Pub Date : 2020-09-15DOI: 10.1109/IC2IE50715.2020.9274582
Anggi Mardiyono, W. Sholihah, F. Hakim
Network monitoring system is a system that works to observe and monitor the situation on a running computer network. Network monitoring is very important to keep the network running well and to know the current condition of the network. Generally, network monitoring requires the administrator to stay and to keep an eye in front of the monitor screen to find out any problems that occur. The administrator or the operator also need network notification problem in real time. This makes the network supervision process very dependent on time and place. This research develops a system for helping administrator or network operator to monitor the network using mobile phone at any time and any place. This monitoring process in this paper using Zabbix. In monitoring the computer network is also needed a notification. The notification of the problem in the network will be sent using Telegram Messenger. This research was conducted in Indonesian Internet Service Provider Association (APJII). The method used in this research is Network Development Life Cycle (NDLC).
{"title":"Mobile-based Network Monitoring System Using Zabbix and Telegram","authors":"Anggi Mardiyono, W. Sholihah, F. Hakim","doi":"10.1109/IC2IE50715.2020.9274582","DOIUrl":"https://doi.org/10.1109/IC2IE50715.2020.9274582","url":null,"abstract":"Network monitoring system is a system that works to observe and monitor the situation on a running computer network. Network monitoring is very important to keep the network running well and to know the current condition of the network. Generally, network monitoring requires the administrator to stay and to keep an eye in front of the monitor screen to find out any problems that occur. The administrator or the operator also need network notification problem in real time. This makes the network supervision process very dependent on time and place. This research develops a system for helping administrator or network operator to monitor the network using mobile phone at any time and any place. This monitoring process in this paper using Zabbix. In monitoring the computer network is also needed a notification. The notification of the problem in the network will be sent using Telegram Messenger. This research was conducted in Indonesian Internet Service Provider Association (APJII). The method used in this research is Network Development Life Cycle (NDLC).","PeriodicalId":211983,"journal":{"name":"2020 3rd International Conference on Computer and Informatics Engineering (IC2IE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127093003","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 : 2020-09-15DOI: 10.1109/IC2IE50715.2020.9274681
Anita Safitri, E. C. Djamal, Fikri Nugraha
Brain-Computer Interface (BCI) is a device that can connect brain commands without the need for movement, gesture, or voice. Usually, BCI uses the Electroencephalogram (EEG) signal as an intermediate device. EEG signals need to be extracted into waves that represent the action in mind. In this study used Wavelet transformation to obtain the imagery motor component from the EEG signal. However, the problem also arises in the considerable channel redundancy in EEG signal recording. Therefore, it requires a signal reduction process. This paper proposed the problem using Independent Component Analysis (ICA). Then ICA components are features of Recurrent Neural Networks (RNN) to classify BCI information into four classes. The experimental results showed that using ICA improved accuracy by up to 99.06%, compared to Wavelet and RNN only, which is only 94.06%. We examined three optimization models, particularly Adam, AdaDelta, and AdaGrad. However, two optimization models provided the best recognition capabilities, i.e., AdaDelta, and AdaGrad.
{"title":"Brain-Computer Interface of Motor Imagery Using ICA and Recurrent Neural Networks","authors":"Anita Safitri, E. C. Djamal, Fikri Nugraha","doi":"10.1109/IC2IE50715.2020.9274681","DOIUrl":"https://doi.org/10.1109/IC2IE50715.2020.9274681","url":null,"abstract":"Brain-Computer Interface (BCI) is a device that can connect brain commands without the need for movement, gesture, or voice. Usually, BCI uses the Electroencephalogram (EEG) signal as an intermediate device. EEG signals need to be extracted into waves that represent the action in mind. In this study used Wavelet transformation to obtain the imagery motor component from the EEG signal. However, the problem also arises in the considerable channel redundancy in EEG signal recording. Therefore, it requires a signal reduction process. This paper proposed the problem using Independent Component Analysis (ICA). Then ICA components are features of Recurrent Neural Networks (RNN) to classify BCI information into four classes. The experimental results showed that using ICA improved accuracy by up to 99.06%, compared to Wavelet and RNN only, which is only 94.06%. We examined three optimization models, particularly Adam, AdaDelta, and AdaGrad. However, two optimization models provided the best recognition capabilities, i.e., AdaDelta, and AdaGrad.","PeriodicalId":211983,"journal":{"name":"2020 3rd International Conference on Computer and Informatics Engineering (IC2IE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121386036","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 : 2020-09-15DOI: 10.1109/IC2IE50715.2020.9274570
Achmad Akbar Megantara, T. Ahmad
The performance of the Intrusion Detection System (IDS) depends on the quality of the model generated in the training process. An appropriate process positively affects not only the performance but also computational time for detecting intrusions. Reliable training data can be obtained by preprocessing the dataset, which can be feature extraction, reduction, and transformation. Generally, feature selection has become the main problem. In this research, we work on that issue by developing a new method based on Feature Importance Ranking Classification. We propose to reduce the size of the dimension by combining Feature Importance Ranking to calculate the importance of each feature and Recursive Features Elimination (RFE). The results of the experiment show that the proposed method raises the performance over the existing methods. It can be proven by evaluating some metrics: accuracy, sensitivity, specificity, and false alarm rate.
