Pub Date : 2021-11-03DOI: 10.1109/ICIC54025.2021.9632926
Fabianaugie Jametoni, D. E. Saputra
The most basic capability of an autonomous drone is its positioning capability. There is various method available to calculate a drone position. To help any new researcher on autonomous drone to choose their option on drone positioning system, a proper categorization is needed. This work provides a taxonomy of drone positioning system. The taxonomy categorizes drone positioning system into two major methods: vision-based and non-vision-based. The taxonomy further divides each method into several sub-method based on the equipment and calculation method. The taxonomy also provides the advantage and disadvantage of each method.
{"title":"A Study on Autonomous Drone Positioning Method","authors":"Fabianaugie Jametoni, D. E. Saputra","doi":"10.1109/ICIC54025.2021.9632926","DOIUrl":"https://doi.org/10.1109/ICIC54025.2021.9632926","url":null,"abstract":"The most basic capability of an autonomous drone is its positioning capability. There is various method available to calculate a drone position. To help any new researcher on autonomous drone to choose their option on drone positioning system, a proper categorization is needed. This work provides a taxonomy of drone positioning system. The taxonomy categorizes drone positioning system into two major methods: vision-based and non-vision-based. The taxonomy further divides each method into several sub-method based on the equipment and calculation method. The taxonomy also provides the advantage and disadvantage of each method.","PeriodicalId":189541,"journal":{"name":"2021 Sixth International Conference on Informatics and Computing (ICIC)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131215253","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 : 2021-11-03DOI: 10.1109/ICIC54025.2021.9632918
Ben Rahman, H. L. Hendric Spits Warnars, Boy Subirosa Sabarguna, W. Budiharto
Heart disease is a disease that needs to be watched out for and is of particular concern. Seeing to the WHO report, in 2018, as many as 17.9 million people died from heart disease, and especially in Indonesia, heart disease in 2020 became the highest cause of death. This study uses data mining techniques to pull out information from the data used. This research provides a scientific contribution, namely detecting heart disease as early as possible. In this case, the author uses the K-Nearest Neighbor Algorithm to classify the data based on the nearest neighbor data. The database is own in a reasonably high volume, so it should note that irrelevant attributes will be removed over or noise. If they are still used, data processing results will not be optimal, so data cleaning needs to be done carefully. The selection of the data used was 1243 records, and after being selected the data were taken in this study as many as 366 records, with parameters using 12 attributes, actual data from hospitals, data consisting of data from patients under surveillance for cardiac care, and data from patients who underwent surgery and Data from Medical Examination. Therefore, it is necessary to develop a decision support system that assists doctors in taking steps for early detection. Research conducted with the K-Nearest Neighbors algorithm accuracy up to 77% with a value of K = 7.
{"title":"Heart Disease Classification Model Using K-Nearest Neighbor Algorithm","authors":"Ben Rahman, H. L. Hendric Spits Warnars, Boy Subirosa Sabarguna, W. Budiharto","doi":"10.1109/ICIC54025.2021.9632918","DOIUrl":"https://doi.org/10.1109/ICIC54025.2021.9632918","url":null,"abstract":"Heart disease is a disease that needs to be watched out for and is of particular concern. Seeing to the WHO report, in 2018, as many as 17.9 million people died from heart disease, and especially in Indonesia, heart disease in 2020 became the highest cause of death. This study uses data mining techniques to pull out information from the data used. This research provides a scientific contribution, namely detecting heart disease as early as possible. In this case, the author uses the K-Nearest Neighbor Algorithm to classify the data based on the nearest neighbor data. The database is own in a reasonably high volume, so it should note that irrelevant attributes will be removed over or noise. If they are still used, data processing results will not be optimal, so data cleaning needs to be done carefully. The selection of the data used was 1243 records, and after being selected the data were taken in this study as many as 366 records, with parameters using 12 attributes, actual data from hospitals, data consisting of data from patients under surveillance for cardiac care, and data from patients who underwent surgery and Data from Medical Examination. Therefore, it is necessary to develop a decision support system that assists doctors in taking steps for early detection. Research conducted with the K-Nearest Neighbors algorithm accuracy up to 77% with a value of K = 7.","PeriodicalId":189541,"journal":{"name":"2021 Sixth International Conference on Informatics and Computing (ICIC)","volume":"395 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124347162","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 : 2021-11-03DOI: 10.1109/ICIC54025.2021.9632972
