2 10 26 https://repository.horizon.ac.id/files/original/9a9ad48e58c47f6c3e0565122c8fa7ff.pdf 09bbcc5787e690365b4826d224dd3a52 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 7 NO 6 (2023) Contributor An entity responsible for making contributions to the resource Sri Wahyuni Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Contactless Biometric Based on Palm Vein Recognition Using Wavelet and Local Line Binary Patterns Subject The topic of the resource contactless biometrics; fuzzy K-Nearest neighbor; local line binary patterns; palm vein; wavelet Description An account of the resource Palm vein recognition has received much attention due to its advantages compared to other biometrics. Because it is contactless, this biometric system does not require physical contact between the user and the sensor device, thus providing several advantages in terms of comfort during acquisition and being more hygienic. In the palm vein recognition system, the palm vein pattern can be considered as a texture feature. Therefore, this study proposes a contactless biometric system based on palm vein recognition using the Local Line Binary Pattern method for extracting texture features of palm vein images resulting from the decomposition of 2D Wavelet Transformation, to produce a texture descriptor that is small and compatible with the texture characteristics of thin veins. The proposed texture feature extraction method has been tested using the Fuzzy k-NN classification method on 600 palm images with a CRR accuracy of 94.0% with a computation time of 0.057 seconds. Creator An entity primarily responsible for making the resource Jayanti Yusmah Sari, Suharsono Bantun Source A related resource from which the described resource is derived http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource December 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-0760 Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text contactless biometrics fuzzy K-Nearest neighbor local line binary patterns palm vein Wavelet https://repository.horizon.ac.id/files/original/1e0a6f05e6799ed66b63d676b199dddf.pdf 5fb4a179683b46f8c784371f3dafac63 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 7 NO 6 (2023) Contributor An entity responsible for making contributions to the resource Sri Wahyuni Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource CNN Method to Identify the Banana Plant Diseases based on Banana Leaf Images by Giving Models of ResNet50 and VGG-19 Subject The topic of the resource banana disease identification; data processing, machine learning; convolutional neural networks Description An account of the resource Identify banana plant diseases using machine learning with the CNN method to make it easier to identify diseases in banana plants through leaf images. It employs the CNN method, incorporating ResNet50 because ResNet50 is one of the best models and a suitable model for the dataset used, and the VGG-19 model is used because VGG-19 was one of the winning models of the 2014 ImageNet Challenge and is a model that also fits the dataset used. The research objectives encompass dataset processing, model architecture development, evaluation, and result reporting, all aimed at enhancing disease identification in banana plants. The ResNet50 model achieved an impressive 94% accuracy, with 88% precision, 91% recall, and an 89% F1- score, while the VGG-19 model demonstrated strong performance with 91% accuracy, surpassing prior research and highlighting the effectiveness of these models in identifying banana plant diseases through leaf images. In conclusion, the ResNet50 model's exceptional accuracy positions it as the preferred model for CNN-based disease identification in banana plants, offering significant advancements and insights for agricultural practices. Future research opportunities include exploring alternative CNN models, architectural variations, and more extensive training datasets to enhance disease identification accuracy Creator An entity primarily responsible for making the resource Ilham Rahmana Syihad, Muhammad Rizal, Zamah Sari, Yufis Azhar Source A related resource from which the described resource is derived http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource December 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-0760 Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text banana disease identification convolutional neural networks data processing machine learning https://repository.horizon.ac.id/files/original/574d3f63493f40c24692f8567a5667f7.pdf e3839b2293c0a7959df6e7955080782b Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 7 NO 6 (2023) Contributor An entity responsible for making contributions to the resource Sri Wahyuni Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Analysis of the SaintekMu Website Quality on User Satisfaction Using the Modified System Usability Scale and Webqual 4.0 Method Subject The topic of the resource website; SaintekMu; usability; system usability scale; webqual Description An account of the resource At present, websites are a major means of finding or providing information. The Saintekmu website was created to offer top�notch service to students. One way to ensure that the website's services are appropriate and that information technology is being used to its fullest potential is to evaluate the level of service provided and improve its quality. This study aims to compare the results of two methods - the System Usability Scale (SUS) and Webqual - used to determine the quality and expectations of website users. The study distributed questionnaires online using Google Forms and had a sample size of 20 students. The data collected were analyzed using the SPSS program. Results from the SUS method indicated that