1 10 20 https://repository.horizon.ac.id/files/original/881f37e5b23381f2a64b3fdeddd0e137.pdf 5a5717e66569768bbe79db8104fa3663 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 8 NO 1 (2024) 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 Image Preprocessing Approaches Toward Better Learning Performance with CNN Subject The topic of the resource convolutional network; deep learning; face recognition; advanced preprocessing; classification Description An account of the resource Convolutional neural networks(CNNs) are at the forefront of computer vision, relying heavily on the quality of input data determined by the preprocessing method. An excessivepreprocessing approach will result in poor learning performance. This study critically examines the impact of advanced image preprocessing techniques on convolutional neural networks(CNNs) in facial recognition. Emphasizing the importance of data quality, we explore various preprocessing approaches, including noise reduction, histogram equalization, and image hashing. Our methodology involves feature visualization to improvefacial feature discernment, training convergence analysis, and real-time model testing. The results demonstrate significant improvements in model performance with the preprocessed data set:average precision,recall, precision, and F1 score enhancements of 4.17%, 3.45%, 3.45%, and 3.81%, respectively. Furthermore,real-time testing shows a 21% performance increase and a 1.41% reduction in computing time. This study not only underscores the effectiveness of preprocessing in boosting CNN capabilities,but also opens avenues for future research in applying these methods to diverse image types and exploring various CNN architectures for a completeunderstanding Creator An entity primarily responsible for making the resource Dhimas Tribuana1,Hazriani2*, Abdul Latief Arda3 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5417/886 Publisher An entity responsible for making the resource available Departementof Computer System,Handayani University Makassar, Makassar, Indonesia1MNC Bank, Cabang Makassar, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 13-01-2024 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W 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 https://repository.horizon.ac.id/files/original/ca83b248757d68e66b290b1302ab0730.pdf 10df4c7a676ff96d4bab2cf734ed216a 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 8 NO 1 (2024) 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 Data Mining Techniques for Predictive Classification of Anemia DiseaseSubtypes Subject The topic of the resource anemia; data mining; J48 decision tree; naïve bayes; random forest Description An account of the resource Anemia, characterizedby insufficient red blood cells or reduced hemoglobin,hinders oxygen transport in the body. Understanding thevarioustypes of anemiais vital to tailor effective prevention and treatment. This research explores data mining's role in predicting and classifying anemia types, emphasizingComplete Blood Count (CBC) and demographic data. Data mining is key to building models that aidhealthcare professionals in thediagnosis and treatment of anemia.Employing the Cross-Industry Standard Process for Data Mining (CRISP-DM), with its six phases, facilitates this endeavour. Our study compared Naïve Bayes, J48 Decision Tree, and Random Forest algorithms using RapidMiner's tools, evaluating accuracy, mean recall, and mean precision. The J48Decision Tree outperformed the others, highlighting the importance ofalgorithm choicein anemia classification models. Furthermore,our analysis identified renal disease-related and chronic anemia as the most prevalent types, with ahigher incidenceamong women.Recognizinggender disparities in the prevalence ofanemiainforms personalizedhealthcare decisions. Understanding demographic factors in specific types ofanemiais crucial for effective care strategies. Creator An entity primarily responsible for making the resource Johan Setiawan1, Dita Amalia2, Iwan Prasetiawan3 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5445/887 Publisher An entity responsible for making the resource available Department of Information Systems, Faculty of Engineering and Informatics, Universitas Multimedia Nusantara, Tangerang, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 15-01-2024 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W 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 https://repository.horizon.ac.id/files/original/fb52759b528ee12c04aa6618c906eb99.pdf 778490419f34a21e4fd15dc3273280c1 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 8 NO 1 (2024) 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 Visual Impaired Assistance for Object and Distance Detection Using Convolutional Neural Networks Subject The topic of the resource model; machine learning; vision Description An account of the resource Vision is a very valuable giftfrom God, most aspects of human needs in the body are dominated by