Pub Date : 2020-10-21DOI: 10.1109/ICSITech49800.2020.9392035
G. Indrawan, K. Setemen, Wayan Sutaya, I. K. Paramarta
This study is aimed to preserve part of Balinese local culture that is endangered, i.e. Latin-to-Balinese Script transliteration knowledge through the web technology that support ubiquitous learning. It analyzed handling of line breaking on Latin-to-Balinese Script transliteration web application since there has not been studied yet on no-word boundaries (scriptio continua) script. There are two rules of thumb should be applied: 1) No line breaking is allowed between syllable-sign cluster or consonant-vowel cluster; and 2) No line breaking is allowed just before a colon, comma, or period. This research added certain perspective and strengthened the transliteration knowledge, as part Balinese Language ubiquitous learning that support Balinese Language education, which is a mandatory local subject from basic to high school in Bali Province. This analysis was conducted on Hanacaraka web application that was developed as a technological product of Universitas Pendidikan Ganesha (Undiksha), Indonesia. Hanacaraka basically receives Latin text input and outputs Balinese Script based on Bali Simbar font. Through the experiment, its handling on line breaking gave good transliteration result since special algorithm was applied. Future work to enrich and strengthen the transliteration knowledge is by extending the handling of line breaking on Latin-to-Balinese Script web application that was supported by dedicated Balinese-Unicode font.
本研究旨在透过支持泛在学习的网络技术，保护部分濒临灭绝的巴厘本土文化，即拉丁-巴厘文字转写知识。针对目前没有对无字边界(scriptio continua)脚本进行研究的情况，分析了拉丁-巴厘文字转写web应用程序对断行的处理。这里有两条经验法则:1)音节-符号集群或辅音-元音集群之间不允许断行;2)冒号、逗号或句号前不允许断行。本研究增加了一定的视角，强化了音译知识，作为支持巴厘语教育的巴厘语泛在学习的一部分。巴厘语教育是巴厘省从小学到高中的地方必修科目。该分析是在Hanacaraka网络应用程序上进行的，该应用程序是作为印度尼西亚Universitas Pendidikan Ganesha (Undiksha)的技术产品开发的。Hanacaraka基本上接收拉丁文本输入，并根据巴厘辛巴字体输出巴厘文字。通过实验，由于采用了特殊的算法，其对断行的处理取得了良好的音译效果。未来丰富和加强音译知识的工作是扩展由专用的巴厘unicode字体支持的latin -to- bali Script web应用程序的断行处理。
{"title":"Handling of Line Breaking on Latin-to-Balinese Script Transliteration Web Application as Part of Balinese Language Ubiquitous Learning","authors":"G. Indrawan, K. Setemen, Wayan Sutaya, I. K. Paramarta","doi":"10.1109/ICSITech49800.2020.9392035","DOIUrl":"https://doi.org/10.1109/ICSITech49800.2020.9392035","url":null,"abstract":"This study is aimed to preserve part of Balinese local culture that is endangered, i.e. Latin-to-Balinese Script transliteration knowledge through the web technology that support ubiquitous learning. It analyzed handling of line breaking on Latin-to-Balinese Script transliteration web application since there has not been studied yet on no-word boundaries (scriptio continua) script. There are two rules of thumb should be applied: 1) No line breaking is allowed between syllable-sign cluster or consonant-vowel cluster; and 2) No line breaking is allowed just before a colon, comma, or period. This research added certain perspective and strengthened the transliteration knowledge, as part Balinese Language ubiquitous learning that support Balinese Language education, which is a mandatory local subject from basic to high school in Bali Province. This analysis was conducted on Hanacaraka web application that was developed as a technological product of Universitas Pendidikan Ganesha (Undiksha), Indonesia. Hanacaraka basically receives Latin text input and outputs Balinese Script based on Bali Simbar font. Through the experiment, its handling on line breaking gave good transliteration result since special algorithm was applied. Future work to enrich and strengthen the transliteration knowledge is by extending the handling of line breaking on Latin-to-Balinese Script web application that was supported by dedicated Balinese-Unicode font.","PeriodicalId":408532,"journal":{"name":"2020 6th International Conference on Science in Information Technology (ICSITech)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126517730","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 : 2020-10-21DOI: 10.1109/ICSITech49800.2020.9392048
A. Aditya, Faridatun Ni’mah, H. Mahmudah, Okkie Puspitorini, N. Siswandari, A. Wijayanti
The number of motorized vehicles operating in Indonesia is increasing, especially two-wheeled motorized vehicles. This also triggers problems such as traffic jams and accidents. Warning from brake lights is not always effective to prevent collisions. Intelligent Transport System (ITS) offers a solution that is a future trend that refers to wireless communication as a system to detect and prevent accidents. In this research, a Vehicle to Vehicle (V2V) communication system using VLC consists of Leading Vehicle (LV) and Following Vehicle (FV). In LV there is an accelerometer that is used to detect the type of brake, namely No Brake, Brake and Hard Brake. The result of calculation of fuzzy logic data is binary data sent by VLC. VLC transmitter design uses and without lenses send binary data. Binary data is sent by V2V communication between LV and FV using VLC. FV detects binary data using a photodiode in day and night conditions. The performance LV that are fuzzy logic algorithm values used have an accuracy value of 87.5%. The results of brake detection using Fuzzy Logic algorithm are binary data sent 2 second sampling time through visible light communication. The binary data process transmitted process at daytime and night. Result highest accuracy using a lens is 58.33% at daytime and 72.34% at night.
