1 10 11 https://repository.horizon.ac.id/files/original/e55a65fd6e9d07835397741cfc5b309d.pdf 47fc4723f372c8f3374df034bad448c2 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 4 (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 Effect of Resampling Techniques on Model Performance Classification of Maternal Health Risks Subject The topic of the resource class imbalance;resampling methods;classification algorithms;maternal health;prediction accuracy;machine learning Description An account of the resource Indonesia's maternal mortality rate was the second highest in ASEAN, reflecting the problem of class imbalance in maternal health data. This research aimed to improve prediction accuracy in the classification of pregnant women's diseases through the application of various resampling methods. The methods used in this research included Synthetic Minority Over-sampling Technique (SMOTE), SMOTE-Edited Nearest Neighbor (SMOTE-ENN), Adaptive Synthetic Sampling (ADASYN), and ADASYN-ENN, using five classification algorithms: Decision Tree, K-Nearest Neighbor (KNN), Naïve Bayes, Random Forest, and Support Vector Machine (SVM). Performance evaluation was carried out using accuracy, precision, recall, and F1-score metrics to determine the best method and algorithm. The results showed that the SMOTE-ENN and ADASYN-ENN methods significantly improved themodel'sperformance in predicting maternal disease. Random Forest and Decision Tree algorithms showed the best results in terms of accuracy and consistency. These findings provided practical guidance for the application of resampling techniques in the classification of pregnant women's health data, which could contribute to improving the quality of maternal health services in Indonesia Creator An entity primarily responsible for making the resource Nia Mauliza1*, Aisha Shakila Iedwan2,Yoga Pristyanto3, Anggit Dwi Hartanto4,Arif Nur Rohman Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5934/955 Publisher An entity responsible for making the resource available Department of Information Systems, Faculty of Computer Science, Amikom Yogyakarta University, Yogyakarta, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 19-08-2024 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/338b66e2a86d81feef750186904f67a5.pdf c57a23e08bff1563721c0829f0392d87 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 4 (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 Comparative Analysis of Gradient Descent Learning Algorithms in Artificial Neural Networks for Forecasting Indonesian Rice Prices Subject The topic of the resource artificial neural network;backpropagation;learning function;accuracy Description An account of the resource Artificial Neural Networks (ANN) are a field of computer science that mimics the way the human brain processes data. ANNs can be used to classify, estimate, predict, or simulate new data from similar sources. The commonly used algorithm for prediction in ANN is Backpropagation, which yields high accuracy but tends to be slow during the training process and is prone to local minima. To address these issues, appropriate parameters are needed in the Backpropagation training process, such as an optimal learning function. The aim of this study is to evaluate and compare various learning functions within the Backpropagation algorithm to determine the best one for prediction cases. The learning functions evaluated include Gradient Descent Backpropagation (traingd), Gradient Descent with Adaptive Learning Rate (traingda), and Gradient Descent with Momentum and Adaptive Learning Rate (traingdx). The dataset used is the average wholesale rice price in Indonesia, obtained from the Central Statistics Agency (BPS) website. The evaluation results show that the traingdx learning function with a 5-5-1 architecture model achieves the highest accuracy of 83.33%, representing an 8.3% improvement over the traingd and traingda learning functions, which both achieved a maximum accuracy of 75%. Based on this study, it can be concluded that using various learning functions in Backpropagation yieldsbetter accuracy compared to standard Backpropagation Creator An entity primarily responsible for making the resource Rica Ramadana1, Agus Perdana Windarto2*, Dedi Suhendro Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5822/949 Publisher An entity responsible for making the resource available Information Systems Study Program, STIKOM Tunas Bangsa, Pematangsiantar, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 07-08-2024 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/98d80bb58e533574cf25990cfb2b58a9.pdf 833928b1cd4ebc8a7e2570358d6d0865 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 4 (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 of Sulawesi Earthquake Data from 2019 to 2023 using DBSCAN Clustering Subject The topic of the resource clustering; DBSCAN; earthquake; Sulawesi; seismic gap Description An account of the resource Sulawesi is a region in Indonesia known for its significant seismic activity, and its history of impactful earthquakes makes it an area of crucial importance for in-depth analysis. This study analyses earthquake occurrence data in the Sulawesi region from 2019 to 2023 using clustering methods with the DBSCAN algorithm. The utilization of the DBSCAN algorithm was chosen for its ability to cluster data based on spatial density, well-suited for analyzing the spatial patterns of earthquakes. DBSCAN is known for its effectiveness in identifying spatial clusters, especially in handling data with undefined density patterns. The primary aim of this research is to identify spatial earthquake occurrence patterns, classify regions with similar earthquake occurrence rates, describe the characteristics of the resulting spatial clusters, and identify seismic gap areas. The results of analysis and clustering using the DBSCAN algorithm have identified clusters with earthquake depth characteristics, which are expected to make a significant contribution to mapping and understanding earthquake