Pub Date : 2020-11-03DOI: 10.1109/ICIC50835.2020.9288589
Harivanto, S. Sudiro, T. M. Kusuma, S. Madenda, L. M. R. Rere
Research on fingerprints has been done a lot, this is because of so many uses of fingerprints as an access tool to enter a system. This method is used to ensure the authenticity of authorized users. Fingerprints are used as biometric identification because fingerprints have a unique pattern that is different from every human fingerprint. The many uses of fingerprint biometric systems also cause many threats to the system, fingerprint forgery occurs so that it can be used to access the system illegally. Therefore this study proposes a system to be able to recognize the authenticity of a fingerprint. CNN is generally designed for object recognition of an image, making it suitable for recognizing fingerprint images to determine if a fingerprint is genuine or fake. The results of the evaluation of several experiments conducted obtained the highest accuracy value of 95.32% for determining the authenticity of fingerprints.
{"title":"Detection of Fingerprint Authenticity Based on Deep Learning Using Image Pixel Value","authors":"Harivanto, S. Sudiro, T. M. Kusuma, S. Madenda, L. M. R. Rere","doi":"10.1109/ICIC50835.2020.9288589","DOIUrl":"https://doi.org/10.1109/ICIC50835.2020.9288589","url":null,"abstract":"Research on fingerprints has been done a lot, this is because of so many uses of fingerprints as an access tool to enter a system. This method is used to ensure the authenticity of authorized users. Fingerprints are used as biometric identification because fingerprints have a unique pattern that is different from every human fingerprint. The many uses of fingerprint biometric systems also cause many threats to the system, fingerprint forgery occurs so that it can be used to access the system illegally. Therefore this study proposes a system to be able to recognize the authenticity of a fingerprint. CNN is generally designed for object recognition of an image, making it suitable for recognizing fingerprint images to determine if a fingerprint is genuine or fake. The results of the evaluation of several experiments conducted obtained the highest accuracy value of 95.32% for determining the authenticity of fingerprints.","PeriodicalId":413610,"journal":{"name":"2020 Fifth International Conference on Informatics and Computing (ICIC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128472084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-03DOI: 10.1109/ICIC50835.2020.9288540
Tuti Purwoningsih, H. Santoso, Z. Hasibuan
The use of a Learning Management System (LMS) in e-learning makes it easier for teachers to track and record student learning behavior. The right analytics of e-learning students can help teachers understand the student context and what learning experiences are most suitable for e-learning students to improve learning outcomes. However, e-learning teachers often experience difficulties in analyzing student data due to a large number of students who must be analyzed and limited data. To support research in this area, we conducted a descriptive analysis of a dataset containing student data from the Open and Distance Learning (ODL) that organizes e-learning. The dataset contains data on student demographic profiles and student activity or behavior during e-learning which is recorded in the LMS system at the Open University of Indonesia. In this initial study, the dataset contained information from 120 classes in 18 subjects with 4,741 students from 33 study programs with many logs on LMS 1,641,234 entries. This article presents an analytical description of the characteristics of students participating in e-learning using Exploratory data analytics (EDA) and machine learning approaches as the basis for predictive and prescriptive analytics of student learning outcomes based on a combination of demographic profile data and learning behavior. This study helps education practitioners in the first step of analytics data as the basis for developing e-Learning instructional designs that support the success of fully online students.
