Pub Date : 2022-07-27DOI: 10.1109/ISITDI55734.2022.9944506
Wahyudi, Ricky Akbar, Teguh Nurhadi Suharsono, A. S. Indrapriyatna
The covid-19 pandemic has been pushing the development of online learning systems in Indonesia. In online learning, computer-based essay tests and assessments have an essential role. Essay test systems are designed to mimic the concept of essay tests without being computer-based. The answer from the lecturer is compared to the response from the student. The TF-IDF (Term Frequency -Inverse Document Frequency) cosine similarity is used. It is one of the methods of information re-gathering systems. The process in this model consists of two types: 1) creating a corpus/ inverted file, and the second is cosine similarity (CS) for calculating the similarity of the user's answers with the lecturer's. Creating a corpus/inverted file involves several stages like data collection, parsing sentences into terms, stoplist, weighting with IDF, and term weighting using TF-IDF. The cosine similarity process consists of parsing users' answers, weighting users' answers using TF-IDF, and finding cosine similarity values of users' answers with lecturers' answers using the vector space model. The highest cosine similarity value is taken to give the user's answer points. Testing the Essay Test system produces excellent grades. The tests were done Mean Squared Error (MSE) values resulted in an average MSE value of 3.28 from three students.
2019冠状病毒病大流行推动了印度尼西亚在线学习系统的发展。在在线学习中，基于计算机的论文测试和评估起着至关重要的作用。论文测试系统旨在模仿论文测试的概念，而不是基于计算机的。讲师的回答与学生的回答相比较。使用TF-IDF (Term Frequency -Inverse Document Frequency)余弦相似度。它是信息再收集系统的方法之一。该模型中的过程包括两种类型:1)创建语料库/倒排文件，第二种是余弦相似度(CS)，用于计算用户的答案与讲师的答案的相似度。创建语料库/反向文件涉及几个阶段，如数据收集、将句子解析为术语、停止列表、使用IDF加权和使用TF-IDF加权术语。余弦相似度过程包括解析用户答案，使用TF-IDF对用户答案进行加权，使用向量空间模型找到用户答案与讲师答案的余弦相似度值。取最大的余弦相似度值来给出用户的答案点。测试论文测试系统产生优秀的成绩。均方误差(MSE)值导致三个学生的平均MSE值为3.28。
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Pub Date : 2022-07-27DOI: 10.1109/ISITDI55734.2022.9944397
Dodon Yendri, Lathifah Arief, Desta Yolanda, Humaira, Fauzan Muhammad
Practicum activities in the laboratory usually equipped with tools and components that must be prepared in advance. This study aims to develop an application for recognizing laboratory tools and components. The application is designed for Android-baced devices by utilizing the smartphone camera and developed using Tiny YOLO. The development follows System Development Life Cycle (SDLC) methodology using waterfall model. The system then tested by training data on 1,666 image objects obtained from Google in the form of laboratory tools and components such as Arduino, Raspberry Pi, HC-05 sensor, Esp-32 Module, Multimeter, Oscilloscope, and Function Generator. The results showed that the system can detect components and laboratory tools at an optimal distance of 25-35 cm and the accuracy of object detection is influenced by the light conditions in the. From several components tested, the object detection accuracy rate for Arduino Uno is 73.33%, Raspberry Pi is 82.5%, Bluetooth HC-05 module is 86.84%, Esp32 module is 84.37%, Multimeter is 80.6%, Oscilloscope is 76.31% and 80% function generator.
