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primary topic: Multivariate LSTM-Based Prediction of Quail Egg Production for Intelligent Decision Support (Center, Bold, Times New Roman 14, maximum 15 words in english)

Multivariate LSTM-Based Prediction of Quail Egg Production for Intelligent Decision Support (Center, Bold, Times New Roman 14, maximum 15 words in english)
type: Thesis
type: Article
abstract: Multivariate LSTM-Based Prediction of Quail Egg Production for Intelligent Decision Support (Center, Bold, Times New Roman 14, maximum 15 words in english) Fisrt author1 , Second author2 , Third author3 , Fourth author4* 1,2 Study Program, Faculty, University Bogor, West Java, 16143, Indonesia 3 Department of Computer Science, Faculty of Mathematics and Natural Science, PakuanUniversity, Bogor, West Java, 16143, Indonesia 4 Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia,81310 Johor Bahru, Johor, Malaysia Abstract Abstract 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 according to the Smart Village framework, small-scale farmers frequently encounter this challenge. In this study, an Intelligent Decision Support System (IDSS) is proposed for the purpose of smart livestock production forecasting. The quail egg production is used as an example of a rural case with representative characteristics. For the purpose of modeling multivariate time-series data, such as historical production, temperature, and humidity, a neural network with Long Short-Term Memory (LSTM) is utilized. The methodology utilized in this study is known as Knowledge Discovery in Databases (KDD), and it encompasses the following stages: data selection, preprocessing, transformation, data mining, and evaluation. The model was developed with data from Bogor Regency between the years 2021 and 2025 regarding the production of quail eggs. The evaluation of the model using RMSE, MAPE, and R2 demonstrates a high level of prediction accuracy, with MAPE being less than 10% and R2 being closer to 1. In order to facilitate real-time forecasting and provide support for data-driven livestock management inside Smart Village ecosystems, the model that performed the best was implemented in…
date: 2025-10-16
status: published
author list: authors
type: EPrint
type: ThesisEPrint
is part of: repository
subject: Ilmu Komputer

Universitas Pakuan
type: Organization

Fakultas Matematika dan Pengetahuan Alam, Universitas Pakuan
type: Organization

Fajar Delli W.
type: Person
given name: Fajar
family name: Delli W.

Eneng Tita Tosida
type: Person
given name: Eneng
family name: Tita Tosida

Siti Halimah
type: Person
given name: Siti
family name: Halimah

Fakultas Matematika dan Ilmu Pnegetahuan Alam
type: Organization

Program Studi Ilmu Komputer
type: Organization

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primary topic: Multivariate LSTM-Based Prediction of Quail Egg Production for Intelligent Decision Support (Center, Bold, Times New Roman 14, maximum 15 words in english)

Ilmu Komputer
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