Plagiarism was an act that made a disadvantage for the author because of the use of other people’s work or ideas without mentioning any credit. Especially for image plagiarism cause disadvantage for the author’s income because in this internet era many image authors sold their work for income. The previous research proposed the model that combined DCT hash and Blockchain, but this research didn’t conduct the plagiarism area of the image, so we proposed our model that combined DCT hash and Blockchain to prevent and detect plagiarism images. Our contribution for this research is to prevent plagiarism through the initial authentication process and then to detect image plagiarism on pixel-by-pixel of the image for more accurate plagiarism detection. Blockchain technology also prevented any change to data that was already stored in the Blockchain network, so it can prevent any change to image data and can prevent plagiarism attempts. With this proposed model, we got 100% accuracy for detecting images as plagiarism or not plagiarism. The results of testing the speed of the model on 100 different types of images show the speed of the model in displaying conclusions is 47.90 seconds. We also added some improvement areas for future research.
{"title":"Image Authentication Application with Blockchain to Prevent and Detect Image Plagiarism","authors":"Andi, Carles Juliandy, Robet Robet, Octara Pribadi, Robby Wijaya","doi":"10.1109/ICIC54025.2021.9632966","DOIUrl":"https://doi.org/10.1109/ICIC54025.2021.9632966","url":null,"abstract":"Plagiarism was an act that made a disadvantage for the author because of the use of other people’s work or ideas without mentioning any credit. Especially for image plagiarism cause disadvantage for the author’s income because in this internet era many image authors sold their work for income. The previous research proposed the model that combined DCT hash and Blockchain, but this research didn’t conduct the plagiarism area of the image, so we proposed our model that combined DCT hash and Blockchain to prevent and detect plagiarism images. Our contribution for this research is to prevent plagiarism through the initial authentication process and then to detect image plagiarism on pixel-by-pixel of the image for more accurate plagiarism detection. Blockchain technology also prevented any change to data that was already stored in the Blockchain network, so it can prevent any change to image data and can prevent plagiarism attempts. With this proposed model, we got 100% accuracy for detecting images as plagiarism or not plagiarism. The results of testing the speed of the model on 100 different types of images show the speed of the model in displaying conclusions is 47.90 seconds. We also added some improvement areas for future research.","PeriodicalId":189541,"journal":{"name":"2021 Sixth International Conference on Informatics and Computing (ICIC)","volume":"95 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":"124822414","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 COVID-19 pandemic causes transitions and social changes in the learning process from offline to online. On the other hand, the adaptation of formal education to digital learning is not always smooth. In this case, startups in the education sector have a role in advancing education and improving the quality of students in Indonesia, especially high school students. The purpose of this research is to analyze the role of educational startups in Indonesia in improving the quality of high school students during the pandemic. This research uses a mix method approach that combines quantitative and qualitative approaches, where data is obtained through distributions of questionnaires to 112 high school students, interview and library research. The data that has been analyzed quantitatively will then be strengthened with qualitative analysis by providing a description and interpretation of the statistical data. The results of this research reveal that quality of learning improved through the use of EdTech is most beneficial and more directed at improving the learning quality in carrying out daily tasks from teachers, both schoolwork and homework assignments, while the influence of Edtech platforms considerably extends to the extent of helping the online teaching and learning process while also helping students to prepare exam and enter university, improving the quality of learning and understanding in SBMPTN test as well as student achievement, and assisting in understanding USBN questions.