{"title":"Detection Brake Condition of Vehicle Using Fuzzy Logic in Visible Light Communication","authors":"A. Aditya, Faridatun Ni’mah, H. Mahmudah, Okkie Puspitorini, N. Siswandari, A. Wijayanti","doi":"10.1109/ICSITech49800.2020.9392048","DOIUrl":"https://doi.org/10.1109/ICSITech49800.2020.9392048","url":null,"abstract":"The number of motorized vehicles operating in Indonesia is increasing, especially two-wheeled motorized vehicles. This also triggers problems such as traffic jams and accidents. Warning from brake lights is not always effective to prevent collisions. Intelligent Transport System (ITS) offers a solution that is a future trend that refers to wireless communication as a system to detect and prevent accidents. In this research, a Vehicle to Vehicle (V2V) communication system using VLC consists of Leading Vehicle (LV) and Following Vehicle (FV). In LV there is an accelerometer that is used to detect the type of brake, namely No Brake, Brake and Hard Brake. The result of calculation of fuzzy logic data is binary data sent by VLC. VLC transmitter design uses and without lenses send binary data. Binary data is sent by V2V communication between LV and FV using VLC. FV detects binary data using a photodiode in day and night conditions. The performance LV that are fuzzy logic algorithm values used have an accuracy value of 87.5%. The results of brake detection using Fuzzy Logic algorithm are binary data sent 2 second sampling time through visible light communication. The binary data process transmitted process at daytime and night. Result highest accuracy using a lens is 58.33% at daytime and 72.34% at night.","PeriodicalId":408532,"journal":{"name":"2020 6th International Conference on Science in Information Technology (ICSITech)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124711689","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 : 2020-10-21DOI: 10.1109/ICSITech49800.2020.9392031
A. Wibawa, Irzan Tri Saputra, Agung Bella Putra Utama, W. Lestari, Zahra Nabila Izdihar
Unique visitors are visitors who use one IP in a certain period of time. The number of unique visitors every day is a benchmark for the success of an electronic journal. The increasing number of unique visitors every day shows that scientific periodicals are increasingly in demand by the wider community, which also affects the breadth of distribution, and speeds up the journal accreditation system. Therefore it is necessary to forecast the number of unique visitors in electronic journals in the future. Here, Long Short-Term Memory (LSTM) captures the pattern of data that has been obtained and then used to describe future data. The data used for testing is unique, ending data as of January 1, 2018, until December 31, 2018. After the data is obtained, the data will be normalized, then processed by the LSTM method to get the output. Then the output will be normalized to get the size of MSE, RMSE, and also the level of accuracy. The selection of the learning rate and the determination of the number of neurons in the LSTM process have an effect on the performance test performed. From the research conducted, the highest accuracy results obtained in the learning rate of 0.1 is 66.81%. While the lowest MSE and RMSE were obtained at a learning rate of 0.2 is 189.53 and 13.76. Thus, the results obtained are expected to be able to predict the number of unique visitors to electronic journals in the future to meet the needs of journal accreditation.
