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All Journal Brahmana : Jurnal Penerapan Kecerdasan Buatan Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Muthia Farida
Universitas Islam Kalimantan Muhammad Arsyad Al Banjari Banjarmasin, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering

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Analisis Penerapan Neural Network dalam Memprediksi Produksi Bijih Nikel di Indonesia Muhammad Edya Rosadi; Dian Agustini; Muthia Farida; Dila Dwi Anjani
Brahmana : Jurnal Penerapan Kecerdasan Buatan Vol 4, No 1 (2022): Edisi Desember
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/brahmana.v4i1.108

Abstract

Nickel ore is one of the exports from the mining subsector. About 72% of the world's nickel resources are found in lateritic nickel deposits, with approximately 15.8% of these deposits located in Indonesia. Nickel is currently one of the most discussed subjects in the world. As an essential component in the creation of batteries for electric vehicles, nickel is pushing changes in energy consumption. Managing nickel ore output in Indonesia is prudent in light of the government's efforts to increase national development, investment, employment, mining downstream, and export demands. To satisfy domestic and international demand, it is essential to examine nickel ore output. Consequently, an investigation is required to forecast nickel ore production. The dataset utilized is from the Central Bureau of Statistics's Publication of Non-Oil and Gas Mining Statistics for 2017-2020. This study employs a backpropagation network with an artificial neural network. The procedure is carried out by separating training data and testing data to choose the most accurate architectural model, which is subsequently utilized as a predictive model. The architectural models to be utilized with Matlab 6.1 are 2-45-1; 2-60-1; 2-75-80-1; 2-85-1; and 2-100-1. From a series of tests, it was determined that the best architectural model was 2-45-1 with a Mean Square Error of 0.00099549, epoch 335, and an accuracy of one hundred percent. This model was then utilized to create predictions
Model Prediksi Kunjungan Wisata: Mengoptimalkan Arsitektur Algoritma Backpropagation untuk Prediksi Kunjungan Wisata Mancanegara (ASIA) Mayang Sari; Dian Agustini; Muthia Farida
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 1 (2024): Edisi Januari
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i1.332

Abstract

This research focuses on developing a prediction model for tourist visits for foreign destinations in the Asian region using Backpropagation algorithm architecture optimization. Tourism has become a crucial economic sector in Asia, and accurate tourist arrival predictions have a significant impact on decision making in this industry. The main approach used is the Backpropagation algorithm in the context of artificial neural networks. Although these algorithms have been successful in a variety of applications, optimizing Backpropagation architectures for tourism visit prediction remains a significant challenge. This research aims to improve model accuracy and performance by adjusting the Backpropagation algorithm architecture. Through careful optimization, this research seeks to overcome these complex dynamics and produce a model that can provide more accurate estimates of tourist visits. This research presents predictions of foreign tourist visits to Indonesia by optimizing the artificial neural network architecture using the Backpropagation algorithm. Focusing on visit data from various nationalities in the period 2018-2024, the test results highlight the performance variations between architectures in 2023 and 2024. Prediction results show that the 4-3-7-1 architecture obtains high test accuracy in 2023 (88%) , but will decrease in 2024 to 74%. The 4-5-1 architecture showed good consistency with test accuracy remaining high in both years (92%). These findings provide valuable insights for optimal architectural selection in predicting future tourist visits and identifying changing patterns of trends at the national level. However, it should be noted that these results are projective and may be influenced by external factors that may change, requiring ongoing evaluation to ensure model accuracy and responsiveness.
Co-Authors Dian Agustini Dila Dwi Anjani Mayang Sari Muhammad Edya Rosadi