Pub Date : 2021-09-23DOI: 10.1109/ICITech50181.2021.9590119
Angelito A. Silverio
The proliferation of conformable devices with embedded wireless capability has propelled the expansion of interconnected devices into a swarm called the Internet of Things (IOT). This allowed the localized sensing while processing is normally done remotely into the cloud. The local sensors need to dissipate low power while providing acceptable performance based on the application. One such sensor is the ubiquitous temperature sensor. Temperature sensing has become a pivotal component in most smart systems for maintaining the device performance at optimum, thereby preventing degradation. Amongst such sensors, solid-state based temperature sensors have proven to provide the widest sensing range as well as promotes integration into a complete system-on-chip. There have been several approaches for the readout circuit either based on MOS or BJT, with either analog or digital outputs. In this work, a low voltage and low power temperature sensor with digital time-based output is presented. The circuit uses a BJT - less current-mode bandgap core incorporating sub-threshold MOS. Temperature dependent output voltages are derived from the core, that drives the source and sink currents of a voltage-frequency converter. The circuit has achieved high linearity of (r2 = 0.99) over the temperature range of −40 to 110 deg C, a power dissipation of just around 30 I-lW at a single supply rail of 1.0 V. The circuit has been designed using TSMC 0.18um technology obtained from MOSIS wafer test runs and was verified using SPICE.
{"title":"Design of a Wide Temperature Range, High Linearity Time Domain CMOS-Based Temperature Sensor for Wearable IOT Applications","authors":"Angelito A. Silverio","doi":"10.1109/ICITech50181.2021.9590119","DOIUrl":"https://doi.org/10.1109/ICITech50181.2021.9590119","url":null,"abstract":"The proliferation of conformable devices with embedded wireless capability has propelled the expansion of interconnected devices into a swarm called the Internet of Things (IOT). This allowed the localized sensing while processing is normally done remotely into the cloud. The local sensors need to dissipate low power while providing acceptable performance based on the application. One such sensor is the ubiquitous temperature sensor. Temperature sensing has become a pivotal component in most smart systems for maintaining the device performance at optimum, thereby preventing degradation. Amongst such sensors, solid-state based temperature sensors have proven to provide the widest sensing range as well as promotes integration into a complete system-on-chip. There have been several approaches for the readout circuit either based on MOS or BJT, with either analog or digital outputs. In this work, a low voltage and low power temperature sensor with digital time-based output is presented. The circuit uses a BJT - less current-mode bandgap core incorporating sub-threshold MOS. Temperature dependent output voltages are derived from the core, that drives the source and sink currents of a voltage-frequency converter. The circuit has achieved high linearity of (r2 = 0.99) over the temperature range of −40 to 110 deg C, a power dissipation of just around 30 I-lW at a single supply rail of 1.0 V. The circuit has been designed using TSMC 0.18um technology obtained from MOSIS wafer test runs and was verified using SPICE.","PeriodicalId":429669,"journal":{"name":"2021 2nd International Conference on Innovative and Creative Information Technology (ICITech)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127346605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-23DOI: 10.1109/ICITech50181.2021.9590150
M. S. Astriani, A. Kurniawan, N. N. Qomariyah
COVID-19 has many symptoms and one of the serious symptoms are heart problems and the increase in body temperature. Checking the heart rate and body temperature can still be useful to be included in our daily live to prevent further spread of the virus. We proposed a solution to let the user do a COVID-19 self-detection by using Magic Mirror with IoT -based technology. This Magic Mirror uses two sensors (heart rate and temperature sensor) to measure user's heart rate and body temperature. If the user is suspected of having COVID-19, an alert will be displayed on the Magic Mirror or smartphone to let the user take further necessary action.