{"title":"Long Short-Term Memory to Predict Unique Visitors of an Electronic Journal","authors":"A. Wibawa, Irzan Tri Saputra, Agung Bella Putra Utama, W. Lestari, Zahra Nabila Izdihar","doi":"10.1109/ICSITech49800.2020.9392031","DOIUrl":"https://doi.org/10.1109/ICSITech49800.2020.9392031","url":null,"abstract":"Unique visitors are visitors who use one IP in a certain period of time. The number of unique visitors every day is a benchmark for the success of an electronic journal. The increasing number of unique visitors every day shows that scientific periodicals are increasingly in demand by the wider community, which also affects the breadth of distribution, and speeds up the journal accreditation system. Therefore it is necessary to forecast the number of unique visitors in electronic journals in the future. Here, Long Short-Term Memory (LSTM) captures the pattern of data that has been obtained and then used to describe future data. The data used for testing is unique, ending data as of January 1, 2018, until December 31, 2018. After the data is obtained, the data will be normalized, then processed by the LSTM method to get the output. Then the output will be normalized to get the size of MSE, RMSE, and also the level of accuracy. The selection of the learning rate and the determination of the number of neurons in the LSTM process have an effect on the performance test performed. From the research conducted, the highest accuracy results obtained in the learning rate of 0.1 is 66.81%. While the lowest MSE and RMSE were obtained at a learning rate of 0.2 is 189.53 and 13.76. Thus, the results obtained are expected to be able to predict the number of unique visitors to electronic journals in the future to meet the needs of journal accreditation.","PeriodicalId":408532,"journal":{"name":"2020 6th International Conference on Science in Information Technology (ICSITech)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121405866","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 : 2020-10-21DOI: 10.1109/ICSITech49800.2020.9392057
Arief Bramanto Wicaksono Putra, Anggri Sartika Wiguna, A. F. O. Gaffar, R. Malani
Rainfall prediction which considering climate variables such as air temperature, air humidity, air pressure, and wind speed are categorized to a non-stationary stochastic processes. Modeling of time series data of precipitation is carried out using MISO ARX model. Genetic Algorithm (GA) is used to optimize the entire model coefficients so that the results obtained is quite accurate. A new concept in the reproductive stage called Cleavage Self (CS) is designed to improve performance of GA in terms of speed and accuracy of the prediction process. In order to prove its performance, prediction of time series precipitation is also conducted by applying AG without CS. The result is the speed of the process AG with CS is approximately 9.6 times faster than that without CS. The absolute differences of the MSE ideal for AG with CS is 0.0004 compare to 0.0151 for AG without CS.
{"title":"Cleavage self : a new concept in reproduction stage of genetic algorithm for rainfall prediction","authors":"Arief Bramanto Wicaksono Putra, Anggri Sartika Wiguna, A. F. O. Gaffar, R. Malani","doi":"10.1109/ICSITech49800.2020.9392057","DOIUrl":"https://doi.org/10.1109/ICSITech49800.2020.9392057","url":null,"abstract":"Rainfall prediction which considering climate variables such as air temperature, air humidity, air pressure, and wind speed are categorized to a non-stationary stochastic processes. Modeling of time series data of precipitation is carried out using MISO ARX model. Genetic Algorithm (GA) is used to optimize the entire model coefficients so that the results obtained is quite accurate. A new concept in the reproductive stage called Cleavage Self (CS) is designed to improve performance of GA in terms of speed and accuracy of the prediction process. In order to prove its performance, prediction of time series precipitation is also conducted by applying AG without CS. The result is the speed of the process AG with CS is approximately 9.6 times faster than that without CS. The absolute differences of the MSE ideal for AG with CS is 0.0004 compare to 0.0151 for AG without CS.","PeriodicalId":408532,"journal":{"name":"2020 6th International Conference on Science in Information Technology (ICSITech)","volume":"258 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116232997","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 : 2020-10-21DOI: 10.1109/ICSITech49800.2020.9392056
A. Pranolo, Yingchi Mao, Yan Tang, Haviluddin, A. Wibawa
The prediction and its accuracy of the rainfall is needed due to it would be affected to the various areas of life, such as feasibility aircraft departures and, in general issue, is climate change. This paper aimed to apply a Long Short Term Memory (LSTM) approach to get accurate rainfall forecasting. Also, the LSTM accuracy would be compared to BPNN (Backpropagation Neural Network) algorithm. In this research, LSTM architecture used a hidden layer of 200, a maximum epoch of 250, 1 gradient threshold, and learning rates of 0.005, 0.007, and 0.009. Then, standardize data was used gamma γ of 1.05. Then, the BPNN architectures of [2-50-10-1, epoch 250] have been explored. The accuracy performance is measured by the root means square error (RMSE). The experimental results showed that the LSTM had produced a good accuracy than BPNN, with the value of RMSE was 0.2367 and 0.1938. It means that the forecast accuracy of the LSTM approach outperformed the BPNN to predict the rainfall. This finding would be useful for the climatology station to develop a forecsat rainfall application-based artificial intelligence.
