<> "The repository administrator has not yet configured an RDF license."^^ . <> . . . "Multivariate LSTM-Based Prediction of Quail Egg Production for Intelligent\r\nDecision Support\r\n(Center, Bold, Times New Roman 14, maximum 15 words in english)"^^ . "Multivariate LSTM-Based Prediction of Quail Egg Production for Intelligent\r\nDecision Support\r\n(Center, Bold, Times New Roman 14, maximum 15 words in english)\r\nFisrt author1\r\n, Second author2\r\n, Third author3\r\n, Fourth author4*\r\n1,2 Study Program, Faculty, University Bogor, West Java, 16143, Indonesia\r\n3 Department of Computer Science, Faculty of Mathematics and Natural Science, PakuanUniversity, Bogor, West\r\nJava, 16143, Indonesia\r\n4 Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia,81310 Johor Bahru, Johor,\r\nMalaysia\r\nAbstract\r\nAbstract Small-scale farmers frequently experience production instability due to environmental variability and poor datadriven decision support. Despite the fact that livestock production is vital for rural economic sustainability and food security\r\naccording to the Smart Village framework, small-scale farmers frequently encounter this challenge. In this study, an Intelligent\r\nDecision Support System (IDSS) is proposed for the purpose of smart livestock production forecasting. The quail egg\r\nproduction is used as an example of a rural case with representative characteristics. For the purpose of modeling multivariate\r\ntime-series data, such as historical production, temperature, and humidity, a neural network with Long Short-Term Memory\r\n(LSTM) is utilized. The methodology utilized in this study is known as Knowledge Discovery in Databases (KDD), and it\r\nencompasses the following stages: data selection, preprocessing, transformation, data mining, and evaluation. The model was\r\ndeveloped with data from Bogor Regency between the years 2021 and 2025 regarding the production of quail eggs. The\r\nevaluation of the model using RMSE, MAPE, and R2 demonstrates a high level of prediction accuracy, with MAPE being less\r\nthan 10% and R2 being closer to 1. In order to facilitate real-time forecasting and provide support for data-driven livestock\r\nmanagement inside Smart Village ecosystems, the model that performed the best was implemented in a web-based Flask\r\napplication.\r\nKeywords: Smart Village, Smart Livestock, Intelligent Decision Support System, Long Short-Term Memory (LSTM), Time-Series\r\nForecasting, Data-Driven Agriculture."^^ . "2025-10-16" . . "Universitas Pakuan"^^ . . . "Fakultas Matematika dan Pengetahuan Alam, Universitas Pakuan"^^ . . . . . . . . . . . "Fajar"^^ . "Delli W."^^ . "Fajar Delli W."^^ . . "Eneng"^^ . "Tita Tosida"^^ . "Eneng Tita Tosida"^^ . . "Siti"^^ . "Halimah"^^ . "Siti Halimah"^^ . . "Universitas Pakuan"^^ . . . "Fakultas Matematika dan Ilmu Pnegetahuan Alam"^^ . . . "Program Studi Ilmu Komputer"^^ . . . . . . "HTML Summary of #10658 \n\nMultivariate LSTM-Based Prediction of Quail Egg Production for Intelligent \nDecision Support \n(Center, Bold, Times New Roman 14, maximum 15 words in english)\n\n" . "text/html" . . . "Ilmu Komputer"@en . .