1 10 26 https://repository.horizon.ac.id/files/original/8fad5a552c09969c157edd795c7eb8b6.pdf 7388924759e26c146a9539be68d23934 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 7 NO 6 (2023) Contributor An entity responsible for making contributions to the resource Sri Wahyuni Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Forecasting the Magnitude Category Based on The Flores Sea Earthquake Subject The topic of the resource gaussiannb; random forest; support vector machine; earthquake; forecasting Description An account of the resource Earthquakes are a phenomenon that is still a mystery in terms of predicting events, one of which is the magnitude. As technology develops, there are many algorithms that can be used as approaches in earthquake forecasting. In the context of magnitude forecasting, the application of GaussianNB, Random Forest and SVM has the potential to reveal these patterns and relationships in the data. With the six main phases of this research, namely data acquisition, data preprocessing, feature selection, model training, forecasting result evaluation, and performance analysis. From these results we obtain, firstly that the GaussianNB model has a relatively simple and fast method in training its model. However, the weakness lies in the assumption of a Gaussian distribution which may not always suit the complex and diverse characteristics of earthquake data. Based on GaussianNB model, the model accurately predicts magnitude category 1 for 421 observations and magnitude category 2 for 33 observations. Meanwhile, the magnitude 3 and magnitude 4 categories did not produce accurate predictions from the model. Second, Random Forest, this method can increase accuracy and overcome the overfitting problem that occurs when forecasting magnitudes. In contrast to GaussianNB, it tends to result in models with greater complexity and require more time to compute. In our findings, we obtained an MSE value of 0.12 with an R2 score of -0.10, this indicates conditions that are less effective in explaining differences in test data. The third option is SVM, which has both benefits and drawbacks that must be taken into account. The capacity of SVM to separate data that has both linear and non-linear separation is one of its key advantages; nevertheless, the main drawback is that it is sensitive to hyperparameter adjustments. It is clear from the results of the algorithm comparison that SVM has more potential for earthquake forecasting, especially the linear SVM and polynomial SVM model. The accuracy of the standard SVM is 0.587, which indicates relatively low performance. Linear SVM obtained a very high accuracy of 0.998. Meanwhile, Polynomial SVM achieves perfect accuracy of 1.0. while RBF SVM has the same accuracy as standard SVM, namely 0.587. Creator An entity primarily responsible for making the resource Adi Jufriansah, Azmi Khusnani, Sabarudin Saputra, Dedi Suwandi Wahab Source A related resource from which the described resource is derived http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource December 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-0760 Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text Earthquake Forecasting gaussiannb Random Forest Support Vector Machine https://repository.horizon.ac.id/files/original/fe50e127306713c37f67b7b7f0fae08f.pdf 9d48cb1b6587b82ffb2187bd1e064df1 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 7 NO 6 (2023) Contributor An entity responsible for making contributions to the resource Sri Wahyuni Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Linear and Non-Linear Spatio-Temporal Input Selection In Wireless Traffic Networks Prediction using Recurrent Neural Networks Subject The topic of the resource wireless traffic; linear and non-linear; spatio-temporal; recurrent neural network Description An account of the resource For the optimization of computer networks with high bandwidth requirements, wireless network traffic prediction is necessary. Its goal is to reduce maintenance costs and enhance internet services. Feature selection is a major issue in the Multivariate Time Series (MTS) Spatio-temporal modeling. Another problem is the dependency between input features, time-lags, and spatial factor so that an appropriate model is needed. This study aims to provide solutions to two problems. The first is to improve a feature extraction and selection process in Spatio-temporal MTS data for relevant features using Detrended Partial Cross-Correlation Analysis (DPPCA) and non-redundant features associated with linear using Pearson's Correlation (PC) filters and non-linear associations using Symmetrical Uncertainty (SU) and combination of both PCSUF. The second is to develop a Spatio-temporal framework model using Recurrent Neural Networks (RNN) to get a better performance than traditional model. These methods are combined and tested using the dataset of cellular networks with one-hour intervals during November in three locations. Testing the effectiveness of the feature selection technique showed that 27.6% of the total extracted features. The forecasting model with the DPCCA–SU-RNN combination method gets the best performance by having RMSE = 380.7, R 2 = 97%, and MAPE = 10% Creator An entity primarily responsible for making the resource Ahmad Saikhu, Agung Teguh Setyadi, Victor Hariadi Source A related resource from which the described resource is derived http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource December 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-0760 Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text linear and non-linear recurrent neural network spatio-temporal wireless traffic https://repository.horizon.ac.id/files/original/d4f7153d3fe5c840adf4f43be256aaaf.pdf 7cafa90534518c39cfc8ccf10c117e63 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 7 NO 6 (2023) Contributor An entity responsible for making contributions to the resource Sri Wahyuni Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Balinese Script Handwriting Recognition Using Faster R-CNN Subject The topic