Abdul Rahman, Ermatita, D. Budianta, Abdiansah
The main problem in tidal land is high soil acidity, and the availability of nutrients in the soil is relatively low. Utilization of local resource vermicompost is used to improve soil conditions in tidal lands in order to increase crop yields. The parameter of paddy plant height has a very high correlation with paddy yields. This study aims to implement the ANFIS method to predict paddy plant height based on the treatment of vermicompost organic fertilizer. The dataset used for ANFIS training was taken directly from the observation data on the height of the paddy plant and the results of soil laboratory tests. The ANFIS process consists of 5 inputs consisting of fertilizer treatment, pH, N, P, K, and one output, namely paddy plant height. The results obtained from the training data process are that there are 486 rules and the error rate using MAPE is 3.53%, or the accuracy level of the prediction results is 96.47%.
{"title":"Prediction of Paddy Plant Height with Vermicompost Fertilizer Treatment on Tidal Land using ANFIS Method","authors":"Abdul Rahman, Ermatita, D. Budianta, Abdiansah","doi":"10.1109/ICIC54025.2021.9632972","DOIUrl":"https://doi.org/10.1109/ICIC54025.2021.9632972","url":null,"abstract":"The main problem in tidal land is high soil acidity, and the availability of nutrients in the soil is relatively low. Utilization of local resource vermicompost is used to improve soil conditions in tidal lands in order to increase crop yields. The parameter of paddy plant height has a very high correlation with paddy yields. This study aims to implement the ANFIS method to predict paddy plant height based on the treatment of vermicompost organic fertilizer. The dataset used for ANFIS training was taken directly from the observation data on the height of the paddy plant and the results of soil laboratory tests. The ANFIS process consists of 5 inputs consisting of fertilizer treatment, pH, N, P, K, and one output, namely paddy plant height. The results obtained from the training data process are that there are 486 rules and the error rate using MAPE is 3.53%, or the accuracy level of the prediction results is 96.47%.","PeriodicalId":189541,"journal":{"name":"2021 Sixth International Conference on Informatics and Computing (ICIC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123657731","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 : 2021-11-03DOI: 10.1109/ICIC54025.2021.9632987
Aa Zezen Zaenal Abidin, M. Othman, Aslinda Hassan, Yuli Murdianingsih, Usep Tatang Suryadi, Zulkiflee Muslim
Verifying a set of most frequent problems is essential before introducing practical solutions using new technology, processes, and practices. This study proposes a way to verify these problem sets. The main contribution of this paper is a method to verify a set of most frequent problems in waste disposal practices previously identified through a survey questionnaire, using Google Earth visualization and the Apriori algorithm. Google Earth is used to pinpoint the geographical locations of existing waste bins, illegal landfills, and people's houses. The distance between the waste bins and the residents' houses, sites of waste disposal by burning, and sites of waste disposal by dumping are then analyzed as a combination of the problems of waste disposal practices. Support, Confidence, multiplication between Support and Confidence, and lift ratio values are then calculated to obtain a combination of the most frequent problems sets. Next, the support value in the Apriori algorithm is compared with the FP-Growth method using Rapidminer. Results obtain support and thus verify data previously obtained from the survey. For a 2-itemset problem and a minimum support value of 0.1, 33% accuracy is obtained, while a 3-itemset problem returns 99% accuracy. We show that our method is useful in verifying data previously obtained from other sources.
