Ruci Meiyanti, Muhammad Yusuf Bagus Rasyiidin, Fachroni Arbi Murad, Riri Fajriah
The use of public wireless networks and not integrated between tourist destinations is an obstacle for visitors when moving to other tourist destinations. This is an obstacle to internet access in the tourism era 4.0. To overcome this, smart wifi is needed. Smart wifi is an integrated internet network access among other tourist destinations so as to expand the connectivity and interaction of tourist visitors. This study used the NDLC method. The result of this research was the design of smart wifi which was built on a wireless network with captive portal technology and barcode scanning. Smart wifi contributes to the development of digitalization in smart tourism, making it easier for visitors to get internet access in various tourist destinations.
{"title":"Smart Wifi Design for Integrated Tourist Destinations in Smart Tourism","authors":"Ruci Meiyanti, Muhammad Yusuf Bagus Rasyiidin, Fachroni Arbi Murad, Riri Fajriah","doi":"10.31253/te.v5i1.766","DOIUrl":"https://doi.org/10.31253/te.v5i1.766","url":null,"abstract":"The use of public wireless networks and not integrated between tourist destinations is an obstacle for visitors when moving to other tourist destinations. This is an obstacle to internet access in the tourism era 4.0. To overcome this, smart wifi is needed. Smart wifi is an integrated internet network access among other tourist destinations so as to expand the connectivity and interaction of tourist visitors. This study used the NDLC method. The result of this research was the design of smart wifi which was built on a wireless network with captive portal technology and barcode scanning. Smart wifi contributes to the development of digitalization in smart tourism, making it easier for visitors to get internet access in various tourist destinations.","PeriodicalId":183093,"journal":{"name":"Tech-E","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122565289","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}
The study focus on to determine factors of the quality management on the higher education and analysis the effect of important factors of quality management. Factors of quality management in this study which covering of human resources, facilities and infrastructure, leadership, and organization. Sample study using students from several private universities in Lampung Province. Analysis method using integrating analysis by Analytical Hirarchy Method (AHP) and Multiple Regression Linear (MLR). Correlation test using the product moment stated quality management of higher education have a strong relationship to human resources, has a moderate relationship with infrastructures, and a weak relationship to the leadership and organizing. The result by multiple regression linear method reveal that significant effect on human resources, facilities and infrastructure, leadership and organizational on Quality Management in higher education. While, AHP method suggestion the result that the most important in Quality Management of Higher Education is a human resources owned by a higher education. This evidence contribute to the decision makers in universities which is priority and have to improve the quality of higher education management
{"title":"Integrating Analysis of Quality Management of Higher Education: Analytical Hierarchy Process and Multiple Linear Regression","authors":"Satria Abadi, Citrawati Jatiningrum, Samsurijal Hasan, Riki Riki","doi":"10.31253/te.v5i2.1114","DOIUrl":"https://doi.org/10.31253/te.v5i2.1114","url":null,"abstract":"The study focus on to determine factors of the quality management on the higher education and analysis the effect of important factors of quality management. Factors of quality management in this study which covering of human resources, facilities and infrastructure, leadership, and organization. Sample study using students from several private universities in Lampung Province. Analysis method using integrating analysis by Analytical Hirarchy Method (AHP) and Multiple Regression Linear (MLR). Correlation test using the product moment stated quality management of higher education have a strong relationship to human resources, has a moderate relationship with infrastructures, and a weak relationship to the leadership and organizing. The result by multiple regression linear method reveal that significant effect on human resources, facilities and infrastructure, leadership and organizational on Quality Management in higher education. While, AHP method suggestion the result that the most important in Quality Management of Higher Education is a human resources owned by a higher education. This evidence contribute to the decision makers in universities which is priority and have to improve the quality of higher education management","PeriodicalId":183093,"journal":{"name":"Tech-E","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114234891","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}
This study aims to simulate the calculation of saving stocks based on historical data for the past 10 years for the period January 2010 - January 2020, because saving simulations based on historical data are still very rare, so a simulation application for saving stock calculations based on historical data is made that can help customers. investors in simulating the calculation of saving stocks so that it can be used as learning to determine how to save the right way that can produce a good return. This application is created using a waterfall model. From the application made, it is expected to know the good return results of the 10 issuers used in this study, and in making basic applications on user requirements obtained through several respondents through online questionnaires and processed with requirement elicitation techniques. With the application of a stock saving calculation simulation application, it is hoped that it can be a lesson for investors in saving stocks and can also be a lesson for potential investors and can also make it easier for investors to do calculations because the calculation process is computerized.