the website's acceptability range was in the Marginal category, with a score of 69.9 and a classification rating of OK. The Webqual method yielded an R Square of 0.948, indicating that the website's usability, quality, and interaction variables had a significant effect on user satisfaction. All WebQual 4.0 dimensions had a positive and significant effect on user satisfaction, both partially and simultaneously. This study provides Muhammadiyah Saintek University with reference material for evaluating their website in the future Creator An entity primarily responsible for making the resource Fitrah Juliansyah, Abdul Fadlil, Rusydi Umar Source A related resource from which the described resource is derived http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource December 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-0760 Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text SaintekMu system usability scale usability webqual website https://repository.horizon.ac.id/files/original/fe50e127306713c37f67b7b7f0fae08f.pdf 9d48cb1b6587b82ffb2187bd1e064df1 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 7 NO 6 (2023) Contributor An entity responsible for making contributions to the resource Sri Wahyuni Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Linear and Non-Linear Spatio-Temporal Input Selection In Wireless Traffic Networks Prediction using Recurrent Neural Networks Subject The topic of the resource wireless traffic; linear and non-linear; spatio-temporal; recurrent neural network Description An account of the resource For the optimization of computer networks with high bandwidth requirements, wireless network traffic prediction is necessary. Its goal is to reduce maintenance costs and enhance internet services. Feature selection is a major issue in the Multivariate Time Series (MTS) Spatio-temporal modeling. Another problem is the dependency between input features, time-lags, and spatial factor so that an appropriate model is needed. This study aims to provide solutions to two problems. The first is to improve a feature extraction and selection process in Spatio-temporal MTS data for relevant features using Detrended Partial Cross-Correlation Analysis (DPPCA) and non-redundant features associated with linear using Pearson's Correlation (PC) filters and non-linear associations using Symmetrical Uncertainty (SU) and combination of both PCSUF. The second is to develop a Spatio-temporal framework model using Recurrent Neural Networks (RNN) to get a better performance than traditional model. These methods are combined and tested using the dataset of cellular networks with one-hour intervals during November in three locations. Testing the effectiveness of the feature selection technique showed that 27.6% of the total extracted features. The forecasting model with the DPCCA–SU-RNN combination method gets the best performance by having RMSE = 380.7, R 2 = 97%, and MAPE = 10% Creator An entity primarily responsible for making the resource Ahmad Saikhu, Agung Teguh Setyadi, Victor Hariadi Source A related resource from which the described resource is derived http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource December 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-0760 Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text linear and non-linear recurrent neural network spatio-temporal wireless traffic https://repository.horizon.ac.id/files/original/1a5570faef424e1701b4ae285a557d25.pdf 120e30397979aa581659146ff6debffd Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 7 NO 6 (2023) Contributor An entity responsible for making contributions to the resource Sri Wahyuni Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource ANoM STEMMER: Nazief & Andriani Modification for Maduresse Stemming Subject The topic of the resource stemming; morphophonemic; understemming; overstemming; madurese language Description An account of the resource Madurese is one of the regional languages in Indonesia. This is a cultural property that needs to be preserved. With various uniqueness and word formation rules, the Madurese language can be used in Information Retrieval, namely stemming. The Madurese language has a close relationship with the Javanese language, in several studies the stemming method is often used, such as the modification of the Nazief & Adriani method which has good performance for the Javanese language, but there has never been any research on the Madurese language and it has not been proven successful. Previous studies also have not used morphophonemic rules that influence word formation in Madurese. Therefore this research was developed by modifying Nazief & Adriani's algorithm for Madurese language based on Madurese language morphology by removing affixes, namely ter-ater (prefix), panoteng (suffix), and morphophonemic rules. Corpus uses 1000 words from the Madurese language dictionary which have received affixes. The accuracy of the algorithm is 89% with 890 words that match, the prefix has an accuracy of 93.81%, the suffix has an accuracy of 83.78% and the confix has 80.07%. As for the overall performance, it produces an accuracy of 89.0% with an error rate of 11%. the understemming is found in 104 words and overstemming in 6 words. the time it takes to compile is 31.31 seconds Creator An entity primarily responsible for making the resource Enni Lindrawati, Ema Utami, Ainul Yaqin Source A related resource from which the described resource is derived http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource December 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-0760 Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text madurese language morphophonemic overstemming Stemming understemming