of the resource balinese script; faster R-CNN; handwriting Description An account of the resource In Balinese culture, the ability to read Balinese script is one of the challenges young generations face. Advances in machine learning have proposed handwriting detection systems using both traditional and deep learning models. However, the traditional approach is usually impractical and is prone to inaccurate identification results. Convolutional Neural Network (CNN)-based models integrate feature extraction and classification into an end-to-end pipeline to increase performance. This research proposes that recognizing characters through an object detection approach makes an end-to-end process of localizing and classifying several characters simultaneously using the Faster R-CNN. Four CNN models, including ResNet-50, ResNet- 101, ResNet-152, and Inception ResNet V2 were tested to detect 28 Balinese characters in a single form covering 18 consonants and 10 digits using Intersection over Union (IoU) thresholds: 0.5 and 0.75. ResNet-50 and Inception ResNet V2 achieve 0.991 mAP at IoU of 0.5, while Inception ResNet V2 excels at IoU of 0.75. Further analysis showed that class “nol” had the lowest Recall due to many undetected ground truths. Meanwhile, class “ba” had the lowest Precision due to its similarity with classes “ga” and “nga”. This research contributes to experimenting with Faster R-CNN in detecting handwritten Balinese scripts. Creator An entity primarily responsible for making the resource Alif Adwitiya Pratama, Mahmud Dwi Sulistiyo, Aditya Firman Ihsan Source A related resource from which the described resource is derived http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource December 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-0760 Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text balinese script Faster R-CNN handwriting https://repository.horizon.ac.id/files/original/4ac6874068c807951883e7332c05f7ce.pdf aa4be149fc6c0b6dd38bffb73a6ee36b Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 7 NO 6 (2023) Contributor An entity responsible for making contributions to the resource Sri Wahyuni Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Forecasting Photovoltaic Output Power Based on Environmental Parameters Using Artificial Neural Network Methods Subject The topic of the resource artificial neural network; forecasting; output power; photovoltaic Description An account of the resource Photovoltaic is a system that can convert sunlight into electrical energy. However, photovoltaic efficiency tends to be low and its performance is affected by several environmental parameters such as dust, wind speed, humidity, temperature and other external factors. Because many factors can affect the power generated, we need a power output prediction system that can assist in planning and managing as well as increasing the efficiency of photovoltaic systems. In this research, a system is designed that can predict the photovoltaic output power in the short term using the Artificial Neural Network method or what is often called an artificial neural network. Predictions are made based on the effects of several environmental parameters such as wind speed, dust, humidity, and temperature on a 10 Wp photovoltaic system. Performance data for 7 days is used as a dataset and then processed using ANN with 1 input layer, 3 hidden layers 1 output layer and 3 sample epochs (10, 100, and 1000). The results of the study can predict the output of photovoltaic power for the next 4 days with an error value of Mean Square Error (MSE) of 0.0010, Mean Absolute Error (MAE) of 0.0155, Root Mean Square Error (RMSE) of 0.0229 with an increase in power reach 0.5 to 1 watt. Creator An entity primarily responsible for making the resource Desri Kristina Silalahi, Agnes Christy Margareth Rumapea, Wahmisari Priharti, Bandiyah Sri Aprillia Source A related resource from which the described resource is derived http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource December 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-0760 Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text Artificial neural network Forecasting output power Photovoltaic https://repository.horizon.ac.id/files/original/4988df508ce695321da583dce742fee4.pdf c1f463ef0dcb50ca7cc1fe0bfed9b9a8 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 7 NO 6 (2023) Contributor An entity responsible for making contributions to the resource Sri Wahyuni Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A Cost-Effective Vital Sign Monitoring System Harnessing Smartwatch for Home Care Patients Subject The topic of the resource home care; national early warning score; vital sign; smartwatch; e-health Description An account of the resource Telemedicine applications enable online health consultation services, but they have limitations: there is no vital sign examination as in offline consultation. Furthermore, vital sign examinations for patients treated outside hospitals, such as outpatients and home care patients, are not as routine as for inpatients. Although patient monitors can be used to monitor vital signs in real-time, they are expensive, making it difficult to use them for home care patients. This research aims to develop a vital sign monitoring system for home care patients that integrates a smartwatch as a data acquisition sensor device, a mobile application as a gateway that collects vital sign data and sends it to a web server, and a web application to remotely monitor vital signs and changes in their values. This system also aims to make it easier for patients and their families to communicate with doctors and nurses while viewing vital sign information and the risk of patient deterioration in the form of Early Warning Score (EWS). The smartwatch chosen is based on its features and low price, so a cost-effective vital sign monitoring system can be produced. Testing using the black-box method shows that the system functions well and meets the expected functionality Creator An entity primarily responsible for making the