{"title":"Stocks Saving Simulation based on Historical Data Web-based","authors":"Mesakh Septiadi Simijaya, Aditiya Hermawan","doi":"10.31253/te.v5i1.622","DOIUrl":"https://doi.org/10.31253/te.v5i1.622","url":null,"abstract":"This study aims to simulate the calculation of saving stocks based on historical data for the past 10 years for the period January 2010 - January 2020, because saving simulations based on historical data are still very rare, so a simulation application for saving stock calculations based on historical data is made that can help customers. investors in simulating the calculation of saving stocks so that it can be used as learning to determine how to save the right way that can produce a good return. This application is created using a waterfall model. From the application made, it is expected to know the good return results of the 10 issuers used in this study, and in making basic applications on user requirements obtained through several respondents through online questionnaires and processed with requirement elicitation techniques. With the application of a stock saving calculation simulation application, it is hoped that it can be a lesson for investors in saving stocks and can also be a lesson for potential investors and can also make it easier for investors to do calculations because the calculation process is computerized.","PeriodicalId":183093,"journal":{"name":"Tech-E","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130987728","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}
The process of filing a letter is usually done by recording it in an archive book, then the letter is stored in a place or filing cabinet that has been provided. This is considered not optimal, in providing correspondence services that are needed by the community. Due to the limited staff working in the village office, the process takes a long time. This research discussed how to create and design digital archive applications and correspondence starting from data collection methods (interviews, observations and documentation) using extreme programming development methods, system design using UML with Use case Diagram design model, and CRC Card. TRITAM model test results that have been conducted involving 16 respondents showed results of 82.56% with good criteria. From the results of these tests showed good results for prototype users for the mail filing system.
{"title":"Digital Archive System Development in Improving Public Services Using Extreme Programming","authors":"Arisantoso Arisantoso, Jefri Rahmadian, Harriansyah Harriansyah, Dwi Sidik Permana, Imam Ahmad","doi":"10.31253/te.v5i1.941","DOIUrl":"https://doi.org/10.31253/te.v5i1.941","url":null,"abstract":"The process of filing a letter is usually done by recording it in an archive book, then the letter is stored in a place or filing cabinet that has been provided. This is considered not optimal, in providing correspondence services that are needed by the community. Due to the limited staff working in the village office, the process takes a long time. This research discussed how to create and design digital archive applications and correspondence starting from data collection methods (interviews, observations and documentation) using extreme programming development methods, system design using UML with Use case Diagram design model, and CRC Card. TRITAM model test results that have been conducted involving 16 respondents showed results of 82.56% with good criteria. From the results of these tests showed good results for prototype users for the mail filing system.","PeriodicalId":183093,"journal":{"name":"Tech-E","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130456621","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}
Sentiment analysis is one way that is widely used to identify the beginning of public opinion in various fields of life which are associated with very massive and a lot of information through social media. This study aims to compare several algorithms in machine learning to see the best ability in sentiment classification. The research dataset uses a dataset of public opinion related to tourism in Indonesia. The number of datasets used is 10,228 twitter data that have been cleaned and labelled. The machine learning algorithm used is Logistic Regression, KNN, AdaBoost, Decision Tree, SVM, Random Forest and Gaussian. The seven algorithms for sentiment classification from the Twitter public opinion each produce a Gaussian accuracy of 0.52; SVM 0.78; KNN 0.98; Logistic Regression, Random Forest, Decision Tree, AdaBoost of 0.99. This study shows that the selection of the right machine learning algorithm will have a very good impact on the classification of public opinion through social media
情感分析是一种广泛用于识别生活各个领域中公众舆论开始的方法，这些领域通过社交媒体与非常大量和大量的信息相关。本研究旨在比较机器学习中的几种算法，以了解情感分类的最佳能力。研究数据集使用了与印尼旅游业相关的公众意见数据集。使用的数据集数量为10,228个已被清理和标记的twitter数据。使用的机器学习算法有Logistic Regression, KNN, AdaBoost, Decision Tree, SVM, Random Forest和Gaussian。7种算法对Twitter舆情进行情感分类，其高斯精度均为0.52;支持向量机0.78;然而,0.98;逻辑回归，随机森林，决策树，AdaBoost为0.99。本研究表明，选择正确的机器学习算法会对通过社交媒体进行舆论分类产生非常好的影响