{"title":"Development of Component Recognition Applications and Labor Tools Based on Android and Tiny Yolo Network (Case Study: Signal and System Laboratory)","authors":"Dodon Yendri, Lathifah Arief, Desta Yolanda, Humaira, Fauzan Muhammad","doi":"10.1109/ISITDI55734.2022.9944397","DOIUrl":"https://doi.org/10.1109/ISITDI55734.2022.9944397","url":null,"abstract":"Practicum activities in the laboratory usually equipped with tools and components that must be prepared in advance. This study aims to develop an application for recognizing laboratory tools and components. The application is designed for Android-baced devices by utilizing the smartphone camera and developed using Tiny YOLO. The development follows System Development Life Cycle (SDLC) methodology using waterfall model. The system then tested by training data on 1,666 image objects obtained from Google in the form of laboratory tools and components such as Arduino, Raspberry Pi, HC-05 sensor, Esp-32 Module, Multimeter, Oscilloscope, and Function Generator. The results showed that the system can detect components and laboratory tools at an optimal distance of 25-35 cm and the accuracy of object detection is influenced by the light conditions in the. From several components tested, the object detection accuracy rate for Arduino Uno is 73.33%, Raspberry Pi is 82.5%, Bluetooth HC-05 module is 86.84%, Esp32 module is 84.37%, Multimeter is 80.6%, Oscilloscope is 76.31% and 80% function generator.","PeriodicalId":312644,"journal":{"name":"2022 International Symposium on Information Technology and Digital Innovation (ISITDI)","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131676247","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 : 2022-07-27DOI: 10.1109/ISITDI55734.2022.9944515
Inggit Putri Naria, S. Sulistyo, Widyawan
The Internet of Things is making an impact in a variety of fields, including healthcare, e-government, smart grid, smart farming, smart building, transportation, and so on. The evolution of the Internet of Things is also directly proportional to security and privacy concerns. Security and privacy issues in the Internet of Things world have been a hot topic among researchers in latest days. The Internet of Things' broader and faster development allows for more significant opportunities for security issues. Starting with attacks on IoT applications themselves, and progressing to attacks on user data, specifically on smart building application users. The study examined security and privacy issues in IoT devices using literature reviews. The review paper method was used by the researchers to complete the study using previous literature. The results of this study show that in its application, by connecting and integrating systems, the level of attacks and vulnerabilities will increase, so that in the future, there is a need for ways that can reduce these risks, one of which is by providing service systems that have high levels of security capabilities.
{"title":"Security and Privacy Issue in Internet of Things, Smart Building System: A Review","authors":"Inggit Putri Naria, S. Sulistyo, Widyawan","doi":"10.1109/ISITDI55734.2022.9944515","DOIUrl":"https://doi.org/10.1109/ISITDI55734.2022.9944515","url":null,"abstract":"The Internet of Things is making an impact in a variety of fields, including healthcare, e-government, smart grid, smart farming, smart building, transportation, and so on. The evolution of the Internet of Things is also directly proportional to security and privacy concerns. Security and privacy issues in the Internet of Things world have been a hot topic among researchers in latest days. The Internet of Things' broader and faster development allows for more significant opportunities for security issues. Starting with attacks on IoT applications themselves, and progressing to attacks on user data, specifically on smart building application users. The study examined security and privacy issues in IoT devices using literature reviews. The review paper method was used by the researchers to complete the study using previous literature. The results of this study show that in its application, by connecting and integrating systems, the level of attacks and vulnerabilities will increase, so that in the future, there is a need for ways that can reduce these risks, one of which is by providing service systems that have high levels of security capabilities.","PeriodicalId":312644,"journal":{"name":"2022 International Symposium on Information Technology and Digital Innovation (ISITDI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130062163","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 : 2022-07-27DOI: 10.1109/ISITDI55734.2022.9944403
R. Astri, A. Kamal, S. Sura
To inhibit the rate of transmission of the Covid-19 virus, one of the efforts made by the Indonesian government is to impose a system of limiting social activities. Thus, resulting in changes in patterns and lifestyles in a short time. Including this “Coffee” activity. A large amount of time available due to WFH has also resulted in an increase in the number of coffee connoisseurs, including the existence of the coffee shop itself. This makes it difficult for coffee fans to choose which coffee shop is the right one to go to desire. So, a recommendation system is needed that aims to provide advice on which coffee shop to choose. The recommendation system is a system that helps users overcome overflowing information by providing specific recommendations for users and it is hoped that these recommendations can meet the wishes and needs of users. There are three types of recommendation systems based on the methods they use, namely collaborative filtering, content-based filtering, and hybrid. The method used is collaborative filtering is often used in recommendation systems. Collaborative filtering is divided into two parts, namely Item-based collaborative filtering and User-based collaborative filtering. This paper uses Item-based collaborative filtering which uses rating data between users to get recommendations. In this technique, each coffee shop that is rated by the user is checked with similar coffee shops, then combines these similar coffee shops into a list of recommendations. The test results show that the Item-based collaborative filtering method with an adjusted cosine similarity algorithm can display recommendations that are by the rating given by the customer.