{"title":"Data Analytics of Students' Profiles and Activities in a Full Online Learning Context","authors":"Tuti Purwoningsih, H. Santoso, Z. Hasibuan","doi":"10.1109/ICIC50835.2020.9288540","DOIUrl":"https://doi.org/10.1109/ICIC50835.2020.9288540","url":null,"abstract":"The use of a Learning Management System (LMS) in e-learning makes it easier for teachers to track and record student learning behavior. The right analytics of e-learning students can help teachers understand the student context and what learning experiences are most suitable for e-learning students to improve learning outcomes. However, e-learning teachers often experience difficulties in analyzing student data due to a large number of students who must be analyzed and limited data. To support research in this area, we conducted a descriptive analysis of a dataset containing student data from the Open and Distance Learning (ODL) that organizes e-learning. The dataset contains data on student demographic profiles and student activity or behavior during e-learning which is recorded in the LMS system at the Open University of Indonesia. In this initial study, the dataset contained information from 120 classes in 18 subjects with 4,741 students from 33 study programs with many logs on LMS 1,641,234 entries. This article presents an analytical description of the characteristics of students participating in e-learning using Exploratory data analytics (EDA) and machine learning approaches as the basis for predictive and prescriptive analytics of student learning outcomes based on a combination of demographic profile data and learning behavior. This study helps education practitioners in the first step of analytics data as the basis for developing e-Learning instructional designs that support the success of fully online students.","PeriodicalId":413610,"journal":{"name":"2020 Fifth International Conference on Informatics and Computing (ICIC)","volume":"58 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127239618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-03DOI: 10.1109/ICIC50835.2020.9288579
M. Alkaff, Andreyan Rizky Baskara, Yohanes Hendro Wicaksono
YouTube is one of the most effective social media sites for promoting products, one of which is movies. The film industry usually publishes video trailers on YouTube to promote their upcoming film. The comments that appear on YouTube could help movie producers to estimate how the public will react to their movie once it is released. In this study, we conducted a sentiment analysis on the comments of Indonesian movie trailers on YouTube. We split movie comments into four popular movie genres: action, romance, comedy, and horror. Then, we use the Delta TF-IDF word weighting method and combine it with several classification methods to compare the model performance. Finally, we evaluated the model using Stratified K-Fold cross-validation with K = 10. Results showed that Logistic Regression and Naïve Bayes are better when classifying sentiment for a specific genre. Simultaneously, the SVM model gives good performance on sentiment analysis for a more general genre.
{"title":"Sentiment Analysis of Indonesian Movie Trailer on YouTube Using Delta TF-IDF and SVM","authors":"M. Alkaff, Andreyan Rizky Baskara, Yohanes Hendro Wicaksono","doi":"10.1109/ICIC50835.2020.9288579","DOIUrl":"https://doi.org/10.1109/ICIC50835.2020.9288579","url":null,"abstract":"YouTube is one of the most effective social media sites for promoting products, one of which is movies. The film industry usually publishes video trailers on YouTube to promote their upcoming film. The comments that appear on YouTube could help movie producers to estimate how the public will react to their movie once it is released. In this study, we conducted a sentiment analysis on the comments of Indonesian movie trailers on YouTube. We split movie comments into four popular movie genres: action, romance, comedy, and horror. Then, we use the Delta TF-IDF word weighting method and combine it with several classification methods to compare the model performance. Finally, we evaluated the model using Stratified K-Fold cross-validation with K = 10. Results showed that Logistic Regression and Naïve Bayes are better when classifying sentiment for a specific genre. Simultaneously, the SVM model gives good performance on sentiment analysis for a more general genre.","PeriodicalId":413610,"journal":{"name":"2020 Fifth International Conference on Informatics and Computing (ICIC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131405045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-03DOI: 10.1109/ICIC50835.2020.9288588
Dhammajoti, J. Young, A. Rusli
User feedback is one of the most important sources of information for improving the quality of software products. Our current research focuses on a software product that is often used in many universities, the E- Learning system. To reduce the effort of manually reading all submitted user feedback, building an automatic text classification using various machine learning approaches is a popular solution. However, there is often a challenge of imbalanced data that could jeopardize the ability of the machine to find the pattern and classify feedback correctly. Several techniques ranging from random resampling of data to artificially creating more data (e.g. SMOTE) have already been proposed for handling imbalanced data and show promising results in terms of performance. This paper aims to implement several numerical representations and implementing resampling techniques (to handling imbalanced data), which then are followed by evaluating some popular supervised machine learning classification algorithms, which are the Logistic Regression, Random Forest, Support Vector Machine, Naive Bayes, and Decision Tree. Finally, evaluating performance with and without using resampling techniques by macro-average F1 Scores. The results show generally the implementation of oversampling techniques leads to better performance, except in a few cases where under-sampling techniques perform better.
