Achmadi, Fadila Arisha, Dara Tursina, B. Pratomo, A. M. Shiddiqi","doi":"10.1109/ICTS52701.2021.9608955","DOIUrl":"https://doi.org/10.1109/ICTS52701.2021.9608955","url":null,"abstract":"Wireless Sensor Network (WSN) has been used in the industrial world and the household. The increasing number of WSN-based smart home devices requires intensive monitoring and automation. Problems may arise when a fault occurs on these devices that result in misinterpretation of the data received. Existing approaches to fault detection and diagnosis have led to the development of fault diagnosis methods for large-scale data. One of the effective methods for fault diagnosis is the Multi-Scale Principal Component Analysis (MSPCA). This research implements a combination of MSPCA and Decision Tree to detect fault data and diagnose the type of fault cause. The classification of faults is based on significant changes in temperature, humidity, light, voltage, as measured from the Normal Profile extracted by the MSPCA. Experiment results showed that our method was able to determine faults with an accuracy score of 0.913.","PeriodicalId":6738,"journal":{"name":"2021 13th International Conference on Information & Communication Technology and System (ICTS)","volume":"15 1","pages":"56-61"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72707176","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 : 2021-10-20DOI: 10.1109/ICTS52701.2021.9608322
Dhena Kamalia Fu'adi, A. Hidayanto, D. I. Inan, K. Phusavat
The rapid change in technology has turned the interaction between the customer and e-commerce application into more realistically. One of the advanced technologies in e-commerce is Augmented Reality (AR). The implementation of AR in e-commerce has been vast and diverse. One of these is to help customizing products based on customer needs. In understanding the extent of implementation for customization in AR e-commerce and its limitations, a systematic literature review was carried out from previous papers. From five paper databases whose publication dates range from 2012 to 2021, 32 papers discuss AR customization in e-commerce. The explanation of this result is divided into six research objectives, such as customer experience, behavioral response, purchase intention, adoption and acceptance, brand love, and attitude toward risk. In this paper, the explanation of customization in AR e-commerce will be divided into the implementation and future works.
{"title":"The Implementation of Augmented Reality in E-Commerce Customization: A Systematic Literature Review","authors":"Dhena Kamalia Fu'adi, A. Hidayanto, D. I. Inan, K. Phusavat","doi":"10.1109/ICTS52701.2021.9608322","DOIUrl":"https://doi.org/10.1109/ICTS52701.2021.9608322","url":null,"abstract":"The rapid change in technology has turned the interaction between the customer and e-commerce application into more realistically. One of the advanced technologies in e-commerce is Augmented Reality (AR). The implementation of AR in e-commerce has been vast and diverse. One of these is to help customizing products based on customer needs. In understanding the extent of implementation for customization in AR e-commerce and its limitations, a systematic literature review was carried out from previous papers. From five paper databases whose publication dates range from 2012 to 2021, 32 papers discuss AR customization in e-commerce. The explanation of this result is divided into six research objectives, such as customer experience, behavioral response, purchase intention, adoption and acceptance, brand love, and attitude toward risk. In this paper, the explanation of customization in AR e-commerce will be divided into the implementation and future works.","PeriodicalId":6738,"journal":{"name":"2021 13th International Conference on Information & Communication Technology and System (ICTS)","volume":"69 1","pages":"12-17"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83196971","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 : 2021-10-20DOI: 10.1109/ICTS52701.2021.9608400
Arjonel M. Mendoza, Rowell M. Hernandez
The thyroid gland plays one of the most important organs in the human body. It secretes thyroid hormones, which regulate metabolism. Hypothyroidism and hyperthyroidism are caused by either too little or too much thyroid hormone secretion. This study assesses and analyzes existing data mining methods for diagnosing thyroid diseases. This paper aims to provide and identify the best practices in terms of applying data mining techniques such as decision tree, k-nearest neighbor, SVM, PNN, various Thyroid ailments which include the best machine learning model, naive Bayes, etc. Also, this research evaluates the preliminary techniques used to diagnose various thyroid diseases based on their efficacy and the number of attributes under the evaluation matrix. The attributes Age, sex, TSH, T3, TBG, T4U, TT4, and FTI were determined to be the most commonly used medical attributes in previous research works to perform experimental work to diagnose thyroid disorders. Almost every researcher has utilized one or more of these features to perform thyroid disease diagnostic work. According to the results of this study, there is a relationship between the number of attributes used and the accuracy rate achieved; The noticeable results that were presented in this study are some models are higher with fewer feature attributes while with the advent of the neural networks, the higher that number of attributes can give a better performance of classification. This area could be explored by considering adding and using more features to provide a more accurate and reliable output that can be a baseline for development.
{"title":"Application of Data Mining Techniques in Diagnosing Various Thyroid Ailments: A Review","authors":"Arjonel M. Mendoza, Rowell M. Hernandez","doi":"10.1109/ICTS52701.2021.9608400","DOIUrl":"https://doi.org/10.1109/ICTS52701.2021.9608400","url":null,"abstract":"The thyroid gland plays one of the most important organs in the human body. It secretes thyroid hormones, which regulate metabolism. Hypothyroidism and hyperthyroidism are caused by either too little or too much thyroid hormone secretion. This study assesses and analyzes existing data mining methods for diagnosing thyroid diseases. This paper aims to provide and identify the best practices in terms of applying data mining techniques such as decision tree, k-nearest neighbor, SVM, PNN, various Thyroid ailments which include the best machine learning model, naive Bayes, etc. Also, this research evaluates the preliminary techniques used to diagnose various thyroid diseases based on their efficacy and the number of attributes under the evaluation matrix. The attributes Age, sex, TSH, T3, TBG, T4U, TT4, and FTI were determined to be the most commonly used medical attributes in previous research works to perform experimental work to diagnose thyroid disorders. Almost every researcher has utilized one or more of these features to perform thyroid disease diagnostic work. According to the results of this study, there is a relationship between the number of attributes used and the accuracy rate achieved; The noticeable results that were presented in this study are some models are higher with fewer feature attributes while with the advent of the neural networks, the higher that number of attributes can give a better performance of classification. This area could be explored by considering adding and using more features to provide a more accurate and reliable output that can be a baseline for development.","PeriodicalId":6738,"journal":{"name":"2021 13th International Conference on Information & Communication Technology and System (ICTS)","volume":"70 1","pages":"207-212"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90658325","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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