{"title":"The Role of Indonesian Education-based Startup in Enhancing the Learning Quality of High School Students in COVID-19 Pandemic Era","authors":"Akmal Silva Pratama, Eidelina Maghfirah, Faiz Ramadhan, Raudhatul Zannah As, J. Jamilah","doi":"10.1109/ICIC54025.2021.9632949","DOIUrl":"https://doi.org/10.1109/ICIC54025.2021.9632949","url":null,"abstract":"The COVID-19 pandemic causes transitions and social changes in the learning process from offline to online. On the other hand, the adaptation of formal education to digital learning is not always smooth. In this case, startups in the education sector have a role in advancing education and improving the quality of students in Indonesia, especially high school students. The purpose of this research is to analyze the role of educational startups in Indonesia in improving the quality of high school students during the pandemic. This research uses a mix method approach that combines quantitative and qualitative approaches, where data is obtained through distributions of questionnaires to 112 high school students, interview and library research. The data that has been analyzed quantitatively will then be strengthened with qualitative analysis by providing a description and interpretation of the statistical data. The results of this research reveal that quality of learning improved through the use of EdTech is most beneficial and more directed at improving the learning quality in carrying out daily tasks from teachers, both schoolwork and homework assignments, while the influence of Edtech platforms considerably extends to the extent of helping the online teaching and learning process while also helping students to prepare exam and enter university, improving the quality of learning and understanding in SBMPTN test as well as student achievement, and assisting in understanding USBN questions.","PeriodicalId":189541,"journal":{"name":"2021 Sixth International Conference on Informatics and Computing (ICIC)","volume":"180 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":"123007418","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/ICIC54025.2021.9632937
Farrel Athaillah Putra, Dwi Anggun Cahyati Jamil, Briliantino Abhista Prabandanu, Suhaili Faruq, Firsta Adi Pradana, Riqqah Fadiyah Alya, H. Santoso, Farrikh Al Zami, Filmada Ocky Saputra
Batik is one of Indonesia's cultural heritages that UNESCO has recognized as an Intangible Cultural Heritage, so we should be proud and preserve it. However, there are problems in the batik industry related to the labelling of traditional and modern batik products. The prevalence of fraud in printed batik, which is given a price equivalent to written batik, which is much more expensive, and public ignorance of the aesthetic value and authenticity of written batik, can disrupt the traditional batik industry in Indonesia. Based on these problems, the authors innovate to develop a machine learning model that aims to classify the authenticity of batik using the Convolutional Neural Network Algorithm with Transfer Learning Method. The classification process consists of several stages: collecting datasets, preprocessing data, developing CNN models with transfer learning, and compiling and training models. The development of the machine learning model that has been trained produces an accuracy of 96.91%. The author hopes that this research can make it easier for people to distinguish between written and printed batik, minimize the existence of batik price fraud, and increase consumer confidence in batik transactions by ensuring the originality of batik products.
{"title":"Classification of Batik Authenticity Using Convolutional Neural Network Algorithm with Transfer Learning Method","authors":"Farrel Athaillah Putra, Dwi Anggun Cahyati Jamil, Briliantino Abhista Prabandanu, Suhaili Faruq, Firsta Adi Pradana, Riqqah Fadiyah Alya, H. Santoso, Farrikh Al Zami, Filmada Ocky Saputra","doi":"10.1109/ICIC54025.2021.9632937","DOIUrl":"https://doi.org/10.1109/ICIC54025.2021.9632937","url":null,"abstract":"Batik is one of Indonesia's cultural heritages that UNESCO has recognized as an Intangible Cultural Heritage, so we should be proud and preserve it. However, there are problems in the batik industry related to the labelling of traditional and modern batik products. The prevalence of fraud in printed batik, which is given a price equivalent to written batik, which is much more expensive, and public ignorance of the aesthetic value and authenticity of written batik, can disrupt the traditional batik industry in Indonesia. Based on these problems, the authors innovate to develop a machine learning model that aims to classify the authenticity of batik using the Convolutional Neural Network Algorithm with Transfer Learning Method. The classification process consists of several stages: collecting datasets, preprocessing data, developing CNN models with transfer learning, and compiling and training models. The development of the machine learning model that has been trained produces an accuracy of 96.91%. The author hopes that this research can make it easier for people to distinguish between written and printed batik, minimize the existence of batik price fraud, and increase consumer confidence in batik transactions by ensuring the originality of batik products.","PeriodicalId":189541,"journal":{"name":"2021 Sixth International Conference on Informatics and Computing (ICIC)","volume":"1 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":"129044390","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/ICIC54025.2021.9632941