{"title":"Verifying Waste Disposal Practice Problems of Rural Areas In Indonesia Using the Apriori Algorithm","authors":"Aa Zezen Zaenal Abidin, M. Othman, Aslinda Hassan, Yuli Murdianingsih, Usep Tatang Suryadi, Zulkiflee Muslim","doi":"10.1109/ICIC54025.2021.9632987","DOIUrl":"https://doi.org/10.1109/ICIC54025.2021.9632987","url":null,"abstract":"Verifying a set of most frequent problems is essential before introducing practical solutions using new technology, processes, and practices. This study proposes a way to verify these problem sets. The main contribution of this paper is a method to verify a set of most frequent problems in waste disposal practices previously identified through a survey questionnaire, using Google Earth visualization and the Apriori algorithm. Google Earth is used to pinpoint the geographical locations of existing waste bins, illegal landfills, and people's houses. The distance between the waste bins and the residents' houses, sites of waste disposal by burning, and sites of waste disposal by dumping are then analyzed as a combination of the problems of waste disposal practices. Support, Confidence, multiplication between Support and Confidence, and lift ratio values are then calculated to obtain a combination of the most frequent problems sets. Next, the support value in the Apriori algorithm is compared with the FP-Growth method using Rapidminer. Results obtain support and thus verify data previously obtained from the survey. For a 2-itemset problem and a minimum support value of 0.1, 33% accuracy is obtained, while a 3-itemset problem returns 99% accuracy. We show that our method is useful in verifying data previously obtained from other sources.","PeriodicalId":189541,"journal":{"name":"2021 Sixth International Conference on Informatics and Computing (ICIC)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122622088","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 : 2021-11-03DOI: 10.1109/ICIC54025.2021.9632914
Anis Fitri Nur Masruriyah, H. Basri, H. H. Handayani, Ahmad Fauzi, Ayu Ratna Juwita, Deden Wahiddin
COVID-19 has been an epidemic since the end of 2019. The number of patients with COVID-19 continues to escalate until new variants emerge. The COVID-19 detection procedure begins with detecting early symptoms, furthermore, confirmed by the swab and Chest X-Ray methods. The process of swab and Chest X-Ray takes a relatively long time since in Chest X-Ray some patients have the same symptoms as pneumonia. This study carried out the classification of COVID-19 and not COVID-19 with Discrete Wavelet Transform as feature extraction techniques and deep learning as the classification method. The result of this study capable to identify Chest X-Ray with COVID-19 and the accuracy increased of more than 10% on Support Vector Machine, Decision Tree and Deep Learning. So that, the comparison result showed that feature extraction was able to significantly improve accuracy.
{"title":"The Rise Efficiency of Coronavirus Disease Classification Employing Feature Extraction","authors":"Anis Fitri Nur Masruriyah, H. Basri, H. H. Handayani, Ahmad Fauzi, Ayu Ratna Juwita, Deden Wahiddin","doi":"10.1109/ICIC54025.2021.9632914","DOIUrl":"https://doi.org/10.1109/ICIC54025.2021.9632914","url":null,"abstract":"COVID-19 has been an epidemic since the end of 2019. The number of patients with COVID-19 continues to escalate until new variants emerge. The COVID-19 detection procedure begins with detecting early symptoms, furthermore, confirmed by the swab and Chest X-Ray methods. The process of swab and Chest X-Ray takes a relatively long time since in Chest X-Ray some patients have the same symptoms as pneumonia. This study carried out the classification of COVID-19 and not COVID-19 with Discrete Wavelet Transform as feature extraction techniques and deep learning as the classification method. The result of this study capable to identify Chest X-Ray with COVID-19 and the accuracy increased of more than 10% on Support Vector Machine, Decision Tree and Deep Learning. So that, the comparison result showed that feature extraction was able to significantly improve accuracy.","PeriodicalId":189541,"journal":{"name":"2021 Sixth International Conference on Informatics and Computing (ICIC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125004132","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 : 2021-11-03DOI: 10.1109/ICIC54025.2021.9632890
I. Irvanizam, Natasya Azzahra, Inayatur Nadhira, Z. Zulfan, M. Subianto, I. Syahrini
The office of social affairs has provided the productive economic endeavors (PEE) program that empowers increasing the income of micros, small and medium enterprises (MSMEs) to build harmonious social relationships among communities. However, in the selection process for this program recipient so far, an officer evaluated potential MSMEs based on requirement data conventionally so that it is very vulnerable to personal subjectivity problems. Therefore, we designed a Multiple Criteria Decision-Making (MCDM) model to apply to this decision-making process. The model integrated the AHP method with the VIKOR method. First, based on the professional decision-maker judgment in evaluating a pairwise criteria comparison, the AHP determined the acceptable criteria weights automatically, and the VIKOR then utilized them to rank alternatives based on the values of utility and regret measures. After checking the acceptability advantage and stability in decision-making, the results showed that alternative U5 and U8 were the compromise solutions representing the closeness to the ideal solution. Finally, this MCDM model is a feasible and suitable tool for dealing with this decision-making problem.