vulnerability and distribution in this region. These findings can aid in more effective disaster mitigation planning, support sustainable development efforts, and enhance earthquake preparedness and response in Sulawesi. This study contributes to a better understanding of earthquake patterns and potential seismic gaps in Sulawesi, which is crucial for developing improved risk mitigation strategies and supporting sustainable development policies. Creator An entity primarily responsible for making the resource Ody Octora Wijaya1*, Rushendra Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5819/948 Publisher An entity responsible for making the resource available Informatics, Faculty of Computer Science, Universitas Mercu Buana, Jakarta, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 04-08-2024 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/153997dcfe6676427783018bcb88d0fb.pdf 7bf4eda47308c53a134095241c2571f8 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 4 (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 Advanced Earthquake Magnitude Prediction Using Regression and Convolutional Recurrent Neural Networks Subject The topic of the resource magnitude prediction; CRNN; regression techniques; seismic data analysis; machine learning Description An account of the resource Earthquake magnitude prediction is critical in seismology, with significant implications for disaster risk management and mitigation. This study presents a novel earthquake magnitude prediction model by integrating regression analysis with Convolutional Recurrent Neural Networks (CRNNs). It utilisesConvolutional Neural Networks (CNNs) for spatial feature extraction from 2-dimensional seismic signal images and Long Short-Term Memory (LSTM) networks to capture temporal dependencies. The innovative model architecture incorporates residual connections and specialisedregression techniques for sequential data. Validated against a comprehensive seismic dataset, the model achieves a Mean Squared Error (MSE) of 0.1909 and a Root Mean Squared Error (RMSE) of 0.4369, with a coefficient of determination of 0.79772. These metrics, alongside a correlation coefficient of 0.8980, demonstrate the model's accuracy andconsistency in predicting earthquake magnitudes, establishing its potential for enhancing seismic risk assessment and informing early warning systems Creator An entity primarily responsible for making the resource Asep Id Hadiana1*, Rifaz Muhammad Sukma2, Eddie Krishna Putra3 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5922/965 Publisher An entity responsible for making the resource available Departmentof Informatics, Facultyof Science and Informatics, Universitas Jenderal Achmad Yani, Cimahi, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 29-08-2024 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/2f8faf5ed152d47a6ed3d39af826b252.pdf 46a443a33ee4d09c78f552103584f00e 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 4 (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 Increasing the Accuracy of Brain Stroke Classification using Random Forest Algorithm with Mutual Information Feature Selection Subject The topic of the resource random forest; strokes; brain; mutual information; features Description An account of the resource Brain stroke stands out as a leading cause of death, distinguishing it from common illnesses and highlighting the critical need to utilize machine learning techniques to identify symptoms. Among these techniques, the Random Forest (RF) algorithm emerged as the main candidate because of its optimal accuracy values. RFwas chosen for its ensemble learning properties that optimize accuracy while simultaneously,bagging all outputs (DT), thus increasing its efficacy. Feature Selection, an important data analysis step, which is mainly achieved through pre-processing, aims to identify influential features and ignore less impactful features. Mutual Information serves as an important feature selection method. Specifically, the highest level of accuracy was achieved by cross-validating the test data -10, resulting in 0.7760 without feature selection and 0.7790 with mutual information. Most of the attributes in the brain stroke dataset show relevance to the stroke disease class, but the resulting decision tree shows age as a particularly important node. So,the researchresults show that the selection feature (Mutual Information) can increase the accuracy of brain stroke classification, although it is not significant, namely an increase of 0.0030%.With an increase, where there is no significant difference, it can be said that almost all the attributes contained in the brain stroke dataset used have an influence on their relevance to the stroke disease class Creator An entity primarily responsible for making the resource Fachruddin1*, Errissya Rasywir2, Yovi Pratama Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5795/962 Publisher An entity responsible for making the resource available Information Systems Department, Computer Science Faculty, Dinamika Bangsa University, Jambi, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 28-08-2024 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/fb9bc71192a5569138198716dc004655.pdf 3f486448b8d11859c7fd065eefb16e31 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 4 (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 Comparing Correlation-Based Feature Selection and Symmetrical Uncertainty for Student Dropout Prediction Subject The topic of the resource educational data mining; performance evaluation; correlation-based feature selection; symmetrical uncertainty Description An account of the resource Predicting student dropout is essential for universities dealing with high attrition rates.This study compares two feature selection (FS) methods—correlation-based feature selection (CFS) and symmetrical uncertainty (SU)—in educational data mining for dropout prediction. We evaluate these methods using three classification algorithms: decision tree (DT), support vector machine(SVM), and naive