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Design and Analysis of Non-Invasive Blood Glucose Levels Monitoring

Design and Analysis of Non-Invasive Blood Glucose Levels Mon...

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1st International Seminar on Application for Technology of Information and Communication (ISemantic)

作者： Sari, Marti Widya Luthfi, Muhtar Univ PGRI Yogyakarta Program Studi Teknik Informatika Yogyakarta Indonesia

ISBN: (纸本)9781509023264

The first symptoms of a diabetes mellitus patient can be seen by the effect of the increasing blood glucose levels. This study discusses about the design and analysis of monitoring application of non-invasive blood glucose levels. Mostly to check blood glucose levels should be done by taking blood samples from patients, namely invasive. Patients with serious conditions or patients with diabetes mellitus who could not do blood sampling many times require another way for checking blood glucose levels besides taking a blood samples. This method is known as non-invasive technique. According to that conditions, a development tools of non-invasive blood glucose levels measurement is needed. Then, we carried out tests to measure blood glucose levels to the patient using these tools. Testing is done by clipping the finger between sensor and LED. The sensors will measure the light intensity. Then, the result will be shown on the application display. Data analysis was done by measuring the percentage error of the measurement results. Measurement of blood glucose levels was done using an existing tools in the hospital and tools that have been created. The result of this study shows that the result of error percentage measurement above, the lowest percentage is 1.03%, then the highest percentage is 9.40%.

关键词： glucose level photodiode diabetes mellitus non-invasive

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Analysis of the AMARI COVID-19 application with the Technology Acceptance Model Method 1

Analysis of the AMARI COVID-19 application with the Technolo...

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1st International Conference on Advanced Information Scientific Development, ICAISD 2020

作者： Nuryanto, A. Setyawan, O. Riana, D. Hadianti, S. Aji, A.M.B. Pujiastuti, E. Program Studi Ilmu Komputer STMIK Nusa Mandiri Program Studi Teknik Informatika Indonesia

The purpose of this research is to know the influence of the application of AMARI COVID-19 to how well the use of the application is received in the community to prevent the spread of the virusCovid-19. This type of research is explanatory research with a quantitative approach. To this end, their responses to an online questionnaire (n=113) were analyzed using IBM SPSS Statistics 21 as software. The sampling techniques used are simple random sampling. Descriptive analysis is used to analyze the research hypothesis. The test results of the five hypothesis in the study stated that theAMARI COVID-19 application has a significant effect on the prevention of the spread of Covid-19 viruses, meaning it is accepted by the wider community as one of the tools that can help prevent the spread of COVID-19 diseases. © 2020 Published under licence by IOP Publishing Ltd.

关键词： COVID-19

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Comparison of Data Mining Algorithms Using Artificial Neural Networks (ANN) and Naive Bayes for Preterm Birth Prediction 1

Comparison of Data Mining Algorithms Using Artificial Neural...

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1st International Conference on Advanced Information Scientific Development, ICAISD 2020

作者： Puspitasari, Diah Ramanda, Kresna Supriyatna, Adi Wahyudi, Mochamad Sikumbang, Erma Delima Sukmana, Sulaeman Hadi Program Studi Sistem Informasi Fakultas Teknik Dan Informatika Universitas Bina Sarana Informatika Indonesia Program Studi Teknik Informatika Sekolah Tinggi Manajemen Informatika Dan Komputer Nusa Mandiri Indonesia

Premature birth is still a big problem in Indonesia, in general, 15 million babies are born prematurely every year, more than 1 million babies die from complications due to premature birth. Themain purpose of this study is to compare the Artificial Neural Network and Naive Bayes datamining algorithm models to predict preterm birth so as to obtain clinical evidence in preterm birth long before confinement so that sudden preterm birth can be converted to normal nativity. The model proposed in research on the prediction of preterm birth is by applying an Artificial Neural Network (ANN)algorithm and Naive Bayes algorithm. Where the two algorithms will be compared the level of accuracy and the value of the AUC against the prediction of preterm birth The results obtained that the prediction of preterm birth using the Artificial Neural Network (ANN) algorithm produces an accuracy value of 90.67% and an ROC value of 0.954. While the Naive Bayes algorithm produces an accuracy value of 84.53% and an ROC value of 0.929. For this reason, it can be concluded that the Artificial Neural Network (ANN) algorithm has a superior accuracy of 6.14% and 0.025 for its ROC value in predicting preterm birth. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Forecasting

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Linear Stability Analysis to Determine Boundary Between Stable or Unstable Flow in Nonlinear Simulation of Vorticity Equation 2

Linear Stability Analysis to Determine Boundary Between Stab...