{"title":"Feature Importance Ranking for Increasing Performance of Intrusion Detection System","authors":"Achmad Akbar Megantara, T. Ahmad","doi":"10.1109/IC2IE50715.2020.9274570","DOIUrl":"https://doi.org/10.1109/IC2IE50715.2020.9274570","url":null,"abstract":"The performance of the Intrusion Detection System (IDS) depends on the quality of the model generated in the training process. An appropriate process positively affects not only the performance but also computational time for detecting intrusions. Reliable training data can be obtained by preprocessing the dataset, which can be feature extraction, reduction, and transformation. Generally, feature selection has become the main problem. In this research, we work on that issue by developing a new method based on Feature Importance Ranking Classification. We propose to reduce the size of the dimension by combining Feature Importance Ranking to calculate the importance of each feature and Recursive Features Elimination (RFE). The results of the experiment show that the proposed method raises the performance over the existing methods. It can be proven by evaluating some metrics: accuracy, sensitivity, specificity, and false alarm rate.","PeriodicalId":211983,"journal":{"name":"2020 3rd International Conference on Computer and Informatics Engineering (IC2IE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129069093","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 : 2020-09-15DOI: 10.1109/IC2IE50715.2020.9274631
Annisa Dwiayu Ramadhanty, Avon Budiono, A. Almaarif
Windows is one of the popular operating systems in use today, while Universal Serial Bus (USB) is one of the mechanisms used by many people with practical plug and play functions. USB has long been used as a vector of attacks on computers. One method of attack is Keylogger. The Keylogger can take advantage of existing vulnerabilities in the Windows 10 operating system attacks carried out in the form of recording computer keystroke activity without the victim knowing. In this research, an attack will be carried out by running a Powershell Script using BadUSB to be able to activate the Keylogger program. The script is embedded in the Arduino Pro Micro device. The results obtained in the Keyboard Injection Attack research using Arduino Pro Micro were successfully carried out with an average time needed to run the keylogger is 7.474 seconds with a computer connected to the internet. The results of the keylogger will be sent to the attacker via email.
Windows是当今最流行的操作系统之一，而通用串行总线(USB)是许多人使用的具有实际即插即用功能的机制之一。长期以来，USB一直被用作攻击计算机的载体。一种攻击方法是键盘记录器。Keylogger可以利用Windows 10操作系统中现有的漏洞，在受害者不知情的情况下以记录计算机击键活动的形式进行攻击。在本研究中，攻击将通过使用BadUSB运行Powershell脚本来激活Keylogger程序。该脚本嵌入在Arduino Pro Micro设备中。在使用Arduino Pro Micro的键盘注入攻击研究中获得的结果成功进行，在连接到互联网的计算机上运行键盘记录程序的平均时间为7.474秒。键盘记录器的结果将通过电子邮件发送给攻击者。
{"title":"Implementation and Analysis of Keyboard Injection Attack using USB Devices in Windows Operating System","authors":"Annisa Dwiayu Ramadhanty, Avon Budiono, A. Almaarif","doi":"10.1109/IC2IE50715.2020.9274631","DOIUrl":"https://doi.org/10.1109/IC2IE50715.2020.9274631","url":null,"abstract":"Windows is one of the popular operating systems in use today, while Universal Serial Bus (USB) is one of the mechanisms used by many people with practical plug and play functions. USB has long been used as a vector of attacks on computers. One method of attack is Keylogger. The Keylogger can take advantage of existing vulnerabilities in the Windows 10 operating system attacks carried out in the form of recording computer keystroke activity without the victim knowing. In this research, an attack will be carried out by running a Powershell Script using BadUSB to be able to activate the Keylogger program. The script is embedded in the Arduino Pro Micro device. The results obtained in the Keyboard Injection Attack research using Arduino Pro Micro were successfully carried out with an average time needed to run the keylogger is 7.474 seconds with a computer connected to the internet. The results of the keylogger will be sent to the attacker via email.","PeriodicalId":211983,"journal":{"name":"2020 3rd International Conference on Computer and Informatics Engineering (IC2IE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132870039","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 : 2020-09-15DOI: 10.1109/IC2IE50715.2020.9274636
Devvi Sarwinda, Terry Argyadiva, Saragih Leonardo B. S., Mahesa Oktareza, P. Handi Bagus, Feraldi Fauzan, Billy Erickson