https://repository.horizon.ac.id/files/original/d396ba12066efe4cf8202134d9fd502d.pdf 5941096b92013fd697a9eb49154562ad Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 7 NO 6 (2023) Contributor An entity responsible for making contributions to the resource Sri Wahyuni Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Comparison of the Accuracy of Drug User Classification Models Using Machine Learning Methods Subject The topic of the resource drug consumption; classification; machine learning; confusion matrix; AUC curve Description An account of the resource Caseand s of drug abuse are on the rise, with many users entering the addiction phase, often resulting in overdose and death. Drugs are chemical compounds that are capable of affecting biological functions, can induce feelings of happiness and reduce pain. To address this growing problem, a proactive measure is needed. Therefore, this study aims to classify drug users and non-users, so that health workers and therapists can educate about the dangers of drugs to non-users and rehabilitate drug users. This study uses drug consumption data taken from the UCI Irvine Machine Learning Repository. The data consists of 1885 rows with 32 attributes and 2 classes, where there are 18 types of legal and illegal drugs. This research utilizes machine learning methods, specifically Artificial Neural Network (ANN), Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest (RF), in addition to evaluation methods such as Confusion Matrix and Area Under Curve (AUC). The results showed that RF outperformed the other methods, with accuracy, precision, and recall of 93%, and an f1-score of 89%, while the AUC value was still suboptimal at 0.66. DT had the worst results, with 82% accuracy, 87% precision, 82% recall, 84% f1-score, and an AUC value of 0.56. With these results, this research can be continued into an application that can classify drug users and non-user Creator An entity primarily responsible for making the resource Nursela Salsabilla Basuni, Amril Mutoi Siregar Source A related resource from which the described resource is derived http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource December 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-0760 Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text AUC curve classification Confusion Matrix drug consumption machine learning https://repository.horizon.ac.id/files/original/2f6b4b53dc2ce80bcf30c7934e812f90.pdf 90cf106c128ae8fa3b951a8b6d47e66e Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 7 NO 6 (2023) Contributor An entity responsible for making contributions to the resource Sri Wahyuni Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Feature Extraction for Improvement Text Classification of Spam YouTube Video Comment using Deep Learning Subject The topic of the resource Improvement; BLSTM; CRF; Data Augmentation; Feature Extraction Description An account of the resource The proposed algorithms are Bidirectional Long Short Term Memory (BiLSTM) and Conditional Random Fields (CRF) with Data Augmentation Technique (DAT). DAT integrates spam YouTube video comments into the traditional TF-IDF algorithm and generates a weighted word vector. The weighted word vector is fed into BiLSTM CRF to capture context information effectively. The result of this study is a new classification model to spam YouTube comment videos and increase the computational value of its performance. This research conducted two experiments: the first using BiLSTM CRF without DAT and the second using BiLSTM CRF with DAT. The experimental results state that the evaluation score using BiLSTM CRF with DAT shows outstanding performance in text classification, especially in spam YouTube video comment texts, with accuracy = 83.3%, precision = 83.6%, recall = 83.3%, and F-measure = 83.3%. So the combination of the BiLSTM-CRF method and the Data Augmentation Technique is very precise, so it can be used to increase the accuracy of classification texts for spam YouTube video comments Creator An entity primarily responsible for making the resource Jasmir Jasmir, Willy Riyadi, Pareza Alam Jusia Source A related resource from which the described resource is derived http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource December 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-0760 Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text BLSTM CRF Data Augmentation Feature extraction Improvement https://repository.horizon.ac.id/files/original/a097b1971d5842db6e3a0eadd5e0678f.pdf 9b02ddd3dd62e577e076b6ccd3736aa1 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 7 NO 6 (2023) Contributor An entity responsible for making contributions to the resource Sri Wahyuni Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Optimization of Machine Learning Classification Analysis of Malnutrition Cases in Children Subject The topic of the resource analysis of classification; malnutrition, artificial neural network (ANN); multilayer perceptron (MLP); west sumatra province Description An account of the resource Malnutrition is one of the problems that occurs in children caused by a lack of nutritional intake. Indonesia contributed 36%, making it the fifth country with the largest cases of malnutrition in the world. Based on this, a solution is needed to reduce the growth rate of malnutrition cases. This research aims to carry out classification analysis to determine nutritional status by optimizing Machine Learning (ML) performance. The ML classification analysis process will later utilize the performance of the Artificial Neural Network (ANN) method with the Multilayer Perceptron (MLP) algorithm. ML performance can be optimized using the Pearson Correlation (PC) method to produce optimal classification analysis patterns. This research