One of moving object problems is the incomplete data that acquired by Geo-tracking technology. This phenomenon can be found in aircraft ground-based tracking with data loss come near to 5 minutes. It needs path smoothing process to complete the data. One solution of path smoothing is using physics of motion, while this research performs path smoothing process using machine learning algorithm that is Support Vector Regression (SVR). This study will optimize the SVR configuration parameters such as kernel, common, gamma, epsilon and degree. Support Vector Regression will predict value of the data lost from aircraft tracking data. We use combination of mean absolute error (MAE) and mean absolute percentage error (MAPE) to get more accuracy. MAE will explain the average value of error that occurs, while MAPE will explain the error percentage to the data. In the experiment, the best error value MAE 0.52 and MAPE 2.07, which means error data ± 0.52, this is equal to 2.07% of the overall data value. Keywords: Moving Object, Path Smoothing, Support Vector Regression, MAE
{"title":"Path Smoothing With Support Vector Regression","authors":"Donni Richasdy, Saiful Akbar","doi":"10.31289/jite.v4i1.3856","DOIUrl":"https://doi.org/10.31289/jite.v4i1.3856","url":null,"abstract":"One of moving object problems is the incomplete data that acquired by Geo-tracking technology. This phenomenon can be found in aircraft ground-based tracking with data loss come near to 5 minutes. It needs path smoothing process to complete the data. One solution of path smoothing is using physics of motion, while this research performs path smoothing process using machine learning algorithm that is Support Vector Regression (SVR). This study will optimize the SVR configuration parameters such as kernel, common, gamma, epsilon and degree. Support Vector Regression will predict value of the data lost from aircraft tracking data. We use combination of mean absolute error (MAE) and mean absolute percentage error (MAPE) to get more accuracy. MAE will explain the average value of error that occurs, while MAPE will explain the error percentage to the data. In the experiment, the best error value MAE 0.52 and MAPE 2.07, which means error data ± 0.52, this is equal to 2.07% of the overall data value. Keywords: Moving Object, Path Smoothing, Support Vector Regression, MAE","PeriodicalId":43632,"journal":{"name":"Journal of Information Technology Education-Innovations in Practice","volume":"23 1","pages":"142-150"},"PeriodicalIF":1.2,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74935214","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}
Nowadays, climbing mountains has become a lifestyle for young people. Outdoor industries that produce clothing, bags and sports shoes participate in developing and following the desires of the market. Each company in producing its products has a special brand. Shop Pos 1 is one of the shops that sell various climbing equipment commonly used by climbers to climb mountains. In addition, Pos 1 stores also find it difficult to get updated information about the level of sales per period. Therefore, we need a decision support systems and methods that can be used to determine business strategies that can provide efficient and effective information, namely data mining using a priori technology association methods. The author chooses mountain bag products only as research material by selecting brands, completing Avtech, Consina, Co-tracks, Cozmed, Eiger, Forester, Rei, Loss. In analyzing the data, the writer uses a priori algorithm calculation by testing the hypothesis of two variables between the value of support and the value of trust. After that, a priori algorithm is calculated using Tanagra. Based on analysis conducted by the author, the operator most preferred by climbers is Avtech, Consina, Cozmed. From these results, it can be used by Pos 1 to prepare brand inventory of mountain bag products that are widely bought by buyers and increase brand inventory. Keywords: Bag Brand, Data Mining, apriori algorithm.