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2nd International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Sihotang, Farida Nurmala Sari, Titania Sekolah Tinggi Teknol Bandung Program Studi Tekn Informatika Bandung Indonesia

ISBN: (纸本)9781728172576

The nonlinear simulation of the vorticity equation is exciting because it provides a more realistic view, it is consistent with the previous investment. However, a linear stability analysis is needed to determine the stability limit of the system, which serves as a guide in determining the flow limits for stable and unstable flows. We use numerical methods to solve this linear stability analysis. This analysis utilizes the Fourier series in the linear form of the vorticity equation, then the vorticity-stream equation containing the Fourier coefficient will be formed in the matrix. Next, numerically using Matlab, we look for the eigenvalues of the matrix to determine the k, beta and Reynolds number values. If one of the eigenvalues has a real positive form given k, beta, and Reynolds number, then the equilibrium solution of the vortex equation is unstable. From graphing the real part of the eigenvalues, which is a function of the Reynolds number for some beta with k = 0.5, we get the critical point of the Reynolds number. By using a Reynolds number greater than the critical Reynolds number give result in an unstable fluid from the simulation. Vice versa, simulation using the Reynolds number, which is smaller than the critical Reynolds number will produce a stable flow. And from the linear stability curve, we know that for k > 1, the flow is stable for every Reynolds number.

关键词： eigenvalue equilibrium linear stability Reynold number vorticity

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The Effect of Satisfaction and Loyalty towards Digital Payment System Users among Generation Z in Yogyakarta Special Region 1

The Effect of Satisfaction and Loyalty towards Digital Payme...

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1st International Conference on Advanced Information Scientific Development, ICAISD 2020

作者： Pradiatiningtyas, Diah Dewa, Chriswardana Bayu Safitri, Lina Ayu Kiswati, Sri Program Studi Sistem Informasi Universitas Bina Sarana Informatika Indonesia

Two of the most popular digital wallet application companies in Indonesia are Go-Pay 80% and OVO 60%. To be able to survive in the midst of the intense competition in digital wallet applications, application companies must innovate marketing strategies that focus on consumers. Due to the useof digital wallets among the very large generation Z and increasingly intense competition in the business of digital payment applications, the authors examine the effect of user satisfaction and loyalty towards digital payments among generation Z ( case study on Go-Pay and OVO users in Yogyakarta). This research objective is to analyze the satisfaction and loyalty of digital payment system users among generation Z in Yogyakarta Special Region, so that the researchers can find out the influencing variable to empower the development of digital payment especially Ovo and Go Pay. The reseacrh methodology considered as descriptive research, in which the data collecting technique utilized Likert scale questionnaire and employed purposive sampling for selecting the sample. The data analysis applied multiple regressions. The result of the multiple regression testing shows that satisfaction and loyalty affect the Z-generation respondents using the digital payment with the indicators that the digital payment sounds more easy, cashless, and more benefit. Moreover, the usage of digital payment is easy to track small expenses. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Competition

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Insulator Detection via CNN for UAS Onboard Computers 8

Insulator Detection via CNN for UAS Onboard Computers

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8th International Conference on ICT for Smart Society, ICISS 2021

作者： Wafi, Alif Ijlal Munir, Rinaldi Institut Teknologi Bandung Program Studi Teknik Informatika Sekolah Teknik Elektro Dan Informatika Bandung Indonesia

ISBN: (纸本)9781665416979

This paper proposes the usage of single-stage CNN models for detecting insulators in aerial images and measures their applicability in low-power computing settings that often found in UAS onboard systems. In addition to methods in literature, we also design another network based on YOLOv2 modified with SPP (spatial pyramid pooling) block and CIoU loss as our baseline. Our results shows that while both using SPP block and optimizing the bounding box regression function increases the overall detection accuracy without significant cost, network architectures that is specifically designed for edge devices are much more suitable on said environments. One of such design is SF-YOLO, with computation cost of 3,842 BFLOP (29% lower than YOLOv3 tiny, 86% lower than ours) while retaining AP50 score higher than 0.9, and thus can be further used for autonomous navigation subsystems with proper edge devices. © 2021 IEEE.

关键词： Network architecture

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The Measurement of Maturity Level in Chili's Using Index Pixel 3

The Measurement of Maturity Level in Chili's Using Index Pix...

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3rd International Conference on Science and Technology 2019, ICST 2019

作者： Delina, Nurul Kusuma Ayub Wahab, Iis Hamsir Khairan, Amal Program Studi Teknik Informatika Program Studi Teknik Elektro Fakultas Teknik Universitas Khairun Ternate Indonesia

A lot of knowledge and techniques of image processing which are now available are widely various. Much research as well as development is conducted in terms of detecting ripe fruits, one of which is chili. It is one of the local commodities that is marketable. The process of being ripe chili is relatively fast. Generally, to measure ripeness is still carried out manually. The disadvantage of this method is that the level of accuracy is inconsistent, causing fruit decay in numbers. On behalf of lowering the risk of fruit decay, the research conducted uses index pixel method. This means itself is the normalization of variety illuminated by taking ratio in each group of smallest points in an image processing. In this study, the image taking of chili using box equipped with black background is confirmed to be useful in reducing time of segmentation process. When it is in the process of detecting image by searching for the average of index pixel, then RGB will be counted using Learning Vector Quantization (LVQ) that is the calculation utilizing weighing value. As a result, the classification of maturity level can be seen in the test with a very high percentage value and influence to detect fruit ripeness, signed by the color of RG pixels (red and green) by 94.9% correctly detected, the color pixel RB (red and blue) is quite influential by 93.9% correctly detected, and the color of the GB pixel (green and blue) which was less influential at 63.6% was correctly detected. © Published under licence by IOP Publishing Ltd.