Arif Wicaksono Septyanto, I. Rosyida, S. Suryono
A traffic light control system is important to reduce traffic jams. Several methods have been proposed to control traffic lights. However, most of them are inaccurate because do not use data on traffic density status. This study proposes an automatic traffic light control system by instilling artificial intelligence and Radio Frequency Identification (RFID) technology which is used to determine the best duration of traffic lights on an intersection. RFID is used to calculate the average speed of vehicles and the percentage of road occupancy in each lane. The average speed value and the percentage of road occupancy are used as inputs for the fuzzy rule-based algorithm. The outputs of the fuzzy rule-based are the status of traffic jams, road occupancy rate on each lane, the average speed of vehicles on each lane, and real time duration of traffic lights. The fuzzy computing process is carried out locally on the fog server via a Wi-Fi gateway to reduce cloud load. We evaluate the rule-based algorithm on an intersection with 4 lanes. The results show that the average speed of lane 1 is middle 0.922, lane 2 middle 0.699, lane 3 middle 0.599 and lane 4 middle 0.621. for fuzzification value of road density obtained lane 1 high 0.409, lane 2 low 0.475, lane 3 mid 0.951 and lane 4 mid 0.858. The conditions of traffic jams using the rule-based are as follows: "Heavy-Clock" for lane 1, "Light" for lane 2, "Light-Heavy" for line 3, and "Light-Heavy" for line 4. The system built-in using RFID technology can calculate average speeds and road occupancy rates accurately.
{"title":"A Fuzzy Rule-Based Fog-Cloud for Control the Traffic Light Duration Based On-road Density","authors":"Arif Wicaksono Septyanto, I. Rosyida, S. Suryono","doi":"10.1109/ICIC54025.2021.9632941","DOIUrl":"https://doi.org/10.1109/ICIC54025.2021.9632941","url":null,"abstract":"A traffic light control system is important to reduce traffic jams. Several methods have been proposed to control traffic lights. However, most of them are inaccurate because do not use data on traffic density status. This study proposes an automatic traffic light control system by instilling artificial intelligence and Radio Frequency Identification (RFID) technology which is used to determine the best duration of traffic lights on an intersection. RFID is used to calculate the average speed of vehicles and the percentage of road occupancy in each lane. The average speed value and the percentage of road occupancy are used as inputs for the fuzzy rule-based algorithm. The outputs of the fuzzy rule-based are the status of traffic jams, road occupancy rate on each lane, the average speed of vehicles on each lane, and real time duration of traffic lights. The fuzzy computing process is carried out locally on the fog server via a Wi-Fi gateway to reduce cloud load. We evaluate the rule-based algorithm on an intersection with 4 lanes. The results show that the average speed of lane 1 is middle 0.922, lane 2 middle 0.699, lane 3 middle 0.599 and lane 4 middle 0.621. for fuzzification value of road density obtained lane 1 high 0.409, lane 2 low 0.475, lane 3 mid 0.951 and lane 4 mid 0.858. The conditions of traffic jams using the rule-based are as follows: \"Heavy-Clock\" for lane 1, \"Light\" for lane 2, \"Light-Heavy\" for line 3, and \"Light-Heavy\" for line 4. The system built-in using RFID technology can calculate average speeds and road occupancy rates accurately.","PeriodicalId":189541,"journal":{"name":"2021 Sixth International Conference on Informatics and Computing (ICIC)","volume":"28 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":"130380414","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/ICIC54025.2021.9632998
Doni Setyawan, Retantyo Wardoyo, Moh Edi Wibowo, E. H. Herdiana Murhandarwati, J. Jamilah
The Convolutional Neural Networks (CNNs) have been used to classify malaria parasites from blood smear images automatically and successfully gave a good result, thus enabling fast diagnoses and saving the patient. This study presents a review of the existing CNN techniques used for malaria diagnosis, focusing on the architectures, data preparation, preprocessing, and classification. Furthermore, this study discusses why the comparability of the presented methods becomes difficult and which challenges must be overcome in the future. First, we review the current CNN approaches used for malaria classification from existing research articles. Next, the performance and properties of proposed CNN approaches are summarized and discussed. The use of CNN as a feature extractor shows better performance than transfer learning and learning from scratch approaches. Unfortunately, some research uses private datasets for training and testing the proposed model. Thus it is not easy to compare with the other methods. The use of CNN in malaria diagnosis is also still limited to binary classification, namely the normal and malaria-infected erythrocyte class. Future research should use available benchmark public datasets to allow the proposed CNN method comparability and proposed a CNN model for multi-class classification such as species and life stages of malaria-causing plasmodium.