Bayes(NB). Results show that SU outperforms CFS overall, with SVM achieving the highest accuracy (98.16%) when combined with SUMoreover, this study identifies total credits in the fourth semester, cumulative GPA, gender, and student domicile as key predictors of student dropout.This study shows how using feature selection methods can improve the accuracy of predicting student dropout, helping educational institutions retain students better Creator An entity primarily responsible for making the resource Haryono Setiadi1*, Indah Paksi Larasati2, Esti Suryani3, Dewi Wisnu Wardani4 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5911/959 Publisher An entity responsible for making the resource available Research Group Data Information Knowledge and Engineering, Department of Informatics, Universitas Sebelas Maret, Surakarta, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 26-08-2024 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/5820c3978765e8ff99e7d1af4ee995c2.pdf 6cae9aaf566296e8206fe605d0a10ad3 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 4 (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 waste classification; transfer learning; EfficientNet-B0 Subject The topic of the resource Recycling of wasteis a significant challenge in modern waste management. Conventional techniques that useinductive and capacitive proximity sensors exhibit limitations in accuracy and flexibility for the detection ofvarious types of waste. Indonesia generates approximately 175,000 tons of waste per day, highlighting the urgent need for efficient waste management solutions.Thisstudy develops a waste classification system based on deep learning, leveraging the powerful EfficientNet-B0 model through transfer learning. EfficientNet-B0 is designed with a compound scaling method, which uniformly scales network depth, width, and resolution, providing an optimal balance between accuracy and computational efficiency. The model was trained on a dataset containing six classes of waste—glass, cardboard, paper, metal, plastic, and residue—totalling7014 images. The model was trained using data augmentation and fine-tuning techniques. The training results show a test accuracy of 91.94%, a precision of 92.10%, and a recall of 91.94%, resulting in an F1-score of 91.96%. Visualisationof predictions demonstrates that the model effectively classifies waste in new test data. Implementing this model in the industry can automate the waste sorting process more efficiently and accurately thanmethods based on inductive and capacitive proximity sensors. This study underscores the significant potential of deep learning models, particularly EfficientNet-B0, in industrial waste classification applications and opens opportunities for further integration with sensor and robotic systems for more advanced waste management solutions Description An account of the resource waste classification; transfer learning; EfficientNet-B0 Creator An entity primarily responsible for making the resource Risfendra1, Gheri Febri Ananda2*, Herlin Setyawan Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5875/961 Publisher An entity responsible for making the resource available Departmentof Electrical Engineering, Faculty of Engineering, Universitas Negeri Padang, Padang, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 25-08-2024 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/ee325c2bf6bca0a232623283dcc769cd.pdf 358134c4a704b61f9c460904f211c689 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 4 (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 Twitter Sentiment Analysis TowardsCandidates ofthe 2024 Indonesian Presidential Election Subject The topic of the resource twitter;sentiment analysis;presidential election Description An account of the resource Indonesia will hold general elections in 2024. Long before the elections were held, the topic related to elections was widely discussed on news portals and social media, including Twitter. A fewstudies related to Indonesian election have tried to predict candidates who will run for the presidential election, but there has been no research that examines public sentimenton social mediatowards each of the potential candidates.The main objective of this study is to analyze the public sentiment in Twitter towards potential candidates for the 2024 Indonesian presidential election. This research seeks to fill the gaps in previous research and become a reference for further research regarding the sentiment analysis for election prediction using Twitter.The presidential candidates used in the research are the top 3 candidates based on the Poltracking survey, namely Ganjar Pranowo, Prabowo Subianto, and Anies Baswedan. The dataweretakenfrom Januaryuntil October 2022, more than a year before the general election began. To predict the sentiment, four different machine-learning methods were used and compared to each other. There are Naïve Bayes, Support Vector Machine, Random Forest, and Neural Networks. The result shows that the number of tweets discussing each candidate from Januaryuntil October 2022 has increased over time for each month.Based on the sentiment results of each candidate, the highest sentiment towards Prabowo is neutral(55.49%), the highest sentiment towards Ganjar is positive (61.34%), and the highest sentiment towards Anies is neutral (44.84%). Result from the study also shows thatAnieswas the presidential candidate who received more negative sentiment than the other two (56.63%). Meanwhile, Ganjar Pranowo got the most positive sentiment of all (42,69%). For the neutral sentiment, Anies Baswedan also got the most results (39,87%), followed by Prabowo (38.99%) and Ganjar Pranowo (21.14%). Result of the study also discovers that Random Forest and Neural Networks have the best performance for sentiment analysis. Other than that, experiment from this research also discovered that using a model for each entity can generate sentiment results specific to the candidate being analyzed, rather than sentiment for the tweet as a whole. This show that