{"title":"Multiple Criteria Decision Making Based on VIKOR for Productive Economic Endeavors Distribution Problem","authors":"I. Irvanizam, Natasya Azzahra, Inayatur Nadhira, Z. Zulfan, M. Subianto, I. Syahrini","doi":"10.1109/ICIC54025.2021.9632890","DOIUrl":"https://doi.org/10.1109/ICIC54025.2021.9632890","url":null,"abstract":"The office of social affairs has provided the productive economic endeavors (PEE) program that empowers increasing the income of micros, small and medium enterprises (MSMEs) to build harmonious social relationships among communities. However, in the selection process for this program recipient so far, an officer evaluated potential MSMEs based on requirement data conventionally so that it is very vulnerable to personal subjectivity problems. Therefore, we designed a Multiple Criteria Decision-Making (MCDM) model to apply to this decision-making process. The model integrated the AHP method with the VIKOR method. First, based on the professional decision-maker judgment in evaluating a pairwise criteria comparison, the AHP determined the acceptable criteria weights automatically, and the VIKOR then utilized them to rank alternatives based on the values of utility and regret measures. After checking the acceptability advantage and stability in decision-making, the results showed that alternative U5 and U8 were the compromise solutions representing the closeness to the ideal solution. Finally, this MCDM model is a feasible and suitable tool for dealing with this decision-making problem.","PeriodicalId":189541,"journal":{"name":"2021 Sixth International Conference on Informatics and Computing (ICIC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128088234","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 : 2021-11-03DOI: 10.1109/ICIC54025.2021.9632907
Bayu Yasa Wedha, Daniel Avian Karjadi, Erick Dazki, Handri Santoso, R. E. Indrajit
Recent years, Internet of Things (IoT) provides new business opportunity in terms of cost reduction, increase in productivity or efficiency to organization. In the context of supply chains, IoT helps trucking logistics to track their assets' health, location, utilization, efficiency, and visibility. Indonesia, as the largest archipelago country in the world, mainly relying on truck fleet as its main logistics transportation. Thus, IoT adoption could bring Indonesia's growth and safety in the country. Based on logistic performance index 2018, Indonesia position is 46th in logistic management. One of the parameters is technology adoption especially IoT to improve Logistic company, there are limited study that explore the adoption of IoT in trucking logistics. In this study, companies use IoT technology in their trucks, based on Industry types and spatial distribution is analyzed. Factors that could affect the IoT adoption are being discussed. The IoT adoption level obtained from experts' interview. And then used to analyze 161 company adoption across Indonesia. The result shows that IoT adoption level is between 2 and 3 in the scale of 5 with the highest adoption in Cement industry. Industry that operates in Java and Sumatera islands tend to be more mature on IoT adoption level than other islands for Chemical and Cement industry respectively. Government can make use of this study's result to make policy that cover more wider industry types and location to improve the overall trucking logistics performance.