The culinary industry in Indonesia is growing fast in this era of globalization. With the increasing trend in fast food and other foreign culinary, Indonesia is threatening its local culinary existential. A lack of database about local culinary causing a lack of information in its people. Therefore, there is a need for a system model that can identify a local culinary. Hence, it can provide easy access to information to Indonesian and provide a database. This database can also help the government to promote Indonesian food and a chance to keep their existence. This research proposes to make a model that can identify Indonesian food with deep learning techniques. Convolutional Neural Network is chosen as a deep learning technique to recognize ten types of Indonesian food, namely kue rangi, kue putu, bika ambon, ayam taliwang, putu mayang, kerak telor, kue ape, papeda, gudeg, and sate bandeng. We used ResNet50 architecture to classify multi-class labeling. Datasets will consist of 200 images and will be duplicated into three models of separating datasets. In the first model, the dataset has a composition of 75% training dataset and a 25% testing dataset. Similarly, the second model, the dataset has 80:20 of composition, and the third model has 85:15 of composition for training and testing dataset. The experimental results show the third model has the best accuracy of 100%, with 30/30 images predicted correctly.
在这个全球化的时代，印尼的烹饪产业发展迅速。随着快餐和其他外国烹饪的增长趋势，印度尼西亚正在威胁其本土烹饪的生存。缺乏关于当地烹饪的数据库导致缺乏关于其人民的信息。因此，需要一个能够识别当地烹饪的系统模型。因此，它可以方便地向印尼语提供信息，并提供一个数据库。这个数据库还可以帮助政府推广印尼食品，并有机会保持它们的存在。本研究提出用深度学习技术制作一个可以识别印尼食物的模型。选择卷积神经网络作为深度学习技术来识别十种印度尼西亚食物，分别是kue rangi, kue putu, bika ambon, ayam taliwang, putu mayang, kerak telor, kue ape, papeda, gudeg和secure bandeng。我们使用ResNet50架构对多类标注进行分类。数据集将由200张图像组成，并将复制到三个分离数据集的模型中。在第一个模型中，数据集由75%的训练数据集和25%的测试数据集组成。同样，第二个模型的数据集的组成比为80:20，第三个模型的训练和测试数据集的组成比为85:15。实验结果表明，第三种模型的准确率最高，达到100%，其中30/30的图像预测正确。
{"title":"Automatic Multi-class Classification of Indonesian Traditional Food using Convolutional Neural Networks","authors":"Devvi Sarwinda, Terry Argyadiva, Saragih Leonardo B. S., Mahesa Oktareza, P. Handi Bagus, Feraldi Fauzan, Billy Erickson","doi":"10.1109/IC2IE50715.2020.9274636","DOIUrl":"https://doi.org/10.1109/IC2IE50715.2020.9274636","url":null,"abstract":"The culinary industry in Indonesia is growing fast in this era of globalization. With the increasing trend in fast food and other foreign culinary, Indonesia is threatening its local culinary existential. A lack of database about local culinary causing a lack of information in its people. Therefore, there is a need for a system model that can identify a local culinary. Hence, it can provide easy access to information to Indonesian and provide a database. This database can also help the government to promote Indonesian food and a chance to keep their existence. This research proposes to make a model that can identify Indonesian food with deep learning techniques. Convolutional Neural Network is chosen as a deep learning technique to recognize ten types of Indonesian food, namely kue rangi, kue putu, bika ambon, ayam taliwang, putu mayang, kerak telor, kue ape, papeda, gudeg, and sate bandeng. We used ResNet50 architecture to classify multi-class labeling. Datasets will consist of 200 images and will be duplicated into three models of separating datasets. In the first model, the dataset has a composition of 75% training dataset and a 25% testing dataset. Similarly, the second model, the dataset has 80:20 of composition, and the third model has 85:15 of composition for training and testing dataset. The experimental results show the third model has the best accuracy of 100%, with 30/30 images predicted correctly.","PeriodicalId":211983,"journal":{"name":"2020 3rd International Conference on Computer and Informatics Engineering (IC2IE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127679353","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 : 2020-09-15DOI: 10.1109/IC2IE50715.2020.9274633
E. S. Wahyuni, Faiz Khairul Isbat, Anggara Jatu Kusumawati