{"title":"A Long Short Term Memory Implemented for Rainfall Forecasting","authors":"A. Pranolo, Yingchi Mao, Yan Tang, Haviluddin, A. Wibawa","doi":"10.1109/ICSITech49800.2020.9392056","DOIUrl":"https://doi.org/10.1109/ICSITech49800.2020.9392056","url":null,"abstract":"The prediction and its accuracy of the rainfall is needed due to it would be affected to the various areas of life, such as feasibility aircraft departures and, in general issue, is climate change. This paper aimed to apply a Long Short Term Memory (LSTM) approach to get accurate rainfall forecasting. Also, the LSTM accuracy would be compared to BPNN (Backpropagation Neural Network) algorithm. In this research, LSTM architecture used a hidden layer of 200, a maximum epoch of 250, 1 gradient threshold, and learning rates of 0.005, 0.007, and 0.009. Then, standardize data was used gamma γ of 1.05. Then, the BPNN architectures of [2-50-10-1, epoch 250] have been explored. The accuracy performance is measured by the root means square error (RMSE). The experimental results showed that the LSTM had produced a good accuracy than BPNN, with the value of RMSE was 0.2367 and 0.1938. It means that the forecast accuracy of the LSTM approach outperformed the BPNN to predict the rainfall. This finding would be useful for the climatology station to develop a forecsat rainfall application-based artificial intelligence.","PeriodicalId":408532,"journal":{"name":"2020 6th International Conference on Science in Information Technology (ICSITech)","volume":"54 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114027514","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 : 2020-10-21DOI: 10.1109/ICSITech49800.2020.9392054
H. Jayadianti, Nurheri Cahyana, Wahyu Garuda Kusuma, A. H. Pratomo, Heryanto
The nutritional requirements for the fattening process in each beef cattle differ according to body weight and body weight gain targets. Inappropriate feed composition can be detrimental to breeders because the bodyweight gain target is not achieved, and improper expenditure of feed funds. Genetic algorithms can be used to search for feed composition solutions, where the nutrients produced are close to the nutrients needed by beef cattle. Genetic algorithms have several disadvantages, one of which often occurs premature convergence, where genetic operators cannot produce offspring better than their parents. Premature convergence on genetic algorithms can be overcome by hybridizing local search algorithms, one of which is Simulated Annealing. Simulated Annealing is a local search algorithm that functions as a counterweight to genetic algorithms, where genetic algorithms are able to explore global areas, while simulated Annealing is able to exploit local areas. Comparative testing of hybrid genetic algorithm and simulated Annealing with a simple genetic algorithm shows that the fitness value of the hybridization method is better than the simple genetic algorithm. The best fitness of the hybridization method is 0.15934987829563, and the best fitness is a simple genetic algorithm of 0.15869962195529. The hybridization method produces better fitness because of the role of simulated Annealing in exploiting individuals on genetic algorithms so that the composition of feed solutions can be closer to the optimal solution.