{"title":"A Comparison of Supervised Text Classification and Resampling Techniques for User Feedback in Bahasa Indonesia","authors":"Dhammajoti, J. Young, A. Rusli","doi":"10.1109/ICIC50835.2020.9288588","DOIUrl":"https://doi.org/10.1109/ICIC50835.2020.9288588","url":null,"abstract":"User feedback is one of the most important sources of information for improving the quality of software products. Our current research focuses on a software product that is often used in many universities, the E- Learning system. To reduce the effort of manually reading all submitted user feedback, building an automatic text classification using various machine learning approaches is a popular solution. However, there is often a challenge of imbalanced data that could jeopardize the ability of the machine to find the pattern and classify feedback correctly. Several techniques ranging from random resampling of data to artificially creating more data (e.g. SMOTE) have already been proposed for handling imbalanced data and show promising results in terms of performance. This paper aims to implement several numerical representations and implementing resampling techniques (to handling imbalanced data), which then are followed by evaluating some popular supervised machine learning classification algorithms, which are the Logistic Regression, Random Forest, Support Vector Machine, Naive Bayes, and Decision Tree. Finally, evaluating performance with and without using resampling techniques by macro-average F1 Scores. The results show generally the implementation of oversampling techniques leads to better performance, except in a few cases where under-sampling techniques perform better.","PeriodicalId":413610,"journal":{"name":"2020 Fifth International Conference on Informatics and Computing (ICIC)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133456070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-03DOI: 10.1109/ICIC50835.2020.9288650
H. N. Zahra, Muhammad Okky Ibrohim, Junaedi Fahmi, Rike Adelia, Fandy Akhmad Nur Febryanto, Oskar Riandi
These days, human-computer interactions develop in an alarmingly fast rate. To keep up with this development, one of many things to be advanced is machine's capability of recognizing human emotions through speech, or simply put, Speech Emotion Recognition (SER). Various studies regarding SER have been carried out using varying data modalities, such as TV shows, movies, and actor voice recordings. While the result may be proven satisfying, to collect these data of TV and actor recordings can be quite difficult and may require some costs. On the other hand, YouTube is an open and free platform for data gathering, and retrieving data from YouTube is effortless as well. Despite that, almost none of SER studies have tried this method of data collecting. This paper presents SER in Indonesian language, using Indonesian YouTube Web Series dataset with 4 labels of emotions. In the beginning, several experiments were carried out to determine which deep learning approach trained with which specific combination of features would yield out the most favorable result. The initial stage of the experiments showed that the Convolutional Neural Network (CNN) using a feature combination of MFCC, Contrast, and Tonnetz, gives better performance than other deep learning approach that we use. After tuning parameter process, we obtain that CNN with the combination of MFCC, Contrast, and Tonnetz gives 62.30% of F1 - Score.
如今，人机交互正以惊人的速度发展。为了跟上这一发展，机器通过语音识别人类情感的能力，或者简单地说，语音情感识别(SER)是许多需要改进的东西之一。关于SER的各种研究使用了不同的数据模式，例如电视节目、电影和演员的录音。虽然结果可能令人满意，但收集电视和演员录音的这些数据可能相当困难，可能需要一些成本。另一方面，YouTube是一个开放和免费的数据收集平台，从YouTube上检索数据也毫不费力。尽管如此，几乎没有SER研究尝试过这种数据收集方法。本文使用带有4个情感标签的印度尼西亚YouTube Web Series数据集来呈现印度尼西亚语的SER。一开始，我们进行了几个实验，以确定哪种深度学习方法使用哪种特定的特征组合进行训练会产生最有利的结果。实验的初始阶段表明，使用MFCC、Contrast和Tonnetz的特征组合的卷积神经网络(CNN)比我们使用的其他深度学习方法提供了更好的性能。经过参数调整处理，我们得到MFCC、Contrast和Tonnetz组合的CNN给出了62.30%的F1 - Score。
{"title":"Speech Emotion Recognition on Indonesian YouTube Web Series Using Deep Learning Approach","authors":"H. N. Zahra, Muhammad Okky Ibrohim, Junaedi Fahmi, Rike Adelia, Fandy Akhmad Nur Febryanto, Oskar Riandi","doi":"10.1109/ICIC50835.2020.9288650","DOIUrl":"https://doi.org/10.1109/ICIC50835.2020.9288650","url":null,"abstract":"These days, human-computer interactions develop in an alarmingly fast rate. To keep up with this development, one of many things to be advanced is machine's capability of recognizing human emotions through speech, or simply put, Speech Emotion Recognition (SER). Various studies regarding SER have been carried out using varying data modalities, such as TV shows, movies, and actor voice recordings. While the result may be proven satisfying, to collect these data of TV and actor recordings can be quite difficult and may require some costs. On the other hand, YouTube is an open and free platform for data gathering, and retrieving data from YouTube is effortless as well. Despite that, almost none of SER studies have tried this method of data collecting. This paper presents SER in Indonesian language, using Indonesian YouTube Web Series dataset with 4 labels of emotions. In the beginning, several experiments were carried out to determine which deep learning approach trained with which specific combination of features would yield out the most favorable result. The initial stage of the experiments showed that the Convolutional Neural Network (CNN) using a feature combination of MFCC, Contrast, and Tonnetz, gives better performance than other deep learning approach that we use. After tuning parameter process, we obtain that CNN with the combination of MFCC, Contrast, and Tonnetz gives 62.30% of F1 - Score.","PeriodicalId":413610,"journal":{"name":"2020 Fifth International Conference on Informatics and Computing (ICIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130948904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-03DOI: 10.1109/ICIC50835.2020.9288652