vision. Based on data from the World Health Organization (WHO) there are around 180 millionpeople in the world experiencing visual impairment, while the prevalence of blindness in Indonesia reaches 3 million people (1.5% of Indonesia's population), so we designed a system in the form of a prototype that could detect objects around the user and convey data in the form of sound to the user. This research discusses the application of a machine learning model using the Convolutional Neural Network method so that it can detect objects optimally. The objects that have been collected will be trained on machine learning and produce a model to be embedded in the system's main machine, namely the Raspberry PI 4B.Machine learning model training was carried out several times by changing several layer compositions until a model with optimal accuracy was obtained, however,thesize of theresulting model was quite large so the researchers carried out SSDMobileNetV2 transfer learning to obtain the optimal model.The optimal model was obtained with a model accuracy of 92% and a model size of 18 MB. Object detection testing carried out in 3 test conditions resulted in an average object detection accuracy of 84.3%, and distance detection testing carried out in 10 conditions resulted in an average distance detection error of 2.1 cm.TheResults show that the system was accurate and effective Creator An entity primarily responsible for making the resource Jumadi Mabe Parenreng1, Andi Baso Kaswar2, Ibnu Fikrie Syahputra3 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5491/889 Publisher An entity responsible for making the resource available 3Informatic Engineering and Computer Education, State University of Makassar, Makassar, South of Sulawesi, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 20-01-2024 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLIAH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/03d7cecd977ddabca8d699430acd4692.pdf 4afb723dd216ed13993493b5636dc9fe 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 8 NO 1 (2024) 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 The Design of a C1 Document Data Extraction Application Using a Tesseract-Optical Character RecognitionEngine Subject The topic of the resource affine transformation;digital signature;automatic data entry;optical character recognition;RSA-2048;SHA-256;tesseract-OCR Description An account of the resource The 2019 election process employed the Vote Counting Information System, also known as Sistem Informasi Penghitungan Suara (Situng), to provide transparency in the recapitulation process. The data displayed in Situng is from the C1 document for 813,336 voting stations in Indonesia. The data collected from the C1 document is entered and uploaded into Situng by officers at the municipal General Election Commission (GEC). Since this process is performed by humans, it is not immune to errors. In the recapitulation process of the 2019 election results, there were 269 data entry errors, and the data entry process also did not run according to the specified target, resulting in delays. Furthermore, there were cases of C1 document modification, raising concerns about the data's authenticity. To avoid human errors and increase data entry speed, automatic data entry is a plausible option. The data entered is text data in image documents with the same template format, so that optical character recognition (OCR) can be used to read the text while improving image quality and alignment, resulting in a more accurate OCR reading area. In this study, we developed aC1 document data extraction application using the waterfall SDLC method, which has undergone a systematic and thorough process. The application wasdeveloped using Tesseract optical character recognition. Tesseract is an open-source OCR engine and command-line programthat allows for the recognition of text characters within a digital image. The accuracy obtained by using this method is still not optimal as a substitute for Situng's data entry officer. To guarantee the integrity of the C1 document, we used the RSA-2048 digital signature scheme.Usingthe Tesseract-OCR Engine for character recognition, combined with digital signature capabilities, provides a comprehensive solution to reduce the human error factor that might result in miscalculations and inaccurate processes Creator An entity primarily responsible for making the resource Ircham Aji Nugroho1, Bety Hayat Susanti2*,Mareta Wahyu Ardyani3, Nadia Paramita R.A.4 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5151/891 Publisher An entity responsible for making the resource available Department of Cryptographic Engineering, Politeknik Siber dan Sandi Negara Date A point or period of time associated with an event in the lifecycle of the resource 04-02024 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W 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 https://repository.horizon.ac.id/files/original/0e599b01721e45df4d38b3cbb3caf094.pdf 