Face recognition is a technology that is widely used in the field of security and biometrics. Every human face has unique features such as the shape of the jaw and the contours of the face. In this study, face detection was carried out using the Phase Only Correlation (POC) method. POC is a face detection method based on the highest correlation value from the calculation of the phase and magnitude of an image. The data used in this study is in the form of facial images in RGB format. Stages of the study were divided into two, namely preprocessing and processing. The pre-processing stage includes the Region of Interest (ROI), image segmentation, grayscale transformation, and Discrete Fourier Transform (DFT). The processing phase includes Cross Spectrum, Inverse Cross Spectrum values, and correlation calculation. There are three testing schemes carried out in this study, namely testing of variations in lighting levels, expressions, and facial positions. From the results of the study, different face positions produce incorrect detection results because changes in the position of the image being tested make changes in the frequency distribution of the phase and magnitude values.
{"title":"Face Recognition Based on Phase Only Correlation (POC)","authors":"E. S. Wahyuni, Faiz Khairul Isbat, Anggara Jatu Kusumawati","doi":"10.1109/IC2IE50715.2020.9274633","DOIUrl":"https://doi.org/10.1109/IC2IE50715.2020.9274633","url":null,"abstract":"Face recognition is a technology that is widely used in the field of security and biometrics. Every human face has unique features such as the shape of the jaw and the contours of the face. In this study, face detection was carried out using the Phase Only Correlation (POC) method. POC is a face detection method based on the highest correlation value from the calculation of the phase and magnitude of an image. The data used in this study is in the form of facial images in RGB format. Stages of the study were divided into two, namely preprocessing and processing. The pre-processing stage includes the Region of Interest (ROI), image segmentation, grayscale transformation, and Discrete Fourier Transform (DFT). The processing phase includes Cross Spectrum, Inverse Cross Spectrum values, and correlation calculation. There are three testing schemes carried out in this study, namely testing of variations in lighting levels, expressions, and facial positions. From the results of the study, different face positions produce incorrect detection results because changes in the position of the image being tested make changes in the frequency distribution of the phase and magnitude values.","PeriodicalId":211983,"journal":{"name":"2020 3rd International Conference on Computer and Informatics Engineering (IC2IE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121079609","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 : 2020-09-15DOI: 10.1109/IC2IE50715.2020.9274637
Ernes Randika Pratama, F. Renaldi, Fajri Rachmat Umbara, E. C. Djamal
In this day and age, many technologies are used to easily track the whereabouts of others, as in this study, Geofencing technology is used to track or monitor the existence of geriatric patients. This is done because geriatric patients who have dementia and Alzheimer’s have impaired brain memory that can potentially get lost somewhere and cannot go home. Therefore, with the Geofencing technology, the movement of patients can be virtually restricted without disturbing real activities by creating boundaries in the patient’s area. By utilizing GPS technology, the signal of a patient’s cellular device can be tracked or monitored. If one day, the patient crosses the Geofencing area, the supervisor will receive a notification from the system and handle the patient so as not to get lost or lost. The patient’s movements can be monitored by the system simply by utilizing the GPS that is on the patient’s device to stay on, and unnoticed by the patient. By utilizing Geofencing technology to monitor patients who are in the room, you can minimize things that can make patients get lost or lost. To fulfill all of that, several things must be done, such as Geofencing area analysis, performing calculations with formula Haversine, and making messages or notifications that are stored on the FCM server. From all of that will produce a system that can supervise patients and get the closest distance between patients and caregiver, as in the results of calculations between patients and caregiver that produce a distance of Ill meters. This system will also receive notifications automatically with the patient out of the Geofencing area. So it can be concluded that by getting the closest distance between the patient and the caregiver, it can speed up the caregiver in handling the patient beyond the Geofencing area boundary and increasing patient safety.