{"title":"Coffee Shop Recommendation System Using an Item-Based Collaborative Filtering Approach","authors":"R. Astri, A. Kamal, S. Sura","doi":"10.1109/ISITDI55734.2022.9944403","DOIUrl":"https://doi.org/10.1109/ISITDI55734.2022.9944403","url":null,"abstract":"To inhibit the rate of transmission of the Covid-19 virus, one of the efforts made by the Indonesian government is to impose a system of limiting social activities. Thus, resulting in changes in patterns and lifestyles in a short time. Including this “Coffee” activity. A large amount of time available due to WFH has also resulted in an increase in the number of coffee connoisseurs, including the existence of the coffee shop itself. This makes it difficult for coffee fans to choose which coffee shop is the right one to go to desire. So, a recommendation system is needed that aims to provide advice on which coffee shop to choose. The recommendation system is a system that helps users overcome overflowing information by providing specific recommendations for users and it is hoped that these recommendations can meet the wishes and needs of users. There are three types of recommendation systems based on the methods they use, namely collaborative filtering, content-based filtering, and hybrid. The method used is collaborative filtering is often used in recommendation systems. Collaborative filtering is divided into two parts, namely Item-based collaborative filtering and User-based collaborative filtering. This paper uses Item-based collaborative filtering which uses rating data between users to get recommendations. In this technique, each coffee shop that is rated by the user is checked with similar coffee shops, then combines these similar coffee shops into a list of recommendations. The test results show that the Item-based collaborative filtering method with an adjusted cosine similarity algorithm can display recommendations that are by the rating given by the customer.","PeriodicalId":312644,"journal":{"name":"2022 International Symposium on Information Technology and Digital Innovation (ISITDI)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124076295","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 : 2022-07-27DOI: 10.1109/ISITDI55734.2022.9944466
Rizqa Nulhusna, Nur Fajar Taufiq, Y. Ruldeviyani
Data is important for organizations to support their operational and decisional activities. Organizations need to ensure that their data is high quality and appropriate for use. This study was conducted at a Government Organization in Indonesia that is currently focusing on a reform agenda in information technology and databases. The organization established a dedicated data management unit and executed data updating programs to support data quality management (DQM). The purpose of this study is to recommend the strategy to improve the organization's DQM, especially on master data. Therefore, it is important to assess the DQM Maturity Level to determine their current state and build up recommendations upon that. This study assessed the DQM maturity level on the organization's master data using the Data Quality Framework by D. Loshin. Overall, the maturity level of DQM on the organization's master data is at level 3 (defined). Recommendations for improving DQM in the organization based on DQM activities in DMBOK are adopting or developing a data quality framework to guide DQM strategy, managing data quality rules related to data quality dimensions, ensuring data quality publication, establishing data quality SLAs and developing dashboard and reporting applications for data users.
{"title":"Strategy to Improve Data Quality Management: A Case Study of Master Data at Government Organization in Indonesia","authors":"Rizqa Nulhusna, Nur Fajar Taufiq, Y. Ruldeviyani","doi":"10.1109/ISITDI55734.2022.9944466","DOIUrl":"https://doi.org/10.1109/ISITDI55734.2022.9944466","url":null,"abstract":"Data is important for organizations to support their operational and decisional activities. Organizations need to ensure that their data is high quality and appropriate for use. This study was conducted at a Government Organization in Indonesia that is currently focusing on a reform agenda in information technology and databases. The organization established a dedicated data management unit and executed data updating programs to support data quality management (DQM). The purpose of this study is to recommend the strategy to improve the organization's DQM, especially on master data. Therefore, it is important to assess the DQM Maturity Level to determine their current state and build up recommendations upon that. This study assessed the DQM maturity level on the organization's master data using the Data Quality Framework by D. Loshin. Overall, the maturity level of DQM on the organization's master data is at level 3 (defined). Recommendations for improving DQM in the organization based on DQM activities in DMBOK are adopting or developing a data quality framework to guide DQM strategy, managing data quality rules related to data quality dimensions, ensuring data quality publication, establishing data quality SLAs and developing dashboard and reporting applications for data users.","PeriodicalId":312644,"journal":{"name":"2022 International Symposium on Information Technology and Digital Innovation (ISITDI)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124148153","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 : 2022-07-27DOI: 10.1109/ISITDI55734.2022.9944525
Susanti, Mustakim, Rice Novita, Inggih Permana
Convolutional Neural Network (CNN) has proven with good performance in the area of feature extraction. Classification of medical images is often faced with the lack of sufficient amounts of data. Therefore, Transfer Learning can be applied to overcome these problems. Chest x-ray data are complex and require deeper layers for specific features. Resnet built with deep layers specifically focuses on problems that often occur in high-depth architectures, which are prone to decreased accuracy and training errors. Some of the aspects are able to affect the performance of the model such as the depth of convolution layers and training procedures, which include data splitting technique and Optimizers. In this study, the Hold Out data splitting and k-fold cross validation of 5 folds with Optimizer Adaptive Moment Estimation (Adam) and Stochastic Gradient Descent (SGD) on the Resnet-50 and Resnet-101 architectures. The training procedure was applied to 15143 Chest x-ray images measuring 224x224 pixels with parameters epoch 50 and batch size 100. The best value was obtained using k-fold cross validation on Resnet-50 using the SGD optimizer with 99% accuracy.
