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All Journal Building of Informatics, Technology and Science Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika)
Ramadhani, Cerah Fitri
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Computer Science & IT Decision Sciences, Operations Research & Management

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Penerapan Pemilihan Model Arsitektur Terbaik pada Neural Network pada Prediksi Jumlah Siswa SD di Kecamatan Siantar Barat Ramadhani, Cerah Fitri; Siregar, Muhammad Noor Hasan; Rahadjeng, Indra Riyana; Windarto, Agus Perdana
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2676

Abstract

The use of the artificial neural network (Backpropagation) method can be used in determining the best architectural model for predicting the number of elementary school students in the Siantar Barat District. The dataset used is a dataset on the number of Elementary School (SD) students in West Siantar District, Pematang Siantar City in 2017-2021 obtained from the Website of the Ministry of Education, Culture, Research and Technology of the Republic of Indonesia (https://dapo.kemdikbud.go.id /pd/3/076303). The dataset is then divided into 2 parts, namely the training and testing dataset. In the training datasets, attribute X1 is a dataset for 2017, X2 is the dataset for 2018, X3 is a dataset for 2019, and attribute Y (target) is the dataset for 2020. For the test datasets, attribute X1 is the dataset for 2018, attribute X2 is a dataset for 2019, attribute X3 is a dataset for 2020 and attribute Y (target) is a dataset for 2021. The results obtained from the analysis of the Backpropagation and virtualization methods using the MatLab application can be generated with a valid dataset and produce an accuracy rate of 87.5% in architectural models 3-9-1. So that the Backpropagation method can be used as a prediction method that makes it very easy to find predictions.
Penerapan Artificial Neural Network dengan Metode Backpropagation Dalam