{"title":"Comparison of Seven Machine Learning Algorithms in the Classification of Public Opinion","authors":"Sri Redjeki, Setyawan Widyarto","doi":"10.31253/te.v5i1.1046","DOIUrl":"https://doi.org/10.31253/te.v5i1.1046","url":null,"abstract":"Sentiment analysis is one way that is widely used to identify the beginning of public opinion in various fields of life which are associated with very massive and a lot of information through social media. This study aims to compare several algorithms in machine learning to see the best ability in sentiment classification. The research dataset uses a dataset of public opinion related to tourism in Indonesia. The number of datasets used is 10,228 twitter data that have been cleaned and labelled. The machine learning algorithm used is Logistic Regression, KNN, AdaBoost, Decision Tree, SVM, Random Forest and Gaussian. The seven algorithms for sentiment classification from the Twitter public opinion each produce a Gaussian accuracy of 0.52; SVM 0.78; KNN 0.98; Logistic Regression, Random Forest, Decision Tree, AdaBoost of 0.99. This study shows that the selection of the right machine learning algorithm will have a very good impact on the classification of public opinion through social media","PeriodicalId":183093,"journal":{"name":"Tech-E","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126864081","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}
Trinugi Wira Harjanti, Hari Setiyani, Joko Trianto, Yuri Rahmanto
Mint is a plant that has many benefits and uses. However, some people are not familiar with the types of mint leaves because they cannot tell the difference. Actually, if you look closely, mint leaves have their own characteristic shape and texture. However, most people judge mint leaves to have a shape similar to other leaves so it is difficult to tell them apart. This paper aims to classify the types of mint leaves using the Euclidean distance algorithm and K-Means clustering with shape and texture feature extraction. The K-Means Clustering Algorithm functions as a segmentation so that the image to be classified can be separated from other objects. In the feature extraction process, metric and eccentricity parameters are used. Meanwhile, for texture feature extraction, use the parameters in the Gray Level Co-occurence Matrix (GLCM). Furthermore, the classification process uses the Euclidean Distance algorithm which has a function to represent the level of similarity between two images by taking into account the distance value from the identified image. Based on the results of the evaluation using a confusion matrix by calculating precision, recall and accuracy, the precision value is 82%, recal is 84% ​​and accuracy is 83%.
{"title":"Classification of Mint Leaf Types Based on the Image Using Euclidean Distance and K-Means Clustering with Shape and Texture Feature Extraction","authors":"Trinugi Wira Harjanti, Hari Setiyani, Joko Trianto, Yuri Rahmanto","doi":"10.31253/te.v5i1.940","DOIUrl":"https://doi.org/10.31253/te.v5i1.940","url":null,"abstract":"Mint is a plant that has many benefits and uses. However, some people are not familiar with the types of mint leaves because they cannot tell the difference. Actually, if you look closely, mint leaves have their own characteristic shape and texture. However, most people judge mint leaves to have a shape similar to other leaves so it is difficult to tell them apart. This paper aims to classify the types of mint leaves using the Euclidean distance algorithm and K-Means clustering with shape and texture feature extraction. The K-Means Clustering Algorithm functions as a segmentation so that the image to be classified can be separated from other objects. In the feature extraction process, metric and eccentricity parameters are used. Meanwhile, for texture feature extraction, use the parameters in the Gray Level Co-occurence Matrix (GLCM). Furthermore, the classification process uses the Euclidean Distance algorithm which has a function to represent the level of similarity between two images by taking into account the distance value from the identified image. Based on the results of the evaluation using a confusion matrix by calculating precision, recall and accuracy, the precision value is 82%, recal is 84% ​​and accuracy is 83%.","PeriodicalId":183093,"journal":{"name":"Tech-E","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128015110","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}
Blibli, Bukalapak, JD.id, Lazada Indonesia, Shopee and Tokopedia are designated as the 6 largest e-commerce in Indonesia based on the 2019 Consumer Pulse eIQ. The increase in e-commerce transactions has greatly impacted the positive growth of e-commerce. The positive growth in e-commerce in Indonesia has resulted in Indonesia being predicted to become the market leader in Southeast Asia. E-commerce customers have personal preferences in choosing e-commerce for transactions. Various criteria make customers confused because of the intense competition between e-commerce companies. DSS is a solution in choosing the right e-commerce for each customer's preferences. The FUCOM-SAW method can be used in calculations to determine favorite e-commerce in Indonesia. The CRISP-DM framework also helps in preparing the research flow well. 4 decision makers were used to assign weighting criteria using FUCOM. The results of this study indicate that the Bukalapak alternative is the best e-commerce, with a preference value of 0.8701. The second and third place respectively are Blibli and Tokopedia. The weighting of the criteria by decision makers, the alternative normalization process and the technique of calculating preference values ​​have a significant effect on the ranking results.