61a9ad06b4b40dcecc7cc27fe4435f0e 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 8 NO 1 (2024) 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 Cattle Weight Estimation Using Linear Regression and Random Forest Regressor Subject The topic of the resource cattle; machine learning; linear regression; random forest regressor; prediction model Description An account of the resource The global cattle farming industry has benefits as a food source, livelihood, economic contribution, land environmental restoration, and energy source. The importance of predicting cow weight for farmers is to monitor animal development. Meanwhile, for traders, knowing the animal's weight makes it easier to calculate the price of the animal meat they buy. The authors propose estimating cattle weighting linear regression and random forest regression. Linear regression can interpret the linear relationship between dependent and independent variables, and random forest regression can generalize the data well. The dataset used in this study consisted of ten variables: live body weight, withers height, sacrum height, chest depth, chest width, maclocks width, hip joint width, oblique body length, oblique back length, and chest circumference. To find out the model that produces the smallest MAE value. The results show that the linear regression algorithm can produce estimated weight values for cattle with the best performance. This model produces a mean absolute error (MAE) of 0.35 kg, a mean absolute percentage error (MAPE) of 0.07%, a root mean square error (RMSE) of 0.5 kg, and an R² of 0.99. Each variable has excellent correlation performance results and contributes to computer vision and machine learning Creator An entity primarily responsible for making the resource Anjar Setiawan1, Ema Utami2, Dhani Ariatmanto3 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5494/894 Publisher An entity responsible for making the resource available Magister of Informatics Engineering, Universitas AMIKOM Yogyakarta, Yogyakarta, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 10-02-2024 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W 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 https://repository.horizon.ac.id/files/original/7ab79c9ae079bf93866124a6d98edde6.pdf 2fd0da996ee9acfeb461601ee1e00a21 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 8 NO 1 (2024) 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 Improving Algorithm Performance using Feature Extraction for Ethereum Forecasting Subject The topic of the resource algorithms;ethereum;feature extraction;forecasting Description An account of the resource Ethereum is a cryptocurrency that is now the second most popular digital asset after Bitcoin. Hightrading volume is the trigger for the popularity of this cryptocurrency. In addition, Ethereum is home to various decentralized applications and acts as a link for Decentralized Finance (DeFi) transactions, Non-Fungible Tokens (NFTs) and the use of smart contracts in the crypto space. This study aims to improve the performance of the forecasting algorithm by using Feature Extraction for Ethereum price forecasting. The algorithms used are Neural Networks, Deep Learning and Support Vector Machines. The research methodology used is Knowledge Discovery in Databases. The dataset used comes from the yahoo.finance.com website regarding Ethereum prices.The research results indicated that the use of Feature Extraction improved the performance of the constructed model. The results show that the Neural Network Algorithm is the best Algorithm compared to Deep Learning and Support Vector Machine. The Root Mean Square Error value for the Neural Network before Feature Selection is 93,248 +/-168,135 (micro average: 186,580 +/-0,000) Linear Sampling method and 54,451 +/-26,771 (micro average: 60,318 +/-0,000) Shuffled Sampling method. Then after the Feature Selection, the Root Mean Square Error value improved to 38,102 +/-31,093 (micro average: 48,600 +/-0,000) usingthe Shuffled Sampling method.This research bridged the gap by either expanding on prior studies or contributing through the comparison of three forecasting algorithms for cryptocurrency datasets. It also compared two feature extraction algorithms, namelyPrincipal Component Analysis and Independent Component Analysis, and employed the T-Test to conduct a performance difference analysis among algorithm results to determine the best model performance Creator An entity primarily responsible for making the resource Indri Tri Julianto1, Dede Kurniadi2, Ricky Rohmanto3, Fathia Alisha Fauzia4 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/4872/895 Publisher An entity responsible for making the resource available 1,2Departmentof Computer Science,Institut Teknologi Garut,Indonesia3DepartmentofInformatic Management, Universitas Ma’some,Bandung,Indonesia4DepartmentofCommunication and Information, Universitas Garut,Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 15-02-2024 Contributor An entity