{"title":"Malaria Classification Using Convolutional Neural Network: A Review","authors":"Doni Setyawan, Retantyo Wardoyo, Moh Edi Wibowo, E. H. Herdiana Murhandarwati, J. Jamilah","doi":"10.1109/ICIC54025.2021.9632998","DOIUrl":"https://doi.org/10.1109/ICIC54025.2021.9632998","url":null,"abstract":"The Convolutional Neural Networks (CNNs) have been used to classify malaria parasites from blood smear images automatically and successfully gave a good result, thus enabling fast diagnoses and saving the patient. This study presents a review of the existing CNN techniques used for malaria diagnosis, focusing on the architectures, data preparation, preprocessing, and classification. Furthermore, this study discusses why the comparability of the presented methods becomes difficult and which challenges must be overcome in the future. First, we review the current CNN approaches used for malaria classification from existing research articles. Next, the performance and properties of proposed CNN approaches are summarized and discussed. The use of CNN as a feature extractor shows better performance than transfer learning and learning from scratch approaches. Unfortunately, some research uses private datasets for training and testing the proposed model. Thus it is not easy to compare with the other methods. The use of CNN in malaria diagnosis is also still limited to binary classification, namely the normal and malaria-infected erythrocyte class. Future research should use available benchmark public datasets to allow the proposed CNN method comparability and proposed a CNN model for multi-class classification such as species and life stages of malaria-causing plasmodium.","PeriodicalId":189541,"journal":{"name":"2021 Sixth International Conference on Informatics and Computing (ICIC)","volume":"61 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":"125019958","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/ICIC54025.2021.9632927
Gladys Indri Putri, Nuryadin Nuryadin, R. E. Indrajit, Erick Dazki, Handri Santoso
This study aims to analyze teacher and student responses to learning using the Learning Management System (LMS) during the COVID-19 pandemic. The respondents in this study were 100 teachers and students in Cilacap Regency, Central Java, Indonesia. 90% of respondents choose Google Classroom as the LMS they use. The information collection method used in this study was a survey with a questionnaire. The data obtained were then analyzed descriptively qualitatively by considering the aspects of the software used, content aspects and display aspects. The results of this study show that LMS helps online learning well during the COVID-19 Pandemic
{"title":"Analysis of Teacher and Student Responses to the Use of a Web-based Learning Management System (LMS) during COVID-19 Pandemic","authors":"Gladys Indri Putri, Nuryadin Nuryadin, R. E. Indrajit, Erick Dazki, Handri Santoso","doi":"10.1109/ICIC54025.2021.9632927","DOIUrl":"https://doi.org/10.1109/ICIC54025.2021.9632927","url":null,"abstract":"This study aims to analyze teacher and student responses to learning using the Learning Management System (LMS) during the COVID-19 pandemic. The respondents in this study were 100 teachers and students in Cilacap Regency, Central Java, Indonesia. 90% of respondents choose Google Classroom as the LMS they use. The information collection method used in this study was a survey with a questionnaire. The data obtained were then analyzed descriptively qualitatively by considering the aspects of the software used, content aspects and display aspects. The results of this study show that LMS helps online learning well during the COVID-19 Pandemic","PeriodicalId":189541,"journal":{"name":"2021 Sixth International Conference on Informatics and Computing (ICIC)","volume":"75 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":"127346125","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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