{"title":"COVID-19 Self-Detection Magic Mirror With IoT-based Heart Rate and Temperature Sensors","authors":"M. S. Astriani, A. Kurniawan, N. N. Qomariyah","doi":"10.1109/ICITech50181.2021.9590150","DOIUrl":"https://doi.org/10.1109/ICITech50181.2021.9590150","url":null,"abstract":"COVID-19 has many symptoms and one of the serious symptoms are heart problems and the increase in body temperature. Checking the heart rate and body temperature can still be useful to be included in our daily live to prevent further spread of the virus. We proposed a solution to let the user do a COVID-19 self-detection by using Magic Mirror with IoT -based technology. This Magic Mirror uses two sensors (heart rate and temperature sensor) to measure user's heart rate and body temperature. If the user is suspected of having COVID-19, an alert will be displayed on the Magic Mirror or smartphone to let the user take further necessary action.","PeriodicalId":429669,"journal":{"name":"2021 2nd International Conference on Innovative and Creative Information Technology (ICITech)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127908983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-23DOI: 10.1109/ICITech50181.2021.9590157
Jane Chrestella Marutotamtama, Iwan Setyawan
One regulation that has been established by governments in most countries to curb the spread of Covid-19 is physical distancing. However, many people still ignore the importance of this regulation. Thus, it is important to develop a system that can help enforcing this regulation. In this paper, we propose a system that can automatically detect the presence of humans in a video frame and measure their distances from each other. Object detection is performed using YOLO v3 and the accuracy of distance measurement is enhanced using Bird's Eye View Transformation. Our experiments show that using this transformation yields an accuracy improvement of up to 20.93% compared to the performance of the system without transformation (i.e., from 74.42% to 95, 35% accuracy).
{"title":"Physical Distancing Detection using YOLO v3 and Bird's Eye View Transform","authors":"Jane Chrestella Marutotamtama, Iwan Setyawan","doi":"10.1109/ICITech50181.2021.9590157","DOIUrl":"https://doi.org/10.1109/ICITech50181.2021.9590157","url":null,"abstract":"One regulation that has been established by governments in most countries to curb the spread of Covid-19 is physical distancing. However, many people still ignore the importance of this regulation. Thus, it is important to develop a system that can help enforcing this regulation. In this paper, we propose a system that can automatically detect the presence of humans in a video frame and measure their distances from each other. Object detection is performed using YOLO v3 and the accuracy of distance measurement is enhanced using Bird's Eye View Transformation. Our experiments show that using this transformation yields an accuracy improvement of up to 20.93% compared to the performance of the system without transformation (i.e., from 74.42% to 95, 35% accuracy).","PeriodicalId":429669,"journal":{"name":"2021 2nd International Conference on Innovative and Creative Information Technology (ICITech)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115397874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-23DOI: 10.1109/ICITech50181.2021.9590123
Yakobus Nobel H. Judo Prajitno, D. Setyohadi, B. Y. Dwiandiyanta
Stock forecasting is an important thing in investing stock to find the next movement. The major aim of this research is to forecast the McDonald's stock from New York Stock Exchange using data covering the period from 6 January 2006 to 14 April 2021 on a daily, weekly, and monthly basis. The GRU (Gated Recurrent Unit) method is used to create the training model and making predictions from closing movement McDonald's shares. The results of this study are to determine the best forecasting results using the GRU method based on the accuracy and error values obtained in the existing data. The results on the three data that have been tested showed that the medium-term data (weekly data) provide the best result compared to the others based on the accuracy value, the minimum error value, and consistent results obtained on reneated tests.