{"title":"Application of Apriori Algorithm Method in Sales Analysis of Mountain Bag Brands in Post Stores 1","authors":"A. Salim, Mochammad Nizar","doi":"10.31289/jite.v4i1.2980","DOIUrl":"https://doi.org/10.31289/jite.v4i1.2980","url":null,"abstract":"Nowadays, climbing mountains has become a lifestyle for young people. Outdoor industries that produce clothing, bags and sports shoes participate in developing and following the desires of the market. Each company in producing its products has a special brand. Shop Pos 1 is one of the shops that sell various climbing equipment commonly used by climbers to climb mountains. In addition, Pos 1 stores also find it difficult to get updated information about the level of sales per period. Therefore, we need a decision support systems and methods that can be used to determine business strategies that can provide efficient and effective information, namely data mining using a priori technology association methods. The author chooses mountain bag products only as research material by selecting brands, completing Avtech, Consina, Co-tracks, Cozmed, Eiger, Forester, Rei, Loss. In analyzing the data, the writer uses a priori algorithm calculation by testing the hypothesis of two variables between the value of support and the value of trust. After that, a priori algorithm is calculated using Tanagra. Based on analysis conducted by the author, the operator most preferred by climbers is Avtech, Consina, Cozmed. From these results, it can be used by Pos 1 to prepare brand inventory of mountain bag products that are widely bought by buyers and increase brand inventory. Keywords: Bag Brand, Data Mining, apriori algorithm.","PeriodicalId":43632,"journal":{"name":"Journal of Information Technology Education-Innovations in Practice","volume":"15 1","pages":"1-10"},"PeriodicalIF":1.2,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90679324","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}
During the rainy season, several regions in Indonesia experienced floods even to the capital of Indonesia also flooded. Some of the causes are the high intensity of continuous rain, clogged or non-smooth drainage, high tides to accommodate the flow of water from rivers, other causes such as forest destruction, shallow and full of garbage and other causes. Every flood disaster comes, often harming the residents who experience it. The late anticipation from the community and the absence of an early warning system or information that indicates that there will be a flood so that the community is not prepared to face floods that cause a lot of losses. Therefore it is necessary to have a detection system to provide early warning if floods will occur, this is very important to prevent material losses from flooded residents. From this problem the researchers designed an internet-based flood detection System of Things (IoT). This tool can later be controlled via a smartphone remotely and can send messages Telegram messenger to citizens if the detector detects a flood will occur. Keywords: Flooding, Smartphone, Telegram messenger, Internet of Thing (IoT).
{"title":"Internet-Based Flood Detection System (Iot) and Telegram Messenger Using Mcu Node and Water Level Sensor","authors":"N. Nanda, Rizalul Akram, Liza Fitria","doi":"10.31289/jite.v4i1.3892","DOIUrl":"https://doi.org/10.31289/jite.v4i1.3892","url":null,"abstract":"During the rainy season, several regions in Indonesia experienced floods even to the capital of Indonesia also flooded. Some of the causes are the high intensity of continuous rain, clogged or non-smooth drainage, high tides to accommodate the flow of water from rivers, other causes such as forest destruction, shallow and full of garbage and other causes. Every flood disaster comes, often harming the residents who experience it. The late anticipation from the community and the absence of an early warning system or information that indicates that there will be a flood so that the community is not prepared to face floods that cause a lot of losses. Therefore it is necessary to have a detection system to provide early warning if floods will occur, this is very important to prevent material losses from flooded residents. From this problem the researchers designed an internet-based flood detection System of Things (IoT). This tool can later be controlled via a smartphone remotely and can send messages Telegram messenger to citizens if the detector detects a flood will occur. Keywords: Flooding, Smartphone, Telegram messenger, Internet of Thing (IoT).","PeriodicalId":43632,"journal":{"name":"Journal of Information Technology Education-Innovations in Practice","volume":"97 1","pages":"230-235"},"PeriodicalIF":1.2,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85900967","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}
Data security by using an alphanumeric combination password is no longer used, so it needs to be added security that is difficult to be manipulated by certain people. One type of security is the type of biometrics technology using face recognition which has different characteristics by combining the Viola-Jones algorithm to detect facial features, GLCM (Gray Level Co-occurrence Matrix) for extracting the texture characteristics of an image, and Cosine Similarity for the measurement of the proximity of the data (image matching). The image will be detected using the Viola-Jones algorithm to get face, eyes, nose, and mouth. The image detection results will be calculated the value of the texture characteristics with the GLCM (Gray Level Cooccurrence Matrix) algorithm. Image matching using cosine similarity will determine or match the data stored in the database with new image input until identification results are obtained. The results obtained in this study get the level of accuracy of the identification of the three algorithms by 77.20% with the amount of data that was correctly identified as many as 386 out of 500 images. Keywords: Security, face recognition, Viola-Jones, Cosine Similarity.
