1 10 20 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/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/e132fa84b9f3fa52a88230a16d11d393.pdf 002a37f7e6aac46d1773315c5dc31e5e 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 Comparison of Machine Learning Algorithms in Detecting Tea LeafDiseases Subject The topic of the resource comparison;machine learning;disease detection;tea leaves Description An account of the resource Tea is one of the top ten most exported products sent from Indonesia toforeign countries. However, in recent years, the amount of tea leaf exports from Indonesia has decreased, even though the export value impacts the country’s economic structure. Besides market competition, Indonesia needs to maintain tea leaf production sothat the spike in export decline is not significant or even increases the export production of tea leaves. To improve the quality of production and reduce production costs, early detection of tea leaf diseases is necessary. This study aims to classify tealeaf images for early detection of tea leaf disease so that appropriate treatment can be carried out early on. This study compares Machine Learning algorithms to determine the best algorithm for detecting tea leaf diseases. The algorithms tested as performance comparisons in classifying the tea leaf diseases are Random Forest (RF), Support Vector Classifier (SVC), Extra Tree Classifier (ETC), Decision Tree (DT), XGBoost Classifier (XGB) and Convolutional Neural algorithms. Network (CNN). As a result, the average accuracy performance generated by ETC produces a higher value than other algorithms, i.e.,getting an average accuracy performance of 77.47%. Another algorithm, i.e.,SVC, has an average accuracy of 76.57%, RF of 76.12%, DT of 65.31%, XGB of 71.62%,and the lowest is CNN of 59.08%. ETC is proven to be the most superior Machine Learning algorithm for detecting tea leaf diseases in this study. Creator An entity primarily responsible for making the resource Candra Nur Ihsan1, Nova Agustina2,Muchammad Naseer3, Harya Gusdevi4,Jack Febrian Rusdi5, Ari Hadhiwibowo6, Fahmi Abdullah Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5587/901 Publisher An entity responsible for making the resource available Department of Informatics, Sekolah Tinggi Teknologi Bandung,Bandung,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/94fba457f862583e7611c1aad6131b79.pdf e7ad120e81475bd84537d682fac116f6 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 Anatomy Identification of Bamboo Stems with The Convolutional Neural Networks (CNN) Method Subject The topic of the resource asinabu; convolutional neural network; identify bamboo; macroscopic images Description An account of the resource Bamboo is important to note that some species of bamboo are protected and considered endangered. However, distinguishing between traded and protected bamboo species or differentiating between bamboo species for various purposes is still a challenge. This requires specialized skills to identify the type of bamboo, and currently, the process can only be carried out in the forest for bamboo that is still in clump form by experienced researchers or officers. However, a study has been conducted to develop an easier and quicker method for identifying bamboo species. The study aims to create an automatic identification system for bamboo stems based on their anatomical structure (ASINABU). The bamboo identification algorithm was developed using macroscopic images of cross-sectioned bamboo stems, and the research method used was the Convolutional Neural Network (CNN). The CNN was designed to identify bamboo species with images taken using a cellphone camera equipped with a lens. The final product is an automatic identification application on Android, which can accurately detect bamboo species with an accuracy of 99.9%. Creator An entity primarily responsible for making the resource Dede Rustandi1, Sony Hartono Wijaya2, Mushthofa3, Ratih Damayanti Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5370/893 Publisher An entity responsible for making the resource available Computer Science Study Program, Faculty of Mathematics and Natural Sciences, IPB University, Bogor, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource XX-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/9a192061006befcda05a182836949b1f.pdf 7476f83fe0fe0b695dadb2f88d03d928 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 Indonesian Crude Oil Price (ICP) Prediction Using Support Vector Regression Algorithm Subject The topic of the resource ICP;prediction;SVR;RMSE;,MAPE Description An account of the resource Indonesian crude oil prices (ICP) experience fluctuating movements, influenced by several factors and other conditions that make ICP prices difficult to predict. ICP priceprediction can be done with the Support Vector Regression (SVR) method. The information utilized originates from the Ministry of Energy and Mineral Resources' official website, specifically focusing oncrude oil pricing data for six primary types of crudeoil: SLC, Attaka, Duri, Belida, Banyuand SC. The data applied covers the time framefrom January 2018 to August 2023. The forecast of the ICP relies on the dateBrent variable and the Alpha factor through the useof support vector regression(SVR. In the case of alinearkernel, the parameters(epsilon)and C (cost)aredetermined using theGrid Search algorithm. In the Dated-Brent variable, the best parameter value is obtained with the value of C = 100 and = 1 while for the Alpha variable, the best parameter value for the SLC crude oil type is C= 0.01 and = 0.01, SC value C = 10 and = 1, Banyu value C = 100 and = 0.1, Banyu value C = 100 and = 0.1, Belida value C = 0.01 and = 0.1, Attaka value C = 0.1 and = 0.01 and Duri value C = 1 and = 1.TheAlpha value of the main crude oil type is the Duri crude oil type with the lowest RMSE value of 0.9651. The MAPE value for SC crude oil type = 19.55% and Duri = 19.46% is in the good category. The R2 value for Banyu crude oil = 0.60610, SC = 0.42717 and Duri = 0.50421 is in the good categoryand the MAPE value for Dated-Brent of49.73% is included in the faircategory Creator An entity primarily responsible for making the resource Des Suryani1, Mutia Fadhilla2 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5551/900 Publisher An entity responsible for making the resource available nformatics Engineering, Islamic University of RiauPekanbaru, 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/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/089c5d332c17d5f42462e9c3456131d2.pdf 62ed0d6c4c085e87c2a6f751a59de804 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) Still Image A static visual representation. Examples include paintings, drawings, graphic designs, plans and maps. Recommended best practice is to assign the type Text to images of textual materials. 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 Kmeans-SMOTE Integration forHandlingImbalance DatainClassifying Financial Distress Companiesusing SVM and Naïve Bayes Subject The topic of the resource K-means-SMOTE;Data Imbalance;Financial Distress Description An account of the resource mbalanced data presents significant challenges in machine learning, leading to biased classification outcomes favouringthe majority class. This issue is especially pronounced in financial distress classification, where data imbalance is common due to the scarcity of such instances in real-world datasets. This study aims to mitigate data imbalance in financial distress companies using the Kmeans-SMOTE method approach by combining K-meansclustering and the Synthetic Minority Oversampling Technique (SMOTE). Various classification approaches, including Naïve Bayes and Support Vector Machine (SVM),are implemented on a financial distress dataset from Kaggle to evaluate the effectiveness of Kmeans-SMOTE. Experimental results show that SVM outperforms Naïve Bayes with impressive accuracy (99.1%), f1-score (99.1%),Area Under Precision-Recall (AUPRC) (99.1%), and Geometric-mean (Gmean) (98.1%). Based on these results, Kmeans-SMOTE canbalance the data effectively, leading to a quite significant improvement in performance Creator An entity primarily responsible for making the resource Didit Johar Maulana1, Siti Saadah2, Prasti Eko Yunanto Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5140/892 Publisher An entity responsible for making the resource available Departmentof Informatics, Informatics, Telkom University, Bandung, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 07-02-2024 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/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 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/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