a model for each entity can give better results thanusing an aggregated model to determine the sentiment of each candidate Creator An entity primarily responsible for making the resource Rhoma Cahyanti1, Desiana Nurul Maftuhah2,Aris Budi Santoso3, Indra Budi Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5839/958 Publisher An entity responsible for making the resource available Faculty of Computer Science, Universitas Indonesia, Depok, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 24-08-2024 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/a18200b45a359a2900a7b69278fffb9c.pdf 66e99fadb1b198412b2db5fe633f43d1 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 4 (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 Classification ofToraja Wood Carving Motif Images Using Convolutional Neural Network (CNN) Subject The topic of the resource CNN; image classification; Torajawood carvings Description An account of the resource Wood carving is a cultural heritage with deep meaning and significance for the Toraja ethnic group's culture. By understanding the meaning of each Toraja carving, both tourists and the local community can gain knowledge about Toraja culture, thereby preserving and maintaining the culture amidst modern developments. Image processing approaches, particularly the development of Convolutional Neural Networks (CNN), offer a solution for extracting information from the diverse and intricate patterns of Toraja wood carvings. This study is highly significant as it implements a deep learning model using the CNN algorithm optimized with the ResNet50 architecture. The methodology in this study involves adjusting the batch size during the model training phase and applying weak-to-strong pixel transformation during the double threshold hysteresis phase in the Canny Feature Extraction process on the edges of Toraja carving images, resulting in ResNet50 architecture accurately recognizing the patterns of Toraja wood carvings. The results demonstrate significant improvements in the performance of the ResNet50 architecture with the preprocessed dataset. average precision, recall, precision, and F1-Score improvements in each Toraja carving class. For the Pa' Lulun Pao class, it was found that the precision and recall values were 0.94, and the F1-Score was 0.94. The Pa’ Somba class also showed good results, with a precision value of 0.9697, a recall of 0.96, and an F1-Score of 0.9648. The Pa’ Tangke Lumu class showed even better results, with a precision value of 0.9898, a recall of 0.97, and an F1-Score of 0.9798. The Pa’ Tumuru class also demonstrated good performance, with a precision value of 0.9327, a recall of 0.97, and an F1-Score of 0.9500. This study not only underscores the effectiveness of processing in enhancing CNN capabilities but also opens opportunities for further research in applying these methods to various image types and exploring different CNN architectures for a more comprehensive understanding Creator An entity primarily responsible for making the resource Nurilmiyanti Wardhani1, Billy Eden William Asrul2*, Antonius Riman Tampang3, Sitti Zuhriyah4, Abdul Latief Arda Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5897/951 Publisher An entity responsible for making the resource available Faculty of Computer Science, Department of Informatic Engineering, Universitas Handayani Makassar, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 07-08-2024 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/11de16949a66de7be86d303c8e76cbed.pdf fb0b6e3b8dd1728cd9c1b5152b1393e1 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 4 (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 Quantum Perceptron: A New Approach for Predicting Rice Prices at the Indonesian Wholesale Trade Leve Subject The topic of the resource prediction; rice price; artificial neural networks; perceptron; quantum computing Description An account of the resource The wholesale rice trade in Indonesia encounters various challenges in forecasting prices. These challenges are influenced by factors such as weather, government policies, global market conditions, and other economic variables. Accurate price predictions are crucial for informing government policy in a timely manner. This research introduces a new approach that utilizes the Quantum Perceptron algorithm to forecast rice prices. The algorithm, an innovative method in quantum computing, is expected to enhance the efficiency and effectiveness of price predictions. Although the research is still in the analytical stage, the use of Quantum Perceptron shows promise in dynamically addressing the complexity of market data and the variability of factors affecting riceprices. The method focuses on developing models that can leverage quantum computing to process information more effectively than classical methods. By harnessing the unique properties of quantum mechanics, such as superposition and entanglement, Quantum Perceptron can identify complex patterns and optimize predictions of future rice prices. The research describes the implementation of quantum algorithms in the context of the Indonesian rice wholesale market, including the technical challenges encountered and future development prospects. The research utilizes quantum computing along with the perceptron algorithm. The researchers focused on analyzing the quantum perceptron algorithm because of the limited availability of quantum computing devices. The findings of this research are confined to analysis. In order to advance this research, the author recommends that future studies employ quantum devices to achieve more accurate predictions. Creator An entity primarily responsible for making the resource Solikhun1*, Tri Yunita Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5869/950 Publisher An entity responsible for making the resource available Informatics Engineering Study Program, STIKOM Tunas Bangsa, Pematang Siantar, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 07-08-2024 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