{"title":"Face Identification on Login Security Using Algorithm Combination of Viola-Jones and Cosine Similarity","authors":"F. Azmi, A. Saleh, N. Dharshinni","doi":"10.31289/jite.v4i1.3885","DOIUrl":"https://doi.org/10.31289/jite.v4i1.3885","url":null,"abstract":"Data security by using an alphanumeric combination password is no longer used, so it needs to be added security that is difficult to be manipulated by certain people. One type of security is the type of biometrics technology using face recognition which has different characteristics by combining the Viola-Jones algorithm to detect facial features, GLCM (Gray Level Co-occurrence Matrix) for extracting the texture characteristics of an image, and Cosine Similarity for the measurement of the proximity of the data (image matching). The image will be detected using the Viola-Jones algorithm to get face, eyes, nose, and mouth. The image detection results will be calculated the value of the texture characteristics with the GLCM (Gray Level Cooccurrence Matrix) algorithm. Image matching using cosine similarity will determine or match the data stored in the database with new image input until identification results are obtained. The results obtained in this study get the level of accuracy of the identification of the three algorithms by 77.20% with the amount of data that was correctly identified as many as 386 out of 500 images. Keywords: Security, face recognition, Viola-Jones, Cosine Similarity.","PeriodicalId":43632,"journal":{"name":"Journal of Information Technology Education-Innovations in Practice","volume":"33 1","pages":"203-211"},"PeriodicalIF":1.2,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90329097","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}
In face recognition there are two commonly used open-source libraries namely Dlib and OpenCV. Analysis of facial recognition algorithms is needed as reference for software developers who want to implement facial recognition features into an application program. From Dlib algorithm to be analyzed is CNN and HoG, from OpenCV algorithm is DNN and HAAR Cascades. These four algorithms are analyzed in terms of speed and accuracy. The same image dataset will be used to test, along with some actual images to get a more general analysis of how algorithm will appear in real life scenarios. The programming language used for face recognition algorithms is Python. The image dataset will come from LFW (Labeled Faces in the Wild), and AT&T, both of which are available and ready to be downloaded from the internet. Pictures of people around the UIB (Batam International University) is used for actual images dataset. HoG algorithm is fastest in speed test (0.011 seconds / image), but the accuracy rate is lower (FRR = 27.27%, FAR = 0%). DNN algorithm is the highest in level of accuracy (FRR = 11.69%, FAR = 2.6%) but the lowest speed (0.119 seconds / picture). There is no best algorithm, each algorithm has advantages and disadvantages. Keywords: Python, Face Recognition, Analysis, Speed, Accuracy.
在人脸识别中，有两个常用的开源库，即Dlib和OpenCV。对于想要在应用程序中实现人脸识别功能的软件开发人员来说，需要对人脸识别算法进行分析作为参考。从Dlib算法要分析的是CNN和HoG，从OpenCV算法要分析的是DNN和HAAR级联。从速度和精度两方面对这四种算法进行了分析。将使用相同的图像数据集进行测试，并使用一些实际图像来更全面地分析算法在现实生活场景中的表现。用于人脸识别算法的编程语言是Python。图像数据集将来自LFW(野外标记的面孔)和AT&T，两者都是可用的，可以从互联网上下载。巴淡国际大学周围的人的照片用于实际图像数据集。HoG算法在速度测试中速度最快(0.011秒/幅)，但准确率较低(FRR = 27.27%， FAR = 0%)。DNN算法的准确率最高(FRR = 11.69%， FAR = 2.6%)，但速度最低(0.119秒/张)。没有最好的算法，每种算法都有优点和缺点。关键词:Python，人脸识别，分析，速度，准确性。
{"title":"Analysis of Face Recognition Algorithm: Dlib and OpenCV","authors":"S. Suwarno, Kevin Kevin","doi":"10.31289/jite.v4i1.3865","DOIUrl":"https://doi.org/10.31289/jite.v4i1.3865","url":null,"abstract":"In face recognition there are two commonly used open-source libraries namely Dlib and OpenCV. Analysis of facial recognition algorithms is needed as reference for software developers who want to implement facial recognition features into an application program. From Dlib algorithm to be analyzed is CNN and HoG, from OpenCV algorithm is DNN and HAAR Cascades. These four algorithms are analyzed in terms of speed and accuracy. The same image dataset will be used to test, along with some actual images to get a more general analysis of how algorithm will appear in real life scenarios. The programming language used for face recognition algorithms is Python. The image dataset will come from LFW (Labeled Faces in the Wild), and AT&T, both of which are available and ready to be downloaded from the internet. Pictures of people around the UIB (Batam International University) is used for actual images dataset. HoG algorithm is fastest in speed test (0.011 seconds / image), but the accuracy rate is lower (FRR = 27.27%, FAR = 0%). DNN algorithm is the highest in level of accuracy (FRR = 11.69%, FAR = 2.6%) but the lowest speed (0.119 seconds / picture). There is no best algorithm, each algorithm has advantages and disadvantages. Keywords: Python, Face Recognition, Analysis, Speed, Accuracy.","PeriodicalId":43632,"journal":{"name":"Journal of Information Technology Education-Innovations in Practice","volume":"4 1","pages":"173-184"},"PeriodicalIF":1.2,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83003782","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}
Web server is software that provides data services in the form of HTTP (Hypertext Transfer Protocol) requests and responses in the form of HTML documents (Hypertext Markup Language) with the aim of managing data in the form of text files, images, videos and files. But in managing large amounts of data, good security monitoring is needed so that the data stored on the web server is not easily hacked. To protect the web server from hackers need an application to detect activities that are considered suspicious or possible hacking activities. By utilizing logs from a web server that is processed using the Jaro Winkler algorithm to see hacking attempts that produce a matrix and hacking activity reports to the admin. Thus the web server admin can see suspicious activity on the web server directly. Keywords: Web Server, Jaro Winkler Algorithm.