responsible for making contributions to the resource fAJAR BAGUS w 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 https://repository.horizon.ac.id/files/original/da59bf97e594bf961a2f8c98aa0cf410.pdf 1f5a81b33037c93a40b3f53a88c1a681 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 8 NO 1 (2024) 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 Predicting Smart Office Electricity Consumption in Response to Weather Conditions Using Deep Learning Subject The topic of the resource smart office;electricity consumption prediction;weather for load forecasting;deep learning;time series Description An account of the resource This study investigates the intricate relationship between electricity consumption in smart office environments, temporal elements such as time, and external factors such asweather conditions. Usinga data set that encompasseselectrical consumption statistics, temporal data, and weather conditions, the research employs preprocessing, visualization, and featureengineering techniques. The predictive model for electric energy usage is constructed using deep learning architectures, including Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM), Gated Recurrent Unit (GRU), and Bidirectional Gated Recurrent Unit (Bi-GRU). Evaluation metrics reveal that the LSTM model outperforms others, achieving minimal Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). The study acknowledges the limitations of the data set,particularly whencomparing electricity usage during workhours and outside working hours in a residential context. Future research aims to address these limitations, considering detailed meteorological data, missing data imputation, and real-time applications for broaderapplicability. The ultimate goal is to develop a predictive model that serves as a valuable tool for improvingenergy management in smart office settings, optimizing electricity usage, and contributing to long-term firm profitability Creator An entity primarily responsible for making the resource Zikri Wahyuzi1, Ahmad Luthfi2, Dhomas Hatta Fudholi3 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5530/897 Publisher An entity responsible for making the resource available Magister Informatika, Informatika, Universitas Islam Indonesia, Yogyakarta, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 18-02-2024 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W 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 https://repository.horizon.ac.id/files/original/57b4479f535aac8dc536cc134950c1a0.pdf 0fefdcd3e57ce7daf9f8ef896c314540 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 8 NO 1 (2024) 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 Comparative Study of HTTP and MQTT for IoT Applications in Hydroponics Subject The topic of the resource hydroponics; IoT; web service; MQTT; HTTP Description An account of the resource Hydroponics is basedon nutrients in water. It mustbe regularlymonitoredto prevent plant defects. The Internet of Things has become a solution for remote hydroponic monitoring and is currently being tested on the Yuan HidroponikKelompok Wanita Tani (KWT).This system will send data every minute,and each data has a possibility of loss in transmission. There is a chance thatthis system will be implemented in other hydroponic organizations. As more devices are involved,it will affectserver resources. This research will compare Message Queue Telemetry Transport (MQTT) and Hypertext Transfer Protocol (HTTP) as popular protocols used in IoT. A test with increasing clients shows thatat 50 clients HTTP needs 87% CPU,while MQTT needs 22.63% CPU. A test with increasing payload shows thatat 10,000 payload HTTP needs 94% CPU while MQTT needs 28.35% CPU. A test with fixed clients and payloads shows thatHTTP has a CPU limit based on the clients involved. A transfer time test shows thatHTTP needs 177.344 seconds while MQTT needs 3.24 seconds. An acceptance rate is calculated by incrementing the count for every incoming payload. It shows thatHTTP can receive 30,000 payloads, unlike MQTT which can only receive 1680 payloads before losses Creator An entity primarily responsible for making the resource rvan Rizki Nugraha1, Widhy Hayuhardhika Nugraha Putra2,Eko Setiawan Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5561/899 Publisher An entity responsible for making the resource available Information System Department, Fakultas Ilmu Komputer, Universitas Brawijaya, Malang, Indonesia3Informatics Engineering Department, Fakultas Ilmu Komputer, Universitas Brawijaya, Malang, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 18-02-2024 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W 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 https://repository.horizon.ac.id/files/original/e2ff90b4150df0a59c66298f651a9b07.pdf 0fefdcd3e57ce7daf9f8ef896c314540 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 8 NO 1 (2024) 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 Monitoring and Controlling System for Mango