{"title":"Geofencing Technology in Monitoring of Geriatric Patients Suffering from Dementia and Alzheimer","authors":"Ernes Randika Pratama, F. Renaldi, Fajri Rachmat Umbara, E. C. Djamal","doi":"10.1109/IC2IE50715.2020.9274637","DOIUrl":"https://doi.org/10.1109/IC2IE50715.2020.9274637","url":null,"abstract":"In this day and age, many technologies are used to easily track the whereabouts of others, as in this study, Geofencing technology is used to track or monitor the existence of geriatric patients. This is done because geriatric patients who have dementia and Alzheimer’s have impaired brain memory that can potentially get lost somewhere and cannot go home. Therefore, with the Geofencing technology, the movement of patients can be virtually restricted without disturbing real activities by creating boundaries in the patient’s area. By utilizing GPS technology, the signal of a patient’s cellular device can be tracked or monitored. If one day, the patient crosses the Geofencing area, the supervisor will receive a notification from the system and handle the patient so as not to get lost or lost. The patient’s movements can be monitored by the system simply by utilizing the GPS that is on the patient’s device to stay on, and unnoticed by the patient. By utilizing Geofencing technology to monitor patients who are in the room, you can minimize things that can make patients get lost or lost. To fulfill all of that, several things must be done, such as Geofencing area analysis, performing calculations with formula Haversine, and making messages or notifications that are stored on the FCM server. From all of that will produce a system that can supervise patients and get the closest distance between patients and caregiver, as in the results of calculations between patients and caregiver that produce a distance of Ill meters. This system will also receive notifications automatically with the patient out of the Geofencing area. So it can be concluded that by getting the closest distance between the patient and the caregiver, it can speed up the caregiver in handling the patient beyond the Geofencing area boundary and increasing patient safety.","PeriodicalId":211983,"journal":{"name":"2020 3rd International Conference on Computer and Informatics Engineering (IC2IE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116890396","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 : 2020-09-15DOI: 10.1109/IC2IE50715.2020.9274597
Dezenia Zain Rachmawati, A. Agus
E-commerce in Indonesia is growing fast. Nowadays, people tend to purchase things through e-commerce. However, the players within the e-commerce business not only e-commerce platform itself, but there are also logistics providers and customers. Both e-commerce and logistics services have service quality that plays an important role for the customer. This study uses descriptive research to analyses the impact of e-commerce service quality and logistics service quality on customer satisfaction and customer loyalty. This research used purposive sampling form Shopee Indonesia, as top 2 market leader in Indonesia Market. Data collected through online questionnaires with total respondents consisted of 546 Shopee users. Data is processed with Structural Equation Modelling method and for data analysis using LISREL software version 8.8. The results conclude that the e-service quality and logistics services quality both has a direct and positive impact on customer satisfaction and customer loyalty. This study helps e-commerce and logistics services to understand their customer so they can improve their service quality to keep existing customer and attract the new customer.
{"title":"E-Service and Logistics Service Quality in E-Commerce, Study Case: Shopee Indonesia","authors":"Dezenia Zain Rachmawati, A. Agus","doi":"10.1109/IC2IE50715.2020.9274597","DOIUrl":"https://doi.org/10.1109/IC2IE50715.2020.9274597","url":null,"abstract":"E-commerce in Indonesia is growing fast. Nowadays, people tend to purchase things through e-commerce. However, the players within the e-commerce business not only e-commerce platform itself, but there are also logistics providers and customers. Both e-commerce and logistics services have service quality that plays an important role for the customer. This study uses descriptive research to analyses the impact of e-commerce service quality and logistics service quality on customer satisfaction and customer loyalty. This research used purposive sampling form Shopee Indonesia, as top 2 market leader in Indonesia Market. Data collected through online questionnaires with total respondents consisted of 546 Shopee users. Data is processed with Structural Equation Modelling method and for data analysis using LISREL software version 8.8. The results conclude that the e-service quality and logistics services quality both has a direct and positive impact on customer satisfaction and customer loyalty. This study helps e-commerce and logistics services to understand their customer so they can improve their service quality to keep existing customer and attract the new customer.","PeriodicalId":211983,"journal":{"name":"2020 3rd International Conference on Computer and Informatics Engineering (IC2IE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124344480","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 : 2020-09-15DOI: 10.1109/IC2IE50715.2020.9274674
S. Sridhar, Sowmya Sanagavarapu
There is a gap observed between the natural language (NL) of speech and writing a program to generate code. Programmers should know the syntax of the programming language in order to code. The aim of the proposed model is to do away with the syntactic structure of a programming language and the user can specify the instructions in human interactive form, using either text or speech. The designed solution is an application based on speech recognition and user interaction to make coding faster and efficient. Lexical, syntax and semantic analysis is performed on the user’s instructions and then the code is generated. C is used as the programming language in the proposed model. The code editor is a web page and the user instructions are sent to a Flask server for processing. Using Python libraries NLTK and ply libraries, conversion of human language data to programmable C codes is done and the code is returned to the client. Lex is used for tokenization and the LALR parser of Yacc processes the syntax specifications to generate an output procedure. The results are recorded and analyzed for time taken to convert the NL commands to code and the efficiency of the implementation is measured with accuracy, precision and recall.