{"title":"Analysis of IoT adoption on Trucking Logistics in Various Industry in Indonesia","authors":"Bayu Yasa Wedha, Daniel Avian Karjadi, Erick Dazki, Handri Santoso, R. E. Indrajit","doi":"10.1109/ICIC54025.2021.9632907","DOIUrl":"https://doi.org/10.1109/ICIC54025.2021.9632907","url":null,"abstract":"Recent years, Internet of Things (IoT) provides new business opportunity in terms of cost reduction, increase in productivity or efficiency to organization. In the context of supply chains, IoT helps trucking logistics to track their assets' health, location, utilization, efficiency, and visibility. Indonesia, as the largest archipelago country in the world, mainly relying on truck fleet as its main logistics transportation. Thus, IoT adoption could bring Indonesia's growth and safety in the country. Based on logistic performance index 2018, Indonesia position is 46th in logistic management. One of the parameters is technology adoption especially IoT to improve Logistic company, there are limited study that explore the adoption of IoT in trucking logistics. In this study, companies use IoT technology in their trucks, based on Industry types and spatial distribution is analyzed. Factors that could affect the IoT adoption are being discussed. The IoT adoption level obtained from experts' interview. And then used to analyze 161 company adoption across Indonesia. The result shows that IoT adoption level is between 2 and 3 in the scale of 5 with the highest adoption in Cement industry. Industry that operates in Java and Sumatera islands tend to be more mature on IoT adoption level than other islands for Chemical and Cement industry respectively. Government can make use of this study's result to make policy that cover more wider industry types and location to improve the overall trucking logistics performance.","PeriodicalId":189541,"journal":{"name":"2021 Sixth International Conference on Informatics and Computing (ICIC)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114214228","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 : 2021-11-03DOI: 10.1109/ICIC54025.2021.9633010
Arief Hidayat, K. Adi, B. Surarso
The student learning process is influenced by several factors, one of which is student learning styles. Learning style is one of the most important factors in the E-learning environment because it can help the system to effectively personalize the learning process of students according to their learning style. Previously, to detect student learning styles by asking students to fill out questionnaires. However, there are problems with this static technique. One of these problems is the lack of students' self-awareness of their learning preferences. In addition, almost all students feel bored when asked to fill out a questionnaire. This research determined the learning style based on the Felder and Silverman Learning Style. This determination process is carried out using student activity data on a pure Moodle learning management system (LMS). The process begins with processing based on the literature to get a vector combination of learning styles. Student activity data is processed to produce data that only contains activities that are included in the selected features. The results of both are combined as input to the clustering process. This research applies the modified K-Means Clustering algorithm. Modifications were made using the learning style combination vector as the initial centroid. The k value used in this study was 8 which came from 8 combinations of learning styles from 3 dimensions used in this study. This is different from the combination of learning styles in FSLSM which has 16 combinations of learning styles originating from 4 dimensions of learning styles. This difference is caused by student activity data that only supports 3 dimensions of learning style.
