1 10 20 https://repository.horizon.ac.id/files/original/d19808990bc3ab4d17cb7b47400a3362.pdf ad4f495a4c72ec751e67a713c3410c98 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 Analysis and Development of Eight Deep Learning Architectures for the Classification of Mushrooms Subject The topic of the resource mushroom; deep learning; modified mobilenet; classification Description An account of the resource One food item that is easy to find in nature is the mushroom. In terms of form and features, mushrooms are similar. Arranging mushrooms into groups so that poisonousand non-poisonous ones can be told apart is important. Real-time analysis of mushrooms is still not used very often. Previous studies focused primarily on performance and accuracy, ignoring architectural computing and a significant amount of data preprocessing. The used dataset is more laboratory-conditioned. This will impede the process of widespread implementation. The study suggests changes to eight current architectures: Modified DenseNet201, DenseNet121, VGG16,VGG19, ResNet50, InceptionNetV3, MobileNet, and EfficientNet B1. The development of this architecture took place within the areas of classification and hyperparameter learning. In contrast to the other eight architectures, the MobileNet architecture exhibits the lowest computational performance and highest accuracy, according to the comparison results. By employing the confusion matrix for evaluation, an accuracy of 82.7% is achieved. Modified MobileNet has the best speed because it keeps a lower-computation architecture and cuts down on unnecessary pre-processing. This means that a lot of people can use smartphones with more realistic data conditions to make it work Creator An entity primarily responsible for making the resource Lia Farokhah1, Suastika Yulia Riska Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5498/902 Publisher An entity responsible for making the resource available Informatics, Technology and Design, Institut Teknologi dan Bisnis ASIA, Malang, Indonesi Date A point or period of time associated with an event in the lifecycle of the resource 19-02-2024 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/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/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/133ee9b55b7bde6afd5df4402f6c6a2d.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 nvestigating the Impact of ReLUand Sigmoid Activation Functions on Animal Classification UsingCNN Models Subject The topic of the resource convolutional neural network; activation function; sigmoid; relu; classification;images Description An account of the resource VGG16 is a convolutional neural network model used for image recognition. It is unique in that it only has 16 weighted layers, rather than relying on a large number of hyperparameters.It is considered one of the best vision model architectures. However, several things needto be improved to increase the accuracy of image recognition. In this context, this work proposes and investigates two ensemble CNNs using transfer learning and compares them with state-of-the-art CNN architectures. This studycompares the performance of (rectified linear unit) ReLUand sigmoidactivation functions on CNN models for animal classification. To choose which model to use, we tested twostate-of-the-art CNN architectures: the default VGG16 with the proposed method VGG16. A dataset consisting of 2,000 images of five different animals was used. The results show that ReLU achieves ahigher classification accuracy than sigmoid. The model with ReLU infully connected and convolutionallayers achieved the highest precisionof 97.56% inthe test dataset.The research aims to findbetter activation functions and identify factors that influence model performance. The datasetconsists of animal images collected from Kaggle, including cats, cows, elephants, horses, and sheep. It is divided into training setsand test sets (ratio 80:20). The CNN model has two convolution layers and two fully connected layers. ReLU and sigmoid activation functions with different learning rates are used. Evaluation metrics include accuracy, precision, recall, F1 score, and test cost. ReLU outperforms sigmoid in accuracy, precision, recall, and F1 score.This study emphasizesthe importance of choosing the right activation function for better classification accuracy. ReLU is identified as effective in solving the vanish-gradientproblem. These findings can guide future research to improve CNN models in animal classification Creator An entity primarily responsible for making the resource Mesran1, Sitti Rachmawati Yahya2, Fifto Nugroho3, Agus Perdana Windarto Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5367/898 Publisher An entity responsible for making the resource available Universitas Budi Darma, Medan, Indonesia2Universitas SiberAsia (UNSIA), Jakarta, Indonesia3Universitas Bung Karno, Jakarta, Indonesia4STIKOM Tunas Bangsa, Pematangsiantar, 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/3a40d786af445f3245d2d0d0c53355ed.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 A Novel Framework for Information Security During the SDLC Implementation Stage: A Systematic Literature Review Subject The topic of the resource information security;implementation;system development life cycle (SDLC);secure software development life cycle (SSDLC Description An account of the resource This research delves into the critical aspects of information security during the implementationstage of the Software Development Life Cycle (SDLC). Usinga systematic review of the literature,the study synthesizes thefindings ofvarious digital repositories, including IEEE Xplore, ACM Digital Library, Scopus, and ScienceDirect, to outline a comprehensive framework that addressestheunique security challenges of the implementation stage.This research contributes to the field by proposing a novel assurance model for software development vendors, focusing on improvinginformation security measures during the implementation stage. The study's findings reveal 12 key steps organizationscan adopt to mitigate security risks and improveinformation security measures during this critical phase. These steps provide actionable insights and strategies designedto supportsecurity protocols effectively. The paper concludes that by incorporatingthese steps, organizationscan significantly improve their security posture, ensuring the integrity and reliability of the software development process, particularly during the implementation stage. This approach not only addressesimmediate security concerns but also sets a precedent for future research and practice in secure software development, particularly in the critical implementation stage of the SDLC Creator An entity primarily responsible for making the resource Mikael Octavinus Chan1, Setiadi Yazid Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5403/896 Publisher An entity responsible for making the resource available Information Technology, Computer Science, Universitas