布卡拉帕克，JD。根据2019年消费者脉搏eIQ, id, Lazada Indonesia, Shopee和Tokopedia被指定为印度尼西亚六大电子商务。电子商务交易的增加极大地影响了电子商务的正增长。印度尼西亚电子商务的积极增长导致印度尼西亚被预测将成为东南亚市场的领导者。电子商务客户在选择电子商务进行交易时有个人偏好。由于电子商务公司之间的激烈竞争，各种各样的标准使客户感到困惑。DSS是一种解决方案，可以根据每个客户的偏好选择合适的电子商务。FUCOM-SAW方法可用于计算确定印度尼西亚最受欢迎的电子商务。CRISP-DM框架还有助于为研究流程做好准备。4位决策者使用FUCOM分配权重标准。本研究结果表明，Bukalapak替代品是最好的电子商务，其偏好值为0.8701。第二名和第三名分别是Blibli和Tokopedia。决策者对标准的权重、备选归一化过程和偏好值计算技术对排名结果有显著影响。
{"title":"Implementation of the FUCOM-SAW Method on E-Commerce Selection DSS in Indonesia","authors":"G. Mahendra","doi":"10.31253/te.v5i1.662","DOIUrl":"https://doi.org/10.31253/te.v5i1.662","url":null,"abstract":"Blibli, Bukalapak, JD.id, Lazada Indonesia, Shopee and Tokopedia are designated as the 6 largest e-commerce in Indonesia based on the 2019 Consumer Pulse eIQ. The increase in e-commerce transactions has greatly impacted the positive growth of e-commerce. The positive growth in e-commerce in Indonesia has resulted in Indonesia being predicted to become the market leader in Southeast Asia. E-commerce customers have personal preferences in choosing e-commerce for transactions. Various criteria make customers confused because of the intense competition between e-commerce companies. DSS is a solution in choosing the right e-commerce for each customer's preferences. The FUCOM-SAW method can be used in calculations to determine favorite e-commerce in Indonesia. The CRISP-DM framework also helps in preparing the research flow well. 4 decision makers were used to assign weighting criteria using FUCOM. The results of this study indicate that the Bukalapak alternative is the best e-commerce, with a preference value of 0.8701. The second and third place respectively are Blibli and Tokopedia. The weighting of the criteria by decision makers, the alternative normalization process and the technique of calculating preference values ​​have a significant effect on the ranking results.","PeriodicalId":183093,"journal":{"name":"Tech-E","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124840593","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}
Now more people are using motorized vehicles. In addition, the use of technology is also increasing, people are increasingly experiencing fast-paced services. However, in the midst of busy society and online services, some services still have to be done manually, one of which is register for a motorcycle or car repair shop. There are still many people difficult to find the right, closest, and comfortable with the needs of their vehicle. With the existing problems, a vehicle service ordering system is needed that can serve the community quickly and practically, which can be accessed by many people, especially in Tangerang City. With a vehicle service ordering system for motorcycles or car repairshop, people can easily find a repair shop that is the closest to their location and can order without having to wait in long queues. The design of the system for the closest repair shop locations uses the Dijkstra method. The workings of Dijkstra's Algorithm is to create a path to one optimal node at each step. Dijkstra's algorithm has the property to find the point whose distance from the starting point is the shortest. To find out whether the system has been accepted and has met the requirements, the system is tested using the User Acceptance Test (UAT) method, and from the test results, 85.1% of users are satisfied with the system.
{"title":"Information System Design of Online Motorcycle and Car Repair Shop Using Dijkstra Method","authors":"Vira Oktaviani Wijaya, Benny Daniawan","doi":"10.31253/te.v5i1.649","DOIUrl":"https://doi.org/10.31253/te.v5i1.649","url":null,"abstract":"Now more people are using motorized vehicles. In addition, the use of technology is also increasing, people are increasingly experiencing fast-paced services. However, in the midst of busy society and online services, some services still have to be done manually, one of which is register for a motorcycle or car repair shop. There are still many people difficult to find the right, closest, and comfortable with the needs of their vehicle. With the existing problems, a vehicle service ordering system is needed that can serve the community quickly and practically, which can be accessed by many people, especially in Tangerang City. With a vehicle service ordering system for motorcycles or car repairshop, people can easily find a repair shop that is the closest to their location and can order without having to wait in long queues. The design of the system for the closest repair shop locations uses the Dijkstra method. The workings of Dijkstra's Algorithm is to create a path to one optimal node at each step. Dijkstra's algorithm has the property to find the point whose distance from the starting point is the shortest. To find out whether the system has been accepted and has met the requirements, the system is tested using the User Acceptance Test (UAT) method, and from the test results, 85.1% of users are satisfied with the system.","PeriodicalId":183093,"journal":{"name":"Tech-E","volume":"232 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130621507","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}
At this time the public is already aware of the value of sharing with the needy, so the search for orphanages is increasing drastically. Similarly, the use of maps on smartphones is increasing because people are aware of technology and use it in every day. But often people are troubled to know the location of the orphanage in around, as well as the condition of the orphanage which includes the number of children, the number of caretakers, and what are the needs of the orphanage without coming to the location of the orphanage. In addition, there are often overlapping scheduling problems that often make people who want to hold activities experience unilateral cancellation and there is also the issue of donations where people are confused how to give a little from their income to the orphanage without having to come to the location. With agile development systems method designed nearby location application using Google place, which is obtained the core of the problem is the difficulty of finding information orphanages and not easy to find orphanages around. Therefore, with this nearby location application is expected to help the public to find information on orphanages closest to their location, as well as be able to schedule activities that can be monitored in real time. So there is no overlap back on the activity. In addition, there is a function of giving donations, so that people can donate without having to come to the orphanage.