{"title":"Determine Felder Silverman Learning Style Model using Literature Based and K-Means Clustering","authors":"Arief Hidayat, K. Adi, B. Surarso","doi":"10.1109/ICIC54025.2021.9633010","DOIUrl":"https://doi.org/10.1109/ICIC54025.2021.9633010","url":null,"abstract":"The student learning process is influenced by several factors, one of which is student learning styles. Learning style is one of the most important factors in the E-learning environment because it can help the system to effectively personalize the learning process of students according to their learning style. Previously, to detect student learning styles by asking students to fill out questionnaires. However, there are problems with this static technique. One of these problems is the lack of students' self-awareness of their learning preferences. In addition, almost all students feel bored when asked to fill out a questionnaire. This research determined the learning style based on the Felder and Silverman Learning Style. This determination process is carried out using student activity data on a pure Moodle learning management system (LMS). The process begins with processing based on the literature to get a vector combination of learning styles. Student activity data is processed to produce data that only contains activities that are included in the selected features. The results of both are combined as input to the clustering process. This research applies the modified K-Means Clustering algorithm. Modifications were made using the learning style combination vector as the initial centroid. The k value used in this study was 8 which came from 8 combinations of learning styles from 3 dimensions used in this study. This is different from the combination of learning styles in FSLSM which has 16 combinations of learning styles originating from 4 dimensions of learning styles. This difference is caused by student activity data that only supports 3 dimensions of learning style.","PeriodicalId":189541,"journal":{"name":"2021 Sixth International Conference on Informatics and Computing (ICIC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121225069","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 : 2021-11-03DOI: 10.1109/ICIC54025.2021.9632939
A. Rahmah
The number of failing students in courses incompletion has increased during the implementation of distance learning due to the Covid-19 outbreak. The phenomenon also happened at STT Terpadu Nurul Fikri, a campus majoring in IT and IS, Depok, Indonesia. The implementation of distance learning commonly utilizes a learning management system (LMS) as the primary learning media, such as Moodle. It encourages a shift in students monitoring approach using their behavior in LMS usage. Therefore, an early warning system using students' at-risk behavior in utilizing the LMS is an opportunity to reduce the failure rate. It is the issue raised in this research, which carried out using the following steps: analyze course incompletion pattern, formulate to-be-monitored factors, designing early warning system, and recommending how to apply it. The results form factors related to the LMS usage monitoring and the design of an early warning system for student at-risk. This result may become a tool to prevent course incompletion by showing overview about students’ at-risk situation.
{"title":"Designing Early Warning System for Course Completion using Learning Management System","authors":"A. Rahmah","doi":"10.1109/ICIC54025.2021.9632939","DOIUrl":"https://doi.org/10.1109/ICIC54025.2021.9632939","url":null,"abstract":"The number of failing students in courses incompletion has increased during the implementation of distance learning due to the Covid-19 outbreak. The phenomenon also happened at STT Terpadu Nurul Fikri, a campus majoring in IT and IS, Depok, Indonesia. The implementation of distance learning commonly utilizes a learning management system (LMS) as the primary learning media, such as Moodle. It encourages a shift in students monitoring approach using their behavior in LMS usage. Therefore, an early warning system using students' at-risk behavior in utilizing the LMS is an opportunity to reduce the failure rate. It is the issue raised in this research, which carried out using the following steps: analyze course incompletion pattern, formulate to-be-monitored factors, designing early warning system, and recommending how to apply it. The results form factors related to the LMS usage monitoring and the design of an early warning system for student at-risk. This result may become a tool to prevent course incompletion by showing overview about students’ at-risk situation.","PeriodicalId":189541,"journal":{"name":"2021 Sixth International Conference on Informatics and Computing (ICIC)","volume":"11 12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115324994","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 : 2021-11-03DOI: 10.1109/ICIC54025.2021.9632925
Andreas Renaldy Darmawidjaja, E. P. Wibowo
The evolve of communication are growing rapidly, people competing to make communication much better than before, the technology develop is 5G communication, the data rate and speed that 5G given more better than 4G. Every communication technology requires an antenna as a transmitter and receiver to support communication working properly. Antipodal Vivaldi (AVA) is one type of Vivaldi Antenna which is better than other types of Antipodal Vivaldi for 5G communications. It has advantages for the High Gain, improve return loss, high efficiency, enhanced beamwidth, low sidelobe level, (Reduce the sidelobe level and back lobe level), Compact size, Stable radiation pattern, higher Operating Frequencies (1 Ghz to 100 Ghz) and more front to back ratio, which are really suitable for 5G communications. Antipodal Vivaldi Antenna (AVA) work at 2.6 GHz (2.6768 Ghz). The antenna needs to get the Institute of Electrical and Electronics Engineers (IEEE) defined standards which is VSWR 1, reference impedance 100 ohm, and s- parameter below -20dB. The Antipodal Vivaldi Antenna design process is carried out by using math formulation and experimental methods. For simulate and optimizing it, it uses CST studio suite 2018 software. To get the IEEE defined standards, AVA need to be optimize with changing antenna dimension elements (feed line width) and conFig. its slots which can lead to physic optimization. The results obtained in the form of slot antenna that works at a frequency of 2.6 GHz (2.6768 Ghz). The results obtained are the value of slot antenna. VSWR has a value of 1.0508971. The return loss is -32.105013. The gain is about 2.697 dB. The antenna has a line impedance of 100 ohm.