Logistics Based on Machine Learning Subject The topic of the resource decision tree; LSTM; machine learning; mango logistics Description An account of the resource Fruits are highly perishable goods, which meansthey have a short shelf life and can pose significant challenges in trade. A longsupply chain can trigger the process of fruit spoilage. The logistics environment, both internal and external, can also affect the decreaseinquality of goods. One common issue facingproducers is the variability in consumer demandfor fruit quality. To address this problem, a machine learning-based logistics monitoring and recommendation system can be developed, utilizing the Long Short-Term Memory (LSTM) and Decision Tree algorithms. Usingmachine learning algorithms, the system can analyze data from devices equipped withthe Internet of Things(IoT),such as temperatureand humidity sensors,to identify potential issues in the supply chain and provide recommendations to optimizelogistics operations. In this study, a machine learning-based monitoring system is developed to monitorthe shelf lifeof perishable goods, with a specific focus on mango fruit. The system utilizes LSTM to predictmango ripeness and decision tree algorithms to recommendfruit ripeness. The objective is to provide producers with recommendations that optimize the logistics process for high-quality mangoes and meet theconsumer demands for quality fruit.The implementation of a machine learning-basedlogistics monitoring and recommendation systemcan provide significant benefits tomango producers. Usingadvanced technologies,such as LSTM and Decision Tree algorithms, producers can optimize their logistics operations, improve fruit quality, reduce waste,and improvecustomer satisfaction Creator An entity primarily responsible for making the resource Buyung Achmad Hardiansyah1, Heru Sukoco2, Sony Hartono Wijaya Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5226/904 Publisher An entity responsible for making the resource available Departmentof Computer Science, IPB University, Bogor, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 19-02-2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W 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 https://repository.horizon.ac.id/files/original/bfd20d7bf4fbb45618c5e3c077aa7ac7.pdf fc31eb56ea052ef47bfdcbee88a10df1 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 8 NO 1 (2024) 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 Development of an Early Warning System Using Social Media for Flood Disaster Subject The topic of the resource lood monitoring; water level measurement; thing speakAPI; user-friendly environment; field testing Description An account of the resource his research paper introduces an innovative prototype system that utilizes IoT technologies for monitoring floodwater levels. The integration of an ultrasonic sensor, ESP8266 microcontroller, Arduino IDE, and the ThingSpeak platform aims to establish a robust flood monitoring solution. The paper provides a thorough exploration of the system's background, the problem it addresses, the methodology employed, and the obtained results, along with insights into future research directions. The studymeticulously outlines the design, implementation, and programming code for data collection and transmission within the system. Through extensive field testing and meticulous data analysis, the paper evaluates the accuracy and effectiveness of the proposed flood monitoring solution. Notably, the research underscores the advantages of IoT, emphasizing real-time data collection, logging, and analysis as essential components for efficient flood management. In addition, the paper elucidates step-by-step instructions for configuring Telegram notifications through the ThingSpeak React app, enhancing the practical applicability of the developed system. The research effectively highlights the potential of IoT in flood monitoring, showcasing its superior accuracy and effectiveness compared to traditional methods. By demonstrating the feasibility and advantages of IoT in the context of flood monitoring, this study contributes valuable insights, enriching existing knowledge and paving theway for future advancements in the field. The research encourages continued exploration of advanced techniques to strengthen flood monitoring and management strategies. Ultimately, this work presents a comprehensive IoT-based prototype for floodwater monitoring, offering invaluable insights and fostering the promising role of IoT technologies in this critical domain Creator An entity primarily responsible for making the resource IKetut Kasta Arya Wijaya1,Ruben Cornelius Siagian2 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5087/909 Publisher An entity responsible for making the resource available Universitas Warmadewa, Denpasar, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 22-02-2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W 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