{"title":"Hybrid Genetic Algorithm and Simulated Annealing for The Selection of Web-Based Beef Cattle Feed Composition","authors":"H. Jayadianti, Nurheri Cahyana, Wahyu Garuda Kusuma, A. H. Pratomo, Heryanto","doi":"10.1109/ICSITech49800.2020.9392054","DOIUrl":"https://doi.org/10.1109/ICSITech49800.2020.9392054","url":null,"abstract":"The nutritional requirements for the fattening process in each beef cattle differ according to body weight and body weight gain targets. Inappropriate feed composition can be detrimental to breeders because the bodyweight gain target is not achieved, and improper expenditure of feed funds. Genetic algorithms can be used to search for feed composition solutions, where the nutrients produced are close to the nutrients needed by beef cattle. Genetic algorithms have several disadvantages, one of which often occurs premature convergence, where genetic operators cannot produce offspring better than their parents. Premature convergence on genetic algorithms can be overcome by hybridizing local search algorithms, one of which is Simulated Annealing. Simulated Annealing is a local search algorithm that functions as a counterweight to genetic algorithms, where genetic algorithms are able to explore global areas, while simulated Annealing is able to exploit local areas. Comparative testing of hybrid genetic algorithm and simulated Annealing with a simple genetic algorithm shows that the fitness value of the hybridization method is better than the simple genetic algorithm. The best fitness of the hybridization method is 0.15934987829563, and the best fitness is a simple genetic algorithm of 0.15869962195529. The hybridization method produces better fitness because of the role of simulated Annealing in exploiting individuals on genetic algorithms so that the composition of feed solutions can be closer to the optimal solution.","PeriodicalId":408532,"journal":{"name":"2020 6th International Conference on Science in Information Technology (ICSITech)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122364843","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 : 2020-10-21DOI: 10.1109/ICSITech49800.2020.9392033
Vaishali Girdhar, Eyhab Al-Masri
With the increase in the abundance and prominence of fog-based systems comes the increase in demand for smarter devices. This can be quite challenging since fog-based IoT systems need to adapt in the event of a sudden deterioration in the level of service they offer due to hardware or software fluctuations. Fog-based IoT systems need to become fault-tolerant in order to ensure the delivery of secure, reliable, robust, and dynamic services while addressing unexpected changes that may occur in terms of both hardware and software. To achieve such fault-tolerance, however, it is imperative to accurately define and identify the differences between errors, faults, and failures that may exist within fog-based environments. In this paper, we propose a solution to this problem and introduce an N-version anomaly-based Fault Detection (NvABFD) technique used for enhancing the fault tolerance of fog-based systems. Using NvABFD, it is possible to identify data anomalies, errors, faults, and failures that may occur in fog-based environments in near real time. We tested the NvABFD technique in a simulated patient monitoring system using the MobiAct dataset. Our results show an accuracy of ~99.9% in anomaly, error, and fault detection indicating that this technique may enhance fault-tolerance in a fog-based system by accurately identifying anomalies, errors, and faults as they occur.
{"title":"N-Version Programming for Enhancing Fault Tolerance in Fog-based IoT Systems","authors":"Vaishali Girdhar, Eyhab Al-Masri","doi":"10.1109/ICSITech49800.2020.9392033","DOIUrl":"https://doi.org/10.1109/ICSITech49800.2020.9392033","url":null,"abstract":"With the increase in the abundance and prominence of fog-based systems comes the increase in demand for smarter devices. This can be quite challenging since fog-based IoT systems need to adapt in the event of a sudden deterioration in the level of service they offer due to hardware or software fluctuations. Fog-based IoT systems need to become fault-tolerant in order to ensure the delivery of secure, reliable, robust, and dynamic services while addressing unexpected changes that may occur in terms of both hardware and software. To achieve such fault-tolerance, however, it is imperative to accurately define and identify the differences between errors, faults, and failures that may exist within fog-based environments. In this paper, we propose a solution to this problem and introduce an N-version anomaly-based Fault Detection (NvABFD) technique used for enhancing the fault tolerance of fog-based systems. Using NvABFD, it is possible to identify data anomalies, errors, faults, and failures that may occur in fog-based environments in near real time. We tested the NvABFD technique in a simulated patient monitoring system using the MobiAct dataset. Our results show an accuracy of ~99.9% in anomaly, error, and fault detection indicating that this technique may enhance fault-tolerance in a fog-based system by accurately identifying anomalies, errors, and faults as they occur.","PeriodicalId":408532,"journal":{"name":"2020 6th International Conference on Science in Information Technology (ICSITech)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125748695","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 : 2020-10-21DOI: 10.1109/icsitech49800.2020.9392065