{"title":"Forecasting Stock Exchange Using Gated Recurrent Unit","authors":"Yakobus Nobel H. Judo Prajitno, D. Setyohadi, B. Y. Dwiandiyanta","doi":"10.1109/ICITech50181.2021.9590123","DOIUrl":"https://doi.org/10.1109/ICITech50181.2021.9590123","url":null,"abstract":"Stock forecasting is an important thing in investing stock to find the next movement. The major aim of this research is to forecast the McDonald's stock from New York Stock Exchange using data covering the period from 6 January 2006 to 14 April 2021 on a daily, weekly, and monthly basis. The GRU (Gated Recurrent Unit) method is used to create the training model and making predictions from closing movement McDonald's shares. The results of this study are to determine the best forecasting results using the GRU method based on the accuracy and error values obtained in the existing data. The results on the three data that have been tested showed that the medium-term data (weekly data) provide the best result compared to the others based on the accuracy value, the minimum error value, and consistent results obtained on reneated tests.","PeriodicalId":429669,"journal":{"name":"2021 2nd International Conference on Innovative and Creative Information Technology (ICITech)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114370268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-23DOI: 10.1109/ICITech50181.2021.9590155
Anastasia Pratiwi Puji Lestari, H. Purnomo, Fian Yulio Santoso
The conventional method of collecting attendance as evidence of student attendance is considered ineffective because it consumes a lot of time and effort. The validity of the data is questionable. There have been many models that have been applied to facial recognition-based attendance systems. However, this model needs much training data so that the model's accuracy is high. In this study, a modification of the deep neural network model for the attendance system is proposed that can work on a small amount of training data. The proposed model is a modification of the DenseNet201 model with batch normalization and average pooling layer. Even though our model's training time is quite long, this model modification can achieve the highest accuracy value of about 90% compared to other pre-trained models, namely ResNet50 and MobileNet.
{"title":"Application of Deep Neural Network Modifications for Face Recognition in Attendance Systems","authors":"Anastasia Pratiwi Puji Lestari, H. Purnomo, Fian Yulio Santoso","doi":"10.1109/ICITech50181.2021.9590155","DOIUrl":"https://doi.org/10.1109/ICITech50181.2021.9590155","url":null,"abstract":"The conventional method of collecting attendance as evidence of student attendance is considered ineffective because it consumes a lot of time and effort. The validity of the data is questionable. There have been many models that have been applied to facial recognition-based attendance systems. However, this model needs much training data so that the model's accuracy is high. In this study, a modification of the deep neural network model for the attendance system is proposed that can work on a small amount of training data. The proposed model is a modification of the DenseNet201 model with batch normalization and average pooling layer. Even though our model's training time is quite long, this model modification can achieve the highest accuracy value of about 90% compared to other pre-trained models, namely ResNet50 and MobileNet.","PeriodicalId":429669,"journal":{"name":"2021 2nd International Conference on Innovative and Creative Information Technology (ICITech)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123988103","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}
Drug's production recording is one of the most critical processes pharmacy industry. The benefit of recording the drugs is to avoid counterfeit drug. By knowing the exact number of drugs produced will make it easier to track. Counterfeit drugs are still circulating today in the market. Counterfeit drugs are very dangerous even cause death for people who consume it. This qualitative research was conducted using Blockchain technology, which has characteristics such as immutable, unchangeable, and peer-to-peer, which will minimize the possibility of counterfeit drugs. Simulations are carried out using the Multichain application to get a clear picture of how drug production can be recorded in the blockchain (Multichain). This research involved one of Indonesia's largest drug industry companies to give input on Blockchain technology (Multichain) for drug production records. The results, blockchain technology (Multichain), can be used to record drug production. So, it is possible to track the drugs record. This research is very important and useful for the drug industry to ensure the quality of drugs production.
{"title":"Medicine Information Record Based on Blockchain Technology","authors":"Meyliana, Surjandy, Erick Fernando, Cadelina Cassandra, Marjuki","doi":"10.1109/ICITech50181.2021.9590133","DOIUrl":"https://doi.org/10.1109/ICITech50181.2021.9590133","url":null,"abstract":"Drug's production recording is one of the most critical processes pharmacy industry. The benefit of recording the drugs is to avoid counterfeit drug. By knowing the exact number of drugs produced will make it easier to track. Counterfeit drugs are still circulating today in the market. Counterfeit drugs are very dangerous even cause death for people who consume it. This qualitative research was conducted using Blockchain technology, which has characteristics such as immutable, unchangeable, and peer-to-peer, which will minimize the possibility of counterfeit drugs. Simulations are carried out using the Multichain application to get a clear picture of how drug production can be recorded in the blockchain (Multichain). This research involved one of Indonesia's largest drug industry companies to give input on Blockchain technology (Multichain) for drug production records. The results, blockchain technology (Multichain), can be used to record drug production. So, it is possible to track the drugs record. This research is very important and useful for the drug industry to ensure the quality of drugs production.","PeriodicalId":429669,"journal":{"name":"2021 2nd International Conference on Innovative and Creative Information Technology (ICITech)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127250760","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}
MRI can detect soft tissue that contains a brain tumour. Imaging produced by MRI in brain tumours can not be analyzed easily if done manually. Results in a longer time required. Deep learning is part of artificial intelligence that can analyze data automatically. Mobilenet is one of the methods in deep learning that functions to perform the segmentation process of medical images. Mobile Network is a CNN model with high accuracy and less computation. Therefore, this study proposes the use of Mobile Network architecture to classify brain tumour types. Mobile Network there are various categories. This study finds evidence that the application of Mobile networks improves overall accuracy. The best result from the Mobile Network category was MobileNet V2 140×224, which achieved an accuracy test of 94%.