{"title":"Application of Residual Network Architecture on Covid-19 Chest x-ray Classification","authors":"Susanti, Mustakim, Rice Novita, Inggih Permana","doi":"10.1109/ISITDI55734.2022.9944525","DOIUrl":"https://doi.org/10.1109/ISITDI55734.2022.9944525","url":null,"abstract":"Convolutional Neural Network (CNN) has proven with good performance in the area of feature extraction. Classification of medical images is often faced with the lack of sufficient amounts of data. Therefore, Transfer Learning can be applied to overcome these problems. Chest x-ray data are complex and require deeper layers for specific features. Resnet built with deep layers specifically focuses on problems that often occur in high-depth architectures, which are prone to decreased accuracy and training errors. Some of the aspects are able to affect the performance of the model such as the depth of convolution layers and training procedures, which include data splitting technique and Optimizers. In this study, the Hold Out data splitting and k-fold cross validation of 5 folds with Optimizer Adaptive Moment Estimation (Adam) and Stochastic Gradient Descent (SGD) on the Resnet-50 and Resnet-101 architectures. The training procedure was applied to 15143 Chest x-ray images measuring 224x224 pixels with parameters epoch 50 and batch size 100. The best value was obtained using k-fold cross validation on Resnet-50 using the SGD optimizer with 99% accuracy.","PeriodicalId":312644,"journal":{"name":"2022 International Symposium on Information Technology and Digital Innovation (ISITDI)","volume":"196 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127642329","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 : 2022-07-27DOI: 10.1109/ISITDI55734.2022.9944472
Zequn Song, Ran Sun, Budi Rahmadya, S. Takeda
This paper presents a vibration monitoring system for electrical appliances. This system is based on RFID sensors and edge processing technologies. For long-term monitoring, two different operation modes referred to as standby and active modes are introduced. The difference between the two modes is radio wave radiation times. The standby mode is useful to reduce energy consumption and temperature increase of an RFID reader, and amount of data uploaded to a network. This mode also detects a beginning of a vibration event caused by the motor of an electrical appliance. The standby mode subsequently triggers the active mode. The active mode accurately monitors the vibration event and keeps the measured data only for the active mode. Experiments for monitoring a refrigerator demonstrate that the proposed modes enable efficient vibration detections. This system can prevent unintended COVID-19 vaccine disposals caused by the problematic operation and management of refrigerators.
{"title":"An RFID-Based Battery-Less Vibration Monitoring System for Electrical Appliances","authors":"Zequn Song, Ran Sun, Budi Rahmadya, S. Takeda","doi":"10.1109/ISITDI55734.2022.9944472","DOIUrl":"https://doi.org/10.1109/ISITDI55734.2022.9944472","url":null,"abstract":"This paper presents a vibration monitoring system for electrical appliances. This system is based on RFID sensors and edge processing technologies. For long-term monitoring, two different operation modes referred to as standby and active modes are introduced. The difference between the two modes is radio wave radiation times. The standby mode is useful to reduce energy consumption and temperature increase of an RFID reader, and amount of data uploaded to a network. This mode also detects a beginning of a vibration event caused by the motor of an electrical appliance. The standby mode subsequently triggers the active mode. The active mode accurately monitors the vibration event and keeps the measured data only for the active mode. Experiments for monitoring a refrigerator demonstrate that the proposed modes enable efficient vibration detections. This system can prevent unintended COVID-19 vaccine disposals caused by the problematic operation and management of refrigerators.","PeriodicalId":312644,"journal":{"name":"2022 International Symposium on Information Technology and Digital Innovation (ISITDI)","volume":"264 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113983302","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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