{"title":"Detection of Attacks on Apache2 Web Server Using Genetic Algorithm Based On Jaro Winkler Algorithm","authors":"M. Maulana","doi":"10.31289/jite.v4i1.3873","DOIUrl":"https://doi.org/10.31289/jite.v4i1.3873","url":null,"abstract":"Web server is software that provides data services in the form of HTTP (Hypertext Transfer Protocol) requests and responses in the form of HTML documents (Hypertext Markup Language) with the aim of managing data in the form of text files, images, videos and files. But in managing large amounts of data, good security monitoring is needed so that the data stored on the web server is not easily hacked. To protect the web server from hackers need an application to detect activities that are considered suspicious or possible hacking activities. By utilizing logs from a web server that is processed using the Jaro Winkler algorithm to see hacking attempts that produce a matrix and hacking activity reports to the admin. Thus the web server admin can see suspicious activity on the web server directly. Keywords: Web Server, Jaro Winkler Algorithm.","PeriodicalId":43632,"journal":{"name":"Journal of Information Technology Education-Innovations in Practice","volume":"8 1","pages":"185-192"},"PeriodicalIF":1.2,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84262018","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}
Ahmad Fachrurozi, Mufid Junaedi, Jordy Lasmana Putra, W. Gata
This data processing has the aim to increase the company's turnover, because by being aware of how the interest in buying goods works, the company can buy products other than the main products that it buys. In increasing company revenue can be done using the Data Mining process, one of which uses a priori algorithm and association techniques. With this a priori algorithm found association technique which later can be used as a pattern of purchasing goods by consumers, this study uses a data repository of 958 data consisting of 45 transactions. From the results obtained goods with the name Paper Chain Kit 50's Christmas is a product that is often bought by consumers and it is known that the most frequent combination patterns are the Retro Spot Paper Chain Kit and the Paper Chain Kit 50's Christmas. So that with known buying patterns, the company manager can predict future market needs, and can calculate the stock of goods that must be reproduced, and goods whose stock must be reduced, and also with the results of the association the manager can manage the layout of the product to be better. Keywords: Apriori Algorithm, Sales Data, Retail.
{"title":"Algorithm Implementation Of Interest Buy Apriori Data On Consumer Retail Sales In Industry","authors":"Ahmad Fachrurozi, Mufid Junaedi, Jordy Lasmana Putra, W. Gata","doi":"10.31289/jite.v4i1.3775","DOIUrl":"https://doi.org/10.31289/jite.v4i1.3775","url":null,"abstract":"This data processing has the aim to increase the company's turnover, because by being aware of how the interest in buying goods works, the company can buy products other than the main products that it buys. In increasing company revenue can be done using the Data Mining process, one of which uses a priori algorithm and association techniques. With this a priori algorithm found association technique which later can be used as a pattern of purchasing goods by consumers, this study uses a data repository of 958 data consisting of 45 transactions. From the results obtained goods with the name Paper Chain Kit 50's Christmas is a product that is often bought by consumers and it is known that the most frequent combination patterns are the Retro Spot Paper Chain Kit and the Paper Chain Kit 50's Christmas. So that with known buying patterns, the company manager can predict future market needs, and can calculate the stock of goods that must be reproduced, and goods whose stock must be reduced, and also with the results of the association the manager can manage the layout of the product to be better. Keywords: Apriori Algorithm, Sales Data, Retail.","PeriodicalId":43632,"journal":{"name":"Journal of Information Technology Education-Innovations in Practice","volume":"12 1","pages":"48-56"},"PeriodicalIF":1.2,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85966711","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}
A portable computer is a technology tool widely used among students and students. With a very helpful role as in typing needs, presentations, and math calculations. A variety of carry-on computers that many certainly make one difficult to determine a decent and good portable computer to use. In general, in the process of selecting a portable computer, there is no recognized standard to determine the recommended portable computer level. The purpose in this research is to produce predictive values that will be a reference in supporting decisions in determining a portable computer that complies with hardware component criteria and pricing. This study implemented FIS Mamdani models with the analysis stage of the formation of fuzzy sets, application of implications function, rule composition and defuzification. The result of this research is an output of predictive value based on hardware component inputs and prices that will assist the user in supporting decisions in determining the best carry-on computer and according to what they want. Keywords: Predictions, Fuzzy Inference System, Mamdani methods, portable computers, students.