通信的进化正在迅速发展，人们竞相使通信比以前好得多，技术发展是5G通信，5G的数据速率和速度比4G更好。每一种通信技术都需要天线作为发射器和接收器来支持通信的正常工作。对映维瓦尔第(AVA)天线是5G通信中比其他对映维瓦尔第天线性能更好的一种。它具有高增益、改善回波损耗、高效率、增强波束宽度、低旁瓣电平(降低旁瓣电平和后瓣电平)、体积小、辐射方向图稳定、工作频率高(1 Ghz至100 Ghz)、前后比大等优点，真正适合5G通信。AVA (Antipodal Vivaldi Antenna)工作频率为2.6 GHz (2.6768 GHz)。该天线需要达到电气和电子工程师协会(IEEE)定义的标准，即VSWR为1，参考阻抗为100欧姆，s-参数低于- 20db。采用数学公式和实验方法进行了对映维瓦尔第天线的设计过程。为了模拟和优化它，它使用CST studio suite 2018软件。为了获得IEEE定义的标准，需要通过改变天线尺寸元素(馈线宽度)和配置来优化AVA。它的插槽可以导致物理优化。得到的结果是工作在2.6 GHz (2.6768 GHz)频率的槽形天线。所得结果为槽形天线的数值。VSWR的值为1.0508971。回波损耗为-32.105013。增益约为2.697 dB。天线的线阻抗为100欧姆。
{"title":"Design and Simulation of Antipodal Vivaldi Antenna (AVA) AT 2.6 GHz For 5G Communication Optimation","authors":"Andreas Renaldy Darmawidjaja, E. P. Wibowo","doi":"10.1109/ICIC54025.2021.9632925","DOIUrl":"https://doi.org/10.1109/ICIC54025.2021.9632925","url":null,"abstract":"The evolve of communication are growing rapidly, people competing to make communication much better than before, the technology develop is 5G communication, the data rate and speed that 5G given more better than 4G. Every communication technology requires an antenna as a transmitter and receiver to support communication working properly. Antipodal Vivaldi (AVA) is one type of Vivaldi Antenna which is better than other types of Antipodal Vivaldi for 5G communications. It has advantages for the High Gain, improve return loss, high efficiency, enhanced beamwidth, low sidelobe level, (Reduce the sidelobe level and back lobe level), Compact size, Stable radiation pattern, higher Operating Frequencies (1 Ghz to 100 Ghz) and more front to back ratio, which are really suitable for 5G communications. Antipodal Vivaldi Antenna (AVA) work at 2.6 GHz (2.6768 Ghz). The antenna needs to get the Institute of Electrical and Electronics Engineers (IEEE) defined standards which is VSWR 1, reference impedance 100 ohm, and s- parameter below -20dB. The Antipodal Vivaldi Antenna design process is carried out by using math formulation and experimental methods. For simulate and optimizing it, it uses CST studio suite 2018 software. To get the IEEE defined standards, AVA need to be optimize with changing antenna dimension elements (feed line width) and conFig. its slots which can lead to physic optimization. The results obtained in the form of slot antenna that works at a frequency of 2.6 GHz (2.6768 Ghz). The results obtained are the value of slot antenna. VSWR has a value of 1.0508971. The return loss is -32.105013. The gain is about 2.697 dB. The antenna has a line impedance of 100 ohm.","PeriodicalId":189541,"journal":{"name":"2021 Sixth International Conference on Informatics and Computing (ICIC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114971203","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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