关键词： Pixels

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The Implementation of the MFEP (Multi Factor Evaluation Process) Method in Determining the Learning Model 1

The Implementation of the MFEP (Multi Factor Evaluation Proc...

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1st International Conference on Advanced Information Scientific Development, ICAISD 2020

作者： Warnilah, Ai Ilah Purnia, Dini Silvi Adiwisastra, Miftah Farid Sutisna, Herlan Ratningsih Ardianto, Rian Program Studi Sistem Informasi Universitas Bina Sarana Informatika Indonesia

The success of every generation of children is the hope of the success of the education *** role of educators especially teachers has an important role in achieving the success of their students then a teacher must have a strategy in developing the learning system. A system approach isneeded by using specific learning models, in order to help the teacher design the learning system. With the selection of the right learning model is one of the factors that support the success of the learning system. One factor of student achievement is less than the maximum is the application oflearning models that are not appropriate, the selection of learning models still using manual systems makes the learning models applied less precise and appropriate. The purpose of this study is to determine the decision support system in determining learning models including cooperative learningmodels, problem based learning models, contextual learning models, Direct learning models, with theaim to assist teachers in determining the appropriate learning model to be applied in teaching and learning activities. The method applied is using the MFEP (Multi Factor Evaluation Process) method., by determining and giving subjective / intuitive assessments on factors / criteria that are very influential on indicators / alternatives and ranking systems to find out the alternative with the highest value. The results of this study cooperative learning models are highly recommended in the learning process in determining the level of success of students with a value of 6.18 © 2020 Published under licence by IOP Publishing Ltd.

关键词： Learning systems

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Comparative Analysis on Dimension Reduction Algorithm of Principal Component Analysis and Singular Value Decomposition for Clustering 1

Comparative Analysis on Dimension Reduction Algorithm of Pri...

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1st International Conference on Advanced Information Scientific Development, ICAISD 2020

作者： Muningsih, Elly Nur, Hidayat Muhammad Dwi Imaniawan, Fabriyan Fandi Saifudin Handayani, Vembria Rose Endiarto, Feri Program Studi Sistem Informasi Universitas Bina Sarana Informatika Indonesia

Clustering is a method of dividing datasets into several groups that have similarity or the same characteristics. High-dimensional Datasets will influence the effectiveness of the grouping process. This study compares two dimension reduction algorithms, namely Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) using K-Means clustering method to find out the best algorithm with the smallest Bouldin Davies Index evaluation. The dataset of this study involved publicdata from UCIMachine Learning which contains the number of weekly sales of a product. Data processing is performed by comparing the number of clusters from 3 to 10 and the dimension reduction from 2to 10. From the data processing the RapidMiner tools, application with dimension reduction can provide better results than without dimension reduction. In particular, the PCA algorithm shows better results than the SVD, with which the best number of clusters is 5, and the number of dimensional reductions is 3 with a Bouldin Index of 0.376. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Principal component analysis

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Classification of Autism Using Feature Extraction Speed Up Robust Feature (SURF) with Boosting Algorithm 2

Classification of Autism Using Feature Extraction Speed Up R...

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2nd International Conference on Information Technology Research and Innovation, ICITRI 2023

作者： Siagian, Yorris Muhathir Maqhfirah, D.R. Fakultas Teknik Universitas Medan Area Mahasiswa Program Studi Teknik Informatika Medan Indonesia Fakultas Teknik Universitas Medan Area Program Studi Teknik Informatika Medan Indonesia

ISBN: (纸本)9798350324945

Autism is a complex disorder that affects an individual's neurobehavioral development. This disorder is characterized by restricted and repetitive communication patterns, difficulties in social interaction, and changes in sensory processing. This study aims to classify autistic and normal faces by using feature extraction using the SURF (Speeded Up Robust Features) method and the boosting algorithm. This study uses several variants of boosting algorithms, such as Adaboost, Gradient Boosting, and LightGBM. The dataset used in this study consisted of 100 samples of autistic faces obtained from Special Schools in Medan, as well as 100 samples of normal faces. Comparison of training data and data testing is 70%:30%. The results of this study indicate that the boosting algorithm provides good performance in classifying autistic and normal faces. Among the boosting algorithm variants used, Gradient Boosting achieved the highest accuracy of 91.67%, followed by LightGBM with 88.33% accuracy, and Adaboost with 81.67% accuracy. This finding demonstrates the effectiveness of the boosting algorithm in this classification task. © 2023 IEEE.

关键词： Adaptive boosting

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