{"title":"Welcome General Chair and Remarks","authors":"","doi":"10.1109/icsitech49800.2020.9392065","DOIUrl":"https://doi.org/10.1109/icsitech49800.2020.9392065","url":null,"abstract":"","PeriodicalId":408532,"journal":{"name":"2020 6th International Conference on Science in Information Technology (ICSITech)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131279581","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 : 2020-10-21DOI: 10.1109/ICSITech49800.2020.9392066
Tammyana Ranas, Y. G. Sucahyo, Arfive Gandhi
The survival of crowdsourcing platforms depends on active crowdsourcee participation. Tripadvisor is one of the crowdsourcing platforms. However, the number of Tripadvisor visitors has stagnant growth. One of the gamification objectives is to increase the user’s intrinsic motivation to engage in certain activities or behaviors. This study aims to determine the effect of gamification in motivating Tripadvisor users to participate actively. Furthermore, this study aims to evaluate gamification’s effectiveness in motivating crowdsourcee to participate in a reference for further gamification development actively. This study uses the Self-Determination Theory (SDT) and Motivational Affordance Perspective (MAP) to analyze gamification’s effect in motivating Tripadvisor users. This research uses a quantitative approach and the PLS-SEM method using SmartPLS3 for processing data. Respondents of this research were 154 Tripadvisor users as valid respondents. In this study, there were fifteen hypotheses with seven accepted hypotheses and eight rejected hypotheses. This study indicates that only the badges rewarding and level of achievement can motivate Tripadvisor users’ participation. It because the user feels that getting gamification is not their purpose of participating in Tripadvisor. They participated because they are indeed like shared their travel experience.
{"title":"Evaluating the Gamification in TripAdvisor: Is it Effective for Crowdsourcing Platform?","authors":"Tammyana Ranas, Y. G. Sucahyo, Arfive Gandhi","doi":"10.1109/ICSITech49800.2020.9392066","DOIUrl":"https://doi.org/10.1109/ICSITech49800.2020.9392066","url":null,"abstract":"The survival of crowdsourcing platforms depends on active crowdsourcee participation. Tripadvisor is one of the crowdsourcing platforms. However, the number of Tripadvisor visitors has stagnant growth. One of the gamification objectives is to increase the user’s intrinsic motivation to engage in certain activities or behaviors. This study aims to determine the effect of gamification in motivating Tripadvisor users to participate actively. Furthermore, this study aims to evaluate gamification’s effectiveness in motivating crowdsourcee to participate in a reference for further gamification development actively. This study uses the Self-Determination Theory (SDT) and Motivational Affordance Perspective (MAP) to analyze gamification’s effect in motivating Tripadvisor users. This research uses a quantitative approach and the PLS-SEM method using SmartPLS3 for processing data. Respondents of this research were 154 Tripadvisor users as valid respondents. In this study, there were fifteen hypotheses with seven accepted hypotheses and eight rejected hypotheses. This study indicates that only the badges rewarding and level of achievement can motivate Tripadvisor users’ participation. It because the user feels that getting gamification is not their purpose of participating in Tripadvisor. They participated because they are indeed like shared their travel experience.","PeriodicalId":408532,"journal":{"name":"2020 6th International Conference on Science in Information Technology (ICSITech)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128061469","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 : 2020-10-21DOI: 10.1109/ICSITech49800.2020.9392064
Ayush Bandil, Eyhab Al-Masri
The wide acceptance of the Internet of Things (IoT) paradigm has contributed in recent years to a significant increase in the number of IoT applications and the existence of IoT devices generating large volumes of data. As a result, IoT devices have become vulnerable to cyberattacks. In addition, conventional digital forensics approaches are no longer effective for investigating a digital crime involving IoT devices and extracting traces across heterogeneous devices. To overcome these challenges, we propose VTA-IH, a fog-based digital forensics framework that employs a Complex Events Processing (CEP) model for intelligently identifying abnormalities in real-time events associated with IoT data streams. To this extent, we devise a Degree of Abnormality (DA) penalty mechanism that is adapted to identify vulnerabilities, threat, or attack patterns using multiple rules across devices and network-related events across fog environments. Throughout the paper, we discuss the architecture of the VTA-IH framework and demonstrate usefulness of the proposed CEP approach. Our proposed VTA-IH framework can be used in applications such as industrial IoT, autonomous vehicles, smart home systems, smart farming, among others.
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