Giandari Maulani, U. Rahardja, Marviola Hardini, Ria Dwi I’zzaty, Q. Aini, N. Santoso
The Digital Based Community Economy Program has been created to expand market access and improve the quality of farmers. The program is called the “Online Farming Program”, in collaboration with the Ministry of Communication and Information Technology, as well as several digital business startups who have created mobile-based applications that aim to increase productivity in agriculture and advance farmers. Standard of living. However, the socialization of the Go-Online Farmers program, which is still not optimal for farmers who are not familiar with computer programs and are not familiar with technology is one of the obstacles they face. So in the era of the industrial revolution 4.0, the Participatory Rural Appraisal (PRA) and Media Production Concept (MPC) methods were used so that the output generated from this study could educate and add to the information obtained by farmers and help farmers buy and sell online so they could use the Go-Online program maximally. It can be concluded that from this research a strategy is needed to disseminate the Go-Online Farmer Program to farmers so that researchers implement a video motion graphic to facilitate this research which is uploaded to the YouTube channel so that information can be disseminated, readily accepted, and distributed to 88.813 villages in Indonesia.
{"title":"Educating Farmers Using Participatory Rural Appraisal Construct","authors":"Giandari Maulani, U. Rahardja, Marviola Hardini, Ria Dwi I’zzaty, Q. Aini, N. Santoso","doi":"10.1109/ICIC50835.2020.9288652","DOIUrl":"https://doi.org/10.1109/ICIC50835.2020.9288652","url":null,"abstract":"The Digital Based Community Economy Program has been created to expand market access and improve the quality of farmers. The program is called the “Online Farming Program”, in collaboration with the Ministry of Communication and Information Technology, as well as several digital business startups who have created mobile-based applications that aim to increase productivity in agriculture and advance farmers. Standard of living. However, the socialization of the Go-Online Farmers program, which is still not optimal for farmers who are not familiar with computer programs and are not familiar with technology is one of the obstacles they face. So in the era of the industrial revolution 4.0, the Participatory Rural Appraisal (PRA) and Media Production Concept (MPC) methods were used so that the output generated from this study could educate and add to the information obtained by farmers and help farmers buy and sell online so they could use the Go-Online program maximally. It can be concluded that from this research a strategy is needed to disseminate the Go-Online Farmer Program to farmers so that researchers implement a video motion graphic to facilitate this research which is uploaded to the YouTube channel so that information can be disseminated, readily accepted, and distributed to 88.813 villages in Indonesia.","PeriodicalId":413610,"journal":{"name":"2020 Fifth International Conference on Informatics and Computing (ICIC)","volume":"491 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133841152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-03DOI: 10.1109/ICIC50835.2020.9288549
Nina Kurnia Hikmawati, Doni Purnama Alamsyah, Ahmad Setiadi
The implementation of information technology is essential for companies, especially supermarkets, as marketing strategy support. In this case, supermarkets usually use information technology in customer relationship management. Based on the phenomenon, a study of the implementation of customer relationship management is carried out concerning customer satisfaction and loyalty. The study was conducted on supermarket consumers in Bandung, using a quantitative questionnaire. Three hundred forty-one respondents were taken randomly within a certain period, and the data from the respondents were processed through linear regression analysis techniques with hypothesis testing. The results of the study show that customer relationship management has a good relationship with customer satisfaction and loyalty. Furthermore, increase customer loyalty, considering that in the implementation of customer relationship management, many factors allow the best service to consumers. Furthermore, in implementing customer relationship management, support from information technology is needed, considering that fair data processing through IT supports the process of service to consumers.