Indonesia, Jakarta, 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/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/a9a08ddd31ab6bbc80d9634d4e642802.pdf f8d172d033735d8b1b694c484befb2e6 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 Rotation Double Random ForestAlgorithm to Predict The Food Insecurity Status of Households Subject The topic of the resource rotation double random forest;easyensemble;SMOTE-NC; food insecurity Description An account of the resource he ensemble tree method has been proven to handle classification problems well. The strength of the ensemble tree technique lies in the diversity and independence between each tree. Increasing the diversity of mutually independent decision trees improves model performance. Various studies propose the development of ensemble tree-based models by forming algorithms that create decision trees that are formed independently of each other and have various inputs. These include random forest (RF), rotation forest (RoF), double random forest (DRF), and the latest is rotation double random forest (RoDRF). RoDRF rotates or transforms data intending to produce better diversity among the learner base. RoDRF applies the variable rotation concept to trees based on the DRF algorithm. Random rotations or transformations on different feature subspaces produce different projections, leading to better generalization or prediction performance. This research aims to compare the performance of RoDRF with RF, RoF, and DRF models on imbalanced data in cases of food insecurity. Class imbalance will be handled with two methods, namely EasyEnsemble and SMOTE-NC. The research results show that the DRF's model with EasyEnsemble techniques produces a model with the best performance among several algorithms tested. Even though the resulting accuracy is 0.62274 and the AUC value is 0.68501, the model can predict each class equally. All algorithms with EasyEnsemble treatment have average AUC values significantly different from each other based on statistical test results. This research alsoused SHAP to explain variables significantly contributing to the household's food insecurity status model Creator An entity primarily responsible for making the resource Rais1, Agus Mohamad Soleh2, Budi Susetyo Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5540/890 Publisher An entity responsible for making the resource available Department of Statistics, Faculty of Mathematics and Science, IPB University, Bogor, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 30-01-2024 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/c9a3c9b2a44484bc1292cdf674f7be88.pdf 4f030abb994ab144ceaa7ca0b62d2c53 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 MRI Image Based Alzheimer’s Disease Classification Using Convolutional Neural Network: EfficientNet Architecture Subject The topic of the resource alzheimer's disease; convolutional neural network; efficientnet-B0; efficientnet-B3 Description An account of the resource Alzheimer's disease is a neurodegenerative disorder or a condition characterized by the degeneration and damage of the nervous system. This leads to a decline in cognitive abilities such as memory, thinking, and focus, which can impact daily activities. In the medical field, a technology called Magnetic Resonance Imaging (MRI) can be used for the initial diagnosis of Alzheimer's disease through image procedures-basedrecognition methods. The development of this detection system aims to assist medical professionals, including doctors and radiologists, in diagnosing, treating, and monitoring patients with Alzheimer's disease. This study also aims to classify different types of Alzheimer's disease into four distinct classes utilizing the ConvolutionalNeural Network method with the EfficientNet-B0 and EfficientNet-B3 architectures. This study utilized 6400 images that encompass four classes, namely Mild Demented, Moderate Demented, Non Demented, and Very Mild Demented. After conducting testing for bothscenarios, the Exactness outcomes for scenario 1 utilizing EfficientNet-B0 reveryed 96.00%, and for scenario 2 utilizing EfficientNet-B3, the Exactness was 97.00% Creator An entity primarily responsible for making the resource Novia Adelia Ujilast1, Nuris Sabila Firdausita2, Christian Sri KusumaAditya3*, Yufis Azhar Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5457/888 Publisher An entity responsible for making the resource available Departement of Informatics, Faculty of Engineering, Muhammadiyah MalangUniversity, Malang, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 18-01-2024 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/3be91f8a4590cb011028a7d270455310.pdf 4f030abb994ab144ceaa7ca0b62d2c53 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 Convolutional Neural Networks for Classification Motives and the Effect of Image Dimensions Subject The topic of the resource Image Processing, Indonesian Batik Motifs, Convolutional Neural Networks Description An account of the resource Although Indonesian batik patterns vary by location, they usually depict local customs and cultures. Each batik has a unique quality and,to correctlyidentify the batik designs, you need to understand the design patterns. However,many people struggle to identify and categorizethese kinds of motivationbecause they don't have the requisite knowledge, understanding, or access to sufficient information. This study used photo data to classify batikpatterns into 15different groups. Batik Kawung, Megamendung, Lasem, Pole, Machete, Gills, Nutmeg, Karaswasih, Cendrawasih, Geblek Renteng, Bali, Betawi, and Dayak are all included in this category. 1,350 images were used in the research. Google supports the collection of data. Toprovide the highest level ofprecisionand to evaluate how image dimensions affect the classification of batik designs, this study employs convolutional neural networks(CNNs). The results of this study show that Multi-Layer Perceptron (MLP) is a well-liked deep learning method for data classification, especially in domains where picture classification is involved. The size of the images utilizedaffects the accuracy ofcomputational neural network(CNN)algorithms. The resultsshowed that the testusing training datacomparisons of60%, 30%and 10%resulted in a 01.89% loss of 1.18%and a 100% improvement in accuracy Creator An entity primarily responsible for making the resource Siti Aisyah1, Rini Astuti2, Fadhil M Basysyar3 Odi Nurdiawan4, Irfan AlI Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5623/910 Publisher An entity responsible for making the resource available Teknik Informatika, STMIKIKMI Cirebon, Cirebon, Indonesia2Sistem Informasi STMIK LIKMI Bandung, Bandung, Indonesia3Sistem Informasi STMIK IKMI Cirebon, Cirebon, Indonesia4Manajemen Informatika STMIK IKMI Cirebon, Cirebon, Indonesia5Rekayasa Perangkat Lunak STMIK IKMI Cirebon, Cirebon, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 22-02-2024 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