{"title":"Android – Based Nearby Location App For Orphanage Searches Using Google Place API Technology With Agile System Development Method","authors":"Setiadi Sutedja, Edy Edy","doi":"10.31253/te.v5i1.626","DOIUrl":"https://doi.org/10.31253/te.v5i1.626","url":null,"abstract":"At this time the public is already aware of the value of sharing with the needy, so the search for orphanages is increasing drastically. Similarly, the use of maps on smartphones is increasing because people are aware of technology and use it in every day. But often people are troubled to know the location of the orphanage in around, as well as the condition of the orphanage which includes the number of children, the number of caretakers, and what are the needs of the orphanage without coming to the location of the orphanage. In addition, there are often overlapping scheduling problems that often make people who want to hold activities experience unilateral cancellation and there is also the issue of donations where people are confused how to give a little from their income to the orphanage without having to come to the location. With agile development systems method designed nearby location application using Google place, which is obtained the core of the problem is the difficulty of finding information orphanages and not easy to find orphanages around. Therefore, with this nearby location application is expected to help the public to find information on orphanages closest to their location, as well as be able to schedule activities that can be monitored in real time. So there is no overlap back on the activity. In addition, there is a function of giving donations, so that people can donate without having to come to the orphanage.","PeriodicalId":183093,"journal":{"name":"Tech-E","volume":"185 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134119543","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}
Ida Ayu Putu Anggie Sinthiya, Keni Puspita Sari, Muhamad Muslihudin, Suhendra Suhendra
Technology can be used in various fields. One of them is in the field of Education. In this case, it can be done to process databases, and process academic information data, for example: lecture systems, assessment systems, curriculum information, education management or learning materials. It can also implement the system gradually starting from a smaller scope to expanding, making it easier to manage the use of IT in the process of providing education. Electronic Attendance with an Android-based QR code is an application which is able to read the QR code from each user or student scanned by the lecturer in the attendance list process for a course. The development of the Electronic Attendance system with QR code based on Android uses the Software Development Life Cycle (SDLC) system and is described by the Data Flow Diagram (DFD), Entity Relationship Diagram (ERD) and Flowchart models followed by web design using Hypertext Preprocessor (PHP) programming, and My Structured Query Language (MySQL), Javascript, and Cascading Style Sheet (CSS) which produces responsive websites and is converted with android studio into applications
{"title":"Electronic Attendance with Android-Based QR Code at STMIK Pringsewu to Improve Student and Lecturer Discipline in Lectures","authors":"Ida Ayu Putu Anggie Sinthiya, Keni Puspita Sari, Muhamad Muslihudin, Suhendra Suhendra","doi":"10.31253/te.v5i1.663","DOIUrl":"https://doi.org/10.31253/te.v5i1.663","url":null,"abstract":"Technology can be used in various fields. One of them is in the field of Education. In this case, it can be done to process databases, and process academic information data, for example: lecture systems, assessment systems, curriculum information, education management or learning materials. It can also implement the system gradually starting from a smaller scope to expanding, making it easier to manage the use of IT in the process of providing education. Electronic Attendance with an Android-based QR code is an application which is able to read the QR code from each user or student scanned by the lecturer in the attendance list process for a course. The development of the Electronic Attendance system with QR code based on Android uses the Software Development Life Cycle (SDLC) system and is described by the Data Flow Diagram (DFD), Entity Relationship Diagram (ERD) and Flowchart models followed by web design using Hypertext Preprocessor (PHP) programming, and My Structured Query Language (MySQL), Javascript, and Cascading Style Sheet (CSS) which produces responsive websites and is converted with android studio into applications","PeriodicalId":183093,"journal":{"name":"Tech-E","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126644989","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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