resource Dodon Turianto Nugrahadi, Rudy Herteno, Mohammad Reza Faisal, Nursyifa Azizah, Friska Abadi, Irwan Budiman, Muhammad Itqan Mazdadi Source A related resource from which the described resource is derived http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource December 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-0760 Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text E-health home care national early warning score; smartwatch vital sign https://repository.horizon.ac.id/files/original/28c93018ca4cb465b3c5a775b77708bb.pdf fbc21f99f677713cdefe4920001ad02f Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 7 NO 6 (2023) Contributor An entity responsible for making contributions to the resource Sri Wahyuni Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Comparison of Segmentation Analysis in Nucleus Detection with GLCM Features using Otsu and Polynomial Methods Subject The topic of the resource pap smear; cervical cancer; nucleus; GLCM otsu; GLCM polynomial Description An account of the resource Pap smear is a digital image generated from the recording of cervical cancer cell preparation. Images generated are susceptible to errors due to the relatively small cell sizes and overlapping cell nuclei. Therefore, accurate Pap smear image analysis is essential to obtain the right information. This research compares nucleus segmentation and detection using Grey Level Co-occurrence Matrix (GLCM) features in two methods: Otsu and Polynomial. The tested data consisted of 400 images sourced from RepoMedUNM, a publicly accessible repository containing 2,346 images. Both methods were compared and evaluated to obtain the most accurate features. The research results showed that the average distance of the Otsu method was 6.6457, which was superior to the Polynomial method with a value of 6.6215. Distance refers to the distance between the nucleus detected by the Otsu and the Polynomial method. Distance is an important measure to assess how closely the detection results align with the actual nucleus positions. It indicates that the Polynomial method produces nucleus detections that are on average closer to the actual nucleus positions compared to the Otsu method. Consequently, this research can serve as a reference for further studies in developing new methods to enhance the accuracy of identification. Creator An entity primarily responsible for making the resource Dwiza Riana, Jufriadif Na'am, Daniati Uki Eka Saputri, Sri Hadianti, Faruq Aziz, Suryadi Putra Liawatimena, Alya Shafra Hewiz, Dika Putri Metalica, , Teguh Herwanto Source A related resource from which the described resource is derived http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource December 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-076 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 cervical cancer GLCM otsu GLCM polynomial nucleus Pap smear https://repository.horizon.ac.id/files/original/1a5570faef424e1701b4ae285a557d25.pdf 120e30397979aa581659146ff6debffd Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 7 NO 6 (2023) Contributor An entity responsible for making contributions to the resource Sri Wahyuni Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource ANoM STEMMER: Nazief & Andriani Modification for Maduresse Stemming Subject The topic of the resource stemming; morphophonemic; understemming; overstemming; madurese language Description An account of the resource Madurese is one of the regional languages in Indonesia. This is a cultural property that needs to be preserved. With various uniqueness and word formation rules, the Madurese language can be used in Information Retrieval, namely stemming. The Madurese language has a close relationship with the Javanese language, in several studies the stemming method is often used, such as the modification of the Nazief & Adriani method which has good performance for the Javanese language, but there has never been any research on the Madurese language and it has not been proven successful. Previous studies also have not used morphophonemic rules that influence word formation in Madurese. Therefore this research was developed by modifying Nazief & Adriani's algorithm for Madurese language based on Madurese language morphology by removing affixes, namely ter-ater (prefix), panoteng (suffix), and morphophonemic rules. Corpus uses 1000 words from the Madurese language dictionary which have received affixes. The accuracy of the algorithm is 89% with 890 words that match, the prefix has an accuracy of 93.81%, the suffix has an accuracy of 83.78% and the confix has 80.07%. As for the overall performance, it produces an accuracy of 89.0% with an error rate of 11%. the understemming is found in 104 words and overstemming in 6 words. the time it takes to compile is 31.31 seconds Creator An entity primarily responsible for making the resource Enni Lindrawati, Ema Utami, Ainul Yaqin Source A related resource from which the described resource is derived http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource December 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-0760 Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text madurese language morphophonemic overstemming Stemming understemming https://repository.horizon.ac.id/files/original/574d3f63493f40c24692f8567a5667f7.pdf e3839b2293c0a7959df6e7955080782b Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 7 NO 6 (2023) Contributor An entity responsible for making contributions to the resource Sri Wahyuni Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Analysis of the SaintekMu Website Quality on User Satisfaction Using the Modified System Usability Scale and Webqual 4.0 Method Subject The topic of the resource website; SaintekMu; usability; system usability scale; webqual Description An account of the resource At present, websites are a major means of finding or providing information. The Saintekmu website was created to offer top�notch service to students. One way to ensure that the website's services are appropriate and that information technology is being used to its