{"title":"Classification of Brain Tumours Types Based On MRI Images Using Mobilenet","authors":"Tsamara Hanifa Arfan, Mardhiya Hayaty, Arifiyanto Hadinegoro","doi":"10.1109/ICITech50181.2021.9590183","DOIUrl":"https://doi.org/10.1109/ICITech50181.2021.9590183","url":null,"abstract":"MRI can detect soft tissue that contains a brain tumour. Imaging produced by MRI in brain tumours can not be analyzed easily if done manually. Results in a longer time required. Deep learning is part of artificial intelligence that can analyze data automatically. Mobilenet is one of the methods in deep learning that functions to perform the segmentation process of medical images. Mobile Network is a CNN model with high accuracy and less computation. Therefore, this study proposes the use of Mobile Network architecture to classify brain tumour types. Mobile Network there are various categories. This study finds evidence that the application of Mobile networks improves overall accuracy. The best result from the Mobile Network category was MobileNet V2 140×224, which achieved an accuracy test of 94%.","PeriodicalId":429669,"journal":{"name":"2021 2nd International Conference on Innovative and Creative Information Technology (ICITech)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133505980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-23DOI: 10.1109/ICITech50181.2021.9590174
Nazmus Shakib Shadin, S. Sanjana, Mayisha Farzana
SARS-CoV-2 has now spread to nearly every part of the world, with the WHO declaring a pandemic because of its rapid spread. One of the diagnostic procedures used to detect the extent of the COVID-19 infection is Chest X-rays. Chest Xrays are commonly used to diagnose lung disorders in the beginning. To improve the accuracy of the computer- aided diagnosis system, a research study assessed how well it can correctly distinguish between non-COVID-19 pneumonia on chest X-ray (CXR) images and COVID-19 pneumonia with the alliance of Artificial Intelligence. COVID-19 pneumonia patients (those that tested positive for COVID-19 antibodies) and non- COVID-19 pneumonia patients (those who did not test positive for COVID-19 antibodies) were included in the analysis. The research was conducted using a standard dataset containing 1563 lung CT scan images of COVID-19 pneumonia and non-COVID-19 pneumonia (virus) patients' samples. The proposed system has two Convolutional Neural Network (CNN) models. The first CNN model using max pooling operation achieved the accuracy, precision, recall, and F1-Score of 98.22%, 98.81 %, 99.33%, and 99.07% respectively and similarly, the second CNN model using average pooling operation performed at 97.82%, 98.60%, 99.13%, and 98.86% respectively
{"title":"Automated Detection of COVID-19 Pneumonia and Non COVID-19 Pneumonia from Chest X-ray Images Using Convolutional Neural Network (CNN)","authors":"Nazmus Shakib Shadin, S. Sanjana, Mayisha Farzana","doi":"10.1109/ICITech50181.2021.9590174","DOIUrl":"https://doi.org/10.1109/ICITech50181.2021.9590174","url":null,"abstract":"SARS-CoV-2 has now spread to nearly every part of the world, with the WHO declaring a pandemic because of its rapid spread. One of the diagnostic procedures used to detect the extent of the COVID-19 infection is Chest X-rays. Chest Xrays are commonly used to diagnose lung disorders in the beginning. To improve the accuracy of the computer- aided diagnosis system, a research study assessed how well it can correctly distinguish between non-COVID-19 pneumonia on chest X-ray (CXR) images and COVID-19 pneumonia with the alliance of Artificial Intelligence. COVID-19 pneumonia patients (those that tested positive for COVID-19 antibodies) and non- COVID-19 pneumonia patients (those who did not test positive for COVID-19 antibodies) were included in the analysis. The research was conducted using a standard dataset containing 1563 lung CT scan images of COVID-19 pneumonia and non-COVID-19 pneumonia (virus) patients' samples. The proposed system has two Convolutional Neural Network (CNN) models. The first CNN model using max pooling operation achieved the accuracy, precision, recall, and F1-Score of 98.22%, 98.81 %, 99.33%, and 99.07% respectively and similarly, the second CNN model using average pooling operation performed at 97.82%, 98.60%, 99.13%, and 98.86% respectively","PeriodicalId":429669,"journal":{"name":"2021 2nd International Conference on Innovative and Creative Information Technology (ICITech)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121262563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-23DOI: 10.1109/ICITech50181.2021.9590140