{"title":"IT Implementation of Customer Relationship Management","authors":"Nina Kurnia Hikmawati, Doni Purnama Alamsyah, Ahmad Setiadi","doi":"10.1109/ICIC50835.2020.9288549","DOIUrl":"https://doi.org/10.1109/ICIC50835.2020.9288549","url":null,"abstract":"The implementation of information technology is essential for companies, especially supermarkets, as marketing strategy support. In this case, supermarkets usually use information technology in customer relationship management. Based on the phenomenon, a study of the implementation of customer relationship management is carried out concerning customer satisfaction and loyalty. The study was conducted on supermarket consumers in Bandung, using a quantitative questionnaire. Three hundred forty-one respondents were taken randomly within a certain period, and the data from the respondents were processed through linear regression analysis techniques with hypothesis testing. The results of the study show that customer relationship management has a good relationship with customer satisfaction and loyalty. Furthermore, increase customer loyalty, considering that in the implementation of customer relationship management, many factors allow the best service to consumers. Furthermore, in implementing customer relationship management, support from information technology is needed, considering that fair data processing through IT supports the process of service to consumers.","PeriodicalId":413610,"journal":{"name":"2020 Fifth International Conference on Informatics and Computing (ICIC)","volume":"214 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122232885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-03DOI: 10.1109/ICIC50835.2020.9288614
Kevin Sutarno, Brian Estadimas, Amira Taliya, Damar Wardoyo, Ika Chandra Hapsari, A. Hidayanto, B. Nazief
The development of IT encourages the increased use of social media. A lot of internet users uses social media since the benefit they get from it, such as building, developing, and maintaining social relationships through sharing personal information. Despite the success of social media, privacy concerns have risen over the past few years. This study explores factors that encourage and hinders social media users from disclosing their personal information on social media, based on three frameworks, which are UTAUT2, Social Cognitive Theory, and Privacy Calculus. There are 155 respondents for this research. This research uses SmartPLS software for processing the data. As a result, the factors that influence people's intention to disclose personal data, including benefits of information disclosure, information control, perceived risks, and user self-presentation behavior.