{"title":"The Application of Mamdani Method for Predicting The Best Portable Computer Based on Hardware and Price","authors":"Gelar Lailatul Qodar","doi":"10.31289/jite.v4i1.3770","DOIUrl":"https://doi.org/10.31289/jite.v4i1.3770","url":null,"abstract":"A portable computer is a technology tool widely used among students and students. With a very helpful role as in typing needs, presentations, and math calculations. A variety of carry-on computers that many certainly make one difficult to determine a decent and good portable computer to use. In general, in the process of selecting a portable computer, there is no recognized standard to determine the recommended portable computer level. The purpose in this research is to produce predictive values that will be a reference in supporting decisions in determining a portable computer that complies with hardware component criteria and pricing. This study implemented FIS Mamdani models with the analysis stage of the formation of fuzzy sets, application of implications function, rule composition and defuzification. The result of this research is an output of predictive value based on hardware component inputs and prices that will assist the user in supporting decisions in determining the best carry-on computer and according to what they want. Keywords: Predictions, Fuzzy Inference System, Mamdani methods, portable computers, students.","PeriodicalId":43632,"journal":{"name":"Journal of Information Technology Education-Innovations in Practice","volume":"59 1","pages":"33-47"},"PeriodicalIF":1.2,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88943004","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}
Indonesian Rupiah (IDR) banknotes have unique characteristics that distinguish them from one another, both in the form of numbers, zeros and background images. This pattern of each type of banknote will be modeled in order to test the nominal value and authenticity of IDR, so as to be able to distinguish not only IDR banknotes but also other denominations. Evolutionary Neural Network is the development of the concept of evolution to get a neural network (NN) using genetic algorithms (GA). In this paper the application of evolutionary neural networks with less input is able to have a better success rate in object recognition, because the parameters for producing neural networks are far better
{"title":"Model Classification Of Nominal Value And The Original Of IDR Money By Applying Evolutionary Neural Network","authors":"A. Al-Khowarizmi","doi":"10.31289/jite.v3i2.3284","DOIUrl":"https://doi.org/10.31289/jite.v3i2.3284","url":null,"abstract":"Indonesian Rupiah (IDR) banknotes have unique characteristics that distinguish them from one another, both in the form of numbers, zeros and background images. This pattern of each type of banknote will be modeled in order to test the nominal value and authenticity of IDR, so as to be able to distinguish not only IDR banknotes but also other denominations. Evolutionary Neural Network is the development of the concept of evolution to get a neural network (NN) using genetic algorithms (GA). In this paper the application of evolutionary neural networks with less input is able to have a better success rate in object recognition, because the parameters for producing neural networks are far better","PeriodicalId":43632,"journal":{"name":"Journal of Information Technology Education-Innovations in Practice","volume":"10 1","pages":"258-265"},"PeriodicalIF":1.2,"publicationDate":"2020-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88806652","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}
Business is an interpersonal and organizational activity that involves the process of selling, purchasing both goods and services with the aim of making a profit. But to get a large profit, it takes many partners who have a high desire to move forward. Information technology provides services for business people so that media information is available as a sign of obstacles. In addition it is necessary to do modeling where the process of communication between businesses running on information technology has a different profit from the business being run. Thus the union has the principle of kinship and has the principle of profitability divided by the amount of contribution given so that the creation of a model in electronic business (e-business) in the hope of having a family principle that is able to provide special profits for businesses other than the profits that run on certain businesses.
{"title":"The e-Business Community Model is Used to Improve Communication Between Businesses by Utilizing Union Principles","authors":"Fauzi Fauzi, A. Al-Khowarizmi, Muhathir Muhathir","doi":"10.31289/jite.v3i2.3260","DOIUrl":"https://doi.org/10.31289/jite.v3i2.3260","url":null,"abstract":"Business is an interpersonal and organizational activity that involves the process of selling, purchasing both goods and services with the aim of making a profit. But to get a large profit, it takes many partners who have a high desire to move forward. Information technology provides services for business people so that media information is available as a sign of obstacles. In addition it is necessary to do modeling where the process of communication between businesses running on information technology has a different profit from the business being run. Thus the union has the principle of kinship and has the principle of profitability divided by the amount of contribution given so that the creation of a model in electronic business (e-business) in the hope of having a family principle that is able to provide special profits for businesses other than the profits that run on certain businesses.","PeriodicalId":43632,"journal":{"name":"Journal of Information Technology Education-Innovations in Practice","volume":"22 1","pages":"252-257"},"PeriodicalIF":1.2,"publicationDate":"2020-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86424654","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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