Danny Sebastian, K. A. Nugraha
The growth of social media pushes every company, even academic institutions to own more than 1 official social media account, which requires more resources, especially in the Customer Service department. One of the ways to avoid this is by using chatbot, as it can increase cost-efficiency while also giving good responses while interacting with the consumers. The chatbot created in this study is developed using Telegram API and webhook method. Other than that, Telegram Bot is used to exchange messages with the users (in this case, the students), higher education institution administrator, and the chatbot. Simple testing shows that all chatbot's functions run well.
{"title":"Academic Customer Service Chatbot Development using TelegramBot API","authors":"Danny Sebastian, K. A. Nugraha","doi":"10.1109/ICITech50181.2021.9590140","DOIUrl":"https://doi.org/10.1109/ICITech50181.2021.9590140","url":null,"abstract":"The growth of social media pushes every company, even academic institutions to own more than 1 official social media account, which requires more resources, especially in the Customer Service department. One of the ways to avoid this is by using chatbot, as it can increase cost-efficiency while also giving good responses while interacting with the consumers. The chatbot created in this study is developed using Telegram API and webhook method. Other than that, Telegram Bot is used to exchange messages with the users (in this case, the students), higher education institution administrator, and the chatbot. Simple testing shows that all chatbot's functions run well.","PeriodicalId":429669,"journal":{"name":"2021 2nd International Conference on Innovative and Creative Information Technology (ICITech)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116318342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-23DOI: 10.1109/ICITech50181.2021.9590139
E. Sediyono, K. Hartomo, Yeremia Alfa Susetyo, Adi Setiwan
This paper discusses research that produces a land asset information system called e-asset. The research method used is an experimental method by creating an asset information system. The system created is given to the Salatiga City Regional Finance Agency (BKD) for internal purposes. Fuzzy Algorithm has been implemented in the system so as to make it easier for users from BKD staff to find the location of land assets. The test results by the user stated that the user was satisfied with the satisfaction level of 3.41 (from scale 0 to 5).
{"title":"The Intelligence Decision Making on Asset Management using Fuzzy Clustering","authors":"E. Sediyono, K. Hartomo, Yeremia Alfa Susetyo, Adi Setiwan","doi":"10.1109/ICITech50181.2021.9590139","DOIUrl":"https://doi.org/10.1109/ICITech50181.2021.9590139","url":null,"abstract":"This paper discusses research that produces a land asset information system called e-asset. The research method used is an experimental method by creating an asset information system. The system created is given to the Salatiga City Regional Finance Agency (BKD) for internal purposes. Fuzzy Algorithm has been implemented in the system so as to make it easier for users from BKD staff to find the location of land assets. The test results by the user stated that the user was satisfied with the satisfaction level of 3.41 (from scale 0 to 5).","PeriodicalId":429669,"journal":{"name":"2021 2nd International Conference on Innovative and Creative Information Technology (ICITech)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131046468","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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