{"title":"Factors Influencing User Intention in Opening Personal Data on Social Media","authors":"Kevin Sutarno, Brian Estadimas, Amira Taliya, Damar Wardoyo, Ika Chandra Hapsari, A. Hidayanto, B. Nazief","doi":"10.1109/ICIC50835.2020.9288614","DOIUrl":"https://doi.org/10.1109/ICIC50835.2020.9288614","url":null,"abstract":"The development of IT encourages the increased use of social media. A lot of internet users uses social media since the benefit they get from it, such as building, developing, and maintaining social relationships through sharing personal information. Despite the success of social media, privacy concerns have risen over the past few years. This study explores factors that encourage and hinders social media users from disclosing their personal information on social media, based on three frameworks, which are UTAUT2, Social Cognitive Theory, and Privacy Calculus. There are 155 respondents for this research. This research uses SmartPLS software for processing the data. As a result, the factors that influence people's intention to disclose personal data, including benefits of information disclosure, information control, perceived risks, and user self-presentation behavior.","PeriodicalId":413610,"journal":{"name":"2020 Fifth International Conference on Informatics and Computing (ICIC)","volume":"840 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120930347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-03DOI: 10.1109/ICIC50835.2020.9288581
Prayudi Utomo, Falahah
Dew computing is one of the distributed computing paradigms which is considered as an extension of the cloud computing paradigm. In dew computing, users can perform full system functionality without depending on internet availability. All data will be stored on the local storage of the user's device, and when an internet connection is available, synchronization will be carried out to synchronize the information on cloud-based applications. There have been many implementations of dew computing in existing applications, but research done in the field of dew computing is not as much as in other distributed computing fields. This study intends to discuss the dew computing concept and its implementation, what constraints might exist on implementation, and what strategies need to be considered in designing dew computing implementations. The results is a proposed framework for consideration on determining the specifications of applications running on dew computing, both for desktop, mobile and cloud environments, which covered four aspects, which are: data storage, synchronization, authorization and collaboration.
{"title":"Dew Computing: Concept and Its Implementation Strategy","authors":"Prayudi Utomo, Falahah","doi":"10.1109/ICIC50835.2020.9288581","DOIUrl":"https://doi.org/10.1109/ICIC50835.2020.9288581","url":null,"abstract":"Dew computing is one of the distributed computing paradigms which is considered as an extension of the cloud computing paradigm. In dew computing, users can perform full system functionality without depending on internet availability. All data will be stored on the local storage of the user's device, and when an internet connection is available, synchronization will be carried out to synchronize the information on cloud-based applications. There have been many implementations of dew computing in existing applications, but research done in the field of dew computing is not as much as in other distributed computing fields. This study intends to discuss the dew computing concept and its implementation, what constraints might exist on implementation, and what strategies need to be considered in designing dew computing implementations. The results is a proposed framework for consideration on determining the specifications of applications running on dew computing, both for desktop, mobile and cloud environments, which covered four aspects, which are: data storage, synchronization, authorization and collaboration.","PeriodicalId":413610,"journal":{"name":"2020 Fifth International Conference on Informatics and Computing (ICIC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121272507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-03DOI: 10.1109/ICIC50835.2020.9288624
Hanny Hikmayanti Handayani, S. Madenda, Eri Prasetyo Wibowo, Tubagus Maulana Kusuma, S. Widiyanto, Anis Fitri Nur Masruriyah
Indonesian populace demands animal protein high enough to fulfill nutrition, one of the most sought-after sources of protein comes from beef. Due to high market needs, some traders are defrauding to get higher profits. This has caused discomfort for most beef consumers because people generally rely on the ability of vision to find out the quality of meat. In order to make it easier for the public to recognize the quality of meat to be consumed, this study classifies the quality of meat marbling based on the size of marbling. The classification in this study used the SVM algorithm, LDA, and Decision Tree. Furthermore, the result came out with the best algorithm in this case was the Decision Tree.
{"title":"The Best Classification Algorithm for Identification Beef Quality Based on Marbling","authors":"Hanny Hikmayanti Handayani, S. Madenda, Eri Prasetyo Wibowo, Tubagus Maulana Kusuma, S. Widiyanto, Anis Fitri Nur Masruriyah","doi":"10.1109/ICIC50835.2020.9288624","DOIUrl":"https://doi.org/10.1109/ICIC50835.2020.9288624","url":null,"abstract":"Indonesian populace demands animal protein high enough to fulfill nutrition, one of the most sought-after sources of protein comes from beef. Due to high market needs, some traders are defrauding to get higher profits. This has caused discomfort for most beef consumers because people generally rely on the ability of vision to find out the quality of meat. In order to make it easier for the public to recognize the quality of meat to be consumed, this study classifies the quality of meat marbling based on the size of marbling. The classification in this study used the SVM algorithm, LDA, and Decision Tree. Furthermore, the result came out with the best algorithm in this case was the Decision Tree.","PeriodicalId":413610,"journal":{"name":"2020 Fifth International Conference on Informatics and Computing (ICIC)","volume":"1989 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125497463","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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