fullest potential is to evaluate the level of service provided and improve its quality. This study aims to compare the results of two methods - the System Usability Scale (SUS) and Webqual - used to determine the quality and expectations of website users. The study distributed questionnaires online using Google Forms and had a sample size of 20 students. The data collected were analyzed using the SPSS program. Results from the SUS method indicated that the website's acceptability range was in the Marginal category, with a score of 69.9 and a classification rating of OK. The Webqual method yielded an R Square of 0.948, indicating that the website's usability, quality, and interaction variables had a significant effect on user satisfaction. All WebQual 4.0 dimensions had a positive and significant effect on user satisfaction, both partially and simultaneously. This study provides Muhammadiyah Saintek University with reference material for evaluating their website in the future Creator An entity primarily responsible for making the resource Fitrah Juliansyah, Abdul Fadlil, Rusydi Umar Source A related resource from which the described resource is derived http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource December 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-0760 Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text SaintekMu system usability scale usability webqual website https://repository.horizon.ac.id/files/original/9cea711e436503a2e67d17d0a7912e59.pdf 15599979f6aa565de030a4e152a61107 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 7 NO 6 (2023) Contributor An entity responsible for making contributions to the resource Sri Wahyuni Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Artificial Neural Network Based Prediction Model Back Propagation on Blood Demand and Blood Supply Subject The topic of the resource neural network; backpropagation; blood demand; blood supply Description An account of the resource The balance between blood demand and supply at the Indonesian Red Cross Blood Transfusion Unit (UTD-PMI) is crucial. This condition needs to be maintained to reduce unused or expired blood supplies. Despite the situation at UTD-PMI, where the blood supply exceeds the demand, there is still a shortage of blood when needed by patients. This research aims to model the prediction of blood demand and supply for each blood type using the Back Propagation artificial neural network approach. Data from the last 3 years, from 2020 to 2022, were utilized in this research process. There are three stages in this research process. The first stage involves the training process, using data from January 2020 to December 2021. The testing process utilizes data from January 2021 to December 2022. The prediction process involves displaying forecasted data for the next 12 months from January to December 2023. The accuracy of the calculations is assessed using the Mean Square Error (MSE). Ultimately, the research results present the prediction model for the four blood types regarding blood demand and supply. These findings can serve as a reference for regulating future blood donation activities carried out by the UTD-PMI. Creator An entity primarily responsible for making the resource Frengky Tedy, Patrisius Batarius, Ign. Pricher A. N. Samane, Alfry Aristo Jansen Sinlae Source A related resource from which the described resource is derived http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource December 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-0760 Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text Backpropagation blood demand blood supply neural network https://repository.horizon.ac.id/files/original/1e0a6f05e6799ed66b63d676b199dddf.pdf 5fb4a179683b46f8c784371f3dafac63 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 7 NO 6 (2023) Contributor An entity responsible for making contributions to the resource Sri Wahyuni Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource CNN Method to Identify the Banana Plant Diseases based on Banana Leaf Images by Giving Models of ResNet50 and VGG-19 Subject The topic of the resource banana disease identification; data processing, machine learning; convolutional neural networks Description An account of the resource Identify banana plant diseases using machine learning with the CNN method to make it easier to identify diseases in banana plants through leaf images. It employs the CNN method, incorporating ResNet50 because ResNet50 is one of the best models and a suitable model for the dataset used, and the VGG-19 model is used because VGG-19 was one of the winning models of the 2014 ImageNet Challenge and is a model that also fits the dataset used. The research objectives encompass dataset processing, model architecture development, evaluation, and result reporting, all aimed at enhancing disease identification in banana plants. The ResNet50 model achieved an impressive 94% accuracy, with 88% precision, 91% recall, and an 89% F1- score, while the VGG-19 model demonstrated strong performance with 91% accuracy, surpassing prior research and highlighting the effectiveness of these models in identifying banana plant diseases through leaf images. In conclusion, the ResNet50 model's exceptional accuracy positions it as the preferred model for CNN-based disease identification in banana plants, offering significant advancements and insights for agricultural practices. Future research opportunities include exploring alternative CNN models, architectural variations, and more extensive training datasets to enhance disease identification accuracy Creator An entity primarily responsible for making the resource Ilham Rahmana Syihad, Muhammad Rizal, Zamah Sari, Yufis Azhar Source A related resource from which the described resource is derived http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource December 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-0760 Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text banana disease identification convolutional neural networks data processing machine learning