dataset uses child nutrition case data of 576 patients sourced from the M. Djamil Padang Province Regional General Hospital (RSUP). The dataset is divided into 417 training data and 159 test data. Based on the tests that have been carried out, the performance of the PC method can provide precise and accurate analysis patterns. This analysis pattern has also been able to provide a fairly good level of accuracy, namely 95%. Not only that, this research is also able to present analysis patterns with the best ANN architectural model in classifying nutritional status. Based on the overall results, this research can be used as an alternative solution to handling nutritional problems in children. Creator An entity primarily responsible for making the resource Musli Yanto, Febri Hadi, Syafri Arlis Source A related resource from which the described resource is derived http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource December 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-0760 Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text analysis of classification artificial neural network (ANN) Malnutrition multilayer perceptron (MLP) west sumatra province https://repository.horizon.ac.id/files/original/9cea711e436503a2e67d17d0a7912e59.pdf 15599979f6aa565de030a4e152a61107 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 7 NO 6 (2023) Contributor An entity responsible for making contributions to the resource Sri Wahyuni Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Artificial Neural Network Based Prediction Model Back Propagation on Blood Demand and Blood Supply Subject The topic of the resource neural network; backpropagation; blood demand; blood supply Description An account of the resource The balance between blood demand and supply at the Indonesian Red Cross Blood Transfusion Unit (UTD-PMI) is crucial. This condition needs to be maintained to reduce unused or expired blood supplies. Despite the situation at UTD-PMI, where the blood supply exceeds the demand, there is still a shortage of blood when needed by patients. This research aims to model the prediction of blood demand and supply for each blood type using the Back Propagation artificial neural network approach. Data from the last 3 years, from 2020 to 2022, were utilized in this research process. There are three stages in this research process. The first stage involves the training process, using data from January 2020 to December 2021. The testing process utilizes data from January 2021 to December 2022. The prediction process involves displaying forecasted data for the next 12 months from January to December 2023. The accuracy of the calculations is assessed using the Mean Square Error (MSE). Ultimately, the research results present the prediction model for the four blood types regarding blood demand and supply. These findings can serve as a reference for regulating future blood donation activities carried out by the UTD-PMI. Creator An entity primarily responsible for making the resource Frengky Tedy, Patrisius Batarius, Ign. Pricher A. N. Samane, Alfry Aristo Jansen Sinlae Source A related resource from which the described resource is derived http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource December 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-0760 Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text Backpropagation blood demand blood supply neural network https://repository.horizon.ac.id/files/original/4988df508ce695321da583dce742fee4.pdf c1f463ef0dcb50ca7cc1fe0bfed9b9a8 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 7 NO 6 (2023) Contributor An entity responsible for making contributions to the resource Sri Wahyuni Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A Cost-Effective Vital Sign Monitoring System Harnessing Smartwatch for Home Care Patients Subject The topic of the resource home care; national early warning score; vital sign; smartwatch; e-health Description An account of the resource Telemedicine applications enable online health consultation services, but they have limitations: there is no vital sign examination as in offline consultation. Furthermore, vital sign examinations for patients treated outside hospitals, such as outpatients and home care patients, are not as routine as for inpatients. Although patient monitors can be used to monitor vital signs in real-time, they are expensive, making it difficult to use them for home care patients. This research aims to develop a vital sign monitoring system for home care patients that integrates a smartwatch as a data acquisition sensor device, a mobile application as a gateway that collects vital sign data and sends it to a web server, and a web application to remotely monitor vital signs and changes in their values. This system also aims to make it easier for patients and their families to communicate with doctors and nurses while viewing vital sign information and the risk of patient deterioration in the form of Early Warning Score (EWS). The smartwatch chosen is based on its features and low price, so a cost-effective vital sign monitoring system can be produced. Testing using the black-box method shows that the system functions well and meets the expected functionality Creator An entity primarily responsible for making the resource Dodon Turianto Nugrahadi, Rudy Herteno, Mohammad Reza Faisal, Nursyifa Azizah, Friska Abadi, Irwan Budiman, Muhammad Itqan Mazdadi Source A related resource from which the described resource is derived http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource December 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-0760 Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text E-health home care national early warning score; smartwatch vital sign