{"title":"A Compiler-based Approach for Natural Language to Code Conversion","authors":"S. Sridhar, Sowmya Sanagavarapu","doi":"10.1109/IC2IE50715.2020.9274674","DOIUrl":"https://doi.org/10.1109/IC2IE50715.2020.9274674","url":null,"abstract":"There is a gap observed between the natural language (NL) of speech and writing a program to generate code. Programmers should know the syntax of the programming language in order to code. The aim of the proposed model is to do away with the syntactic structure of a programming language and the user can specify the instructions in human interactive form, using either text or speech. The designed solution is an application based on speech recognition and user interaction to make coding faster and efficient. Lexical, syntax and semantic analysis is performed on the user’s instructions and then the code is generated. C is used as the programming language in the proposed model. The code editor is a web page and the user instructions are sent to a Flask server for processing. Using Python libraries NLTK and ply libraries, conversion of human language data to programmable C codes is done and the code is returned to the client. Lex is used for tokenization and the LALR parser of Yacc processes the syntax specifications to generate an output procedure. The results are recorded and analyzed for time taken to convert the NL commands to code and the efficiency of the implementation is measured with accuracy, precision and recall.","PeriodicalId":211983,"journal":{"name":"2020 3rd International Conference on Computer and Informatics Engineering (IC2IE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123496022","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 : 2020-09-15DOI: 10.1109/IC2IE50715.2020.9274664
Aslon Damanik, P. W. Handayani, A. Pinem
The Indonesian government has sought to implement good governance by issuing a series of laws and regulations in governing the management of state finances. The State Audit Agency (BPK) has the responsibility to conduct finance audit of the government institution based on established auditing standards. One of the strategies that has become the focus of BPK in encouraging auditors to utilize IT services optimally in the audit process. However, based on the performance accountability report IT utilization only reach 80.84% with original target of 97.87%. Thus, this study aims to analyze the factors that become the obstacles in the use of IT in the audit process. The factor was categorized in four dimensions which are technological, organization, environment and human. The data was collected using questionnaire and 115 valid data were analyzed using entropy method to assess the weight of each factors. The results shown that human dimension is the most importance factor to be considered by BPK in IT utilization. However, based on current condition the technological factor need to be improvement urgently since it is the most cited barrier by the respondents.
{"title":"The Barriers of IT Utilization: A Case Study of Indonesian Audit Organisation","authors":"Aslon Damanik, P. W. Handayani, A. Pinem","doi":"10.1109/IC2IE50715.2020.9274664","DOIUrl":"https://doi.org/10.1109/IC2IE50715.2020.9274664","url":null,"abstract":"The Indonesian government has sought to implement good governance by issuing a series of laws and regulations in governing the management of state finances. The State Audit Agency (BPK) has the responsibility to conduct finance audit of the government institution based on established auditing standards. One of the strategies that has become the focus of BPK in encouraging auditors to utilize IT services optimally in the audit process. However, based on the performance accountability report IT utilization only reach 80.84% with original target of 97.87%. Thus, this study aims to analyze the factors that become the obstacles in the use of IT in the audit process. The factor was categorized in four dimensions which are technological, organization, environment and human. The data was collected using questionnaire and 115 valid data were analyzed using entropy method to assess the weight of each factors. The results shown that human dimension is the most importance factor to be considered by BPK in IT utilization. However, based on current condition the technological factor need to be improvement urgently since it is the most cited barrier by the respondents.","PeriodicalId":211983,"journal":{"name":"2020 3rd International Conference on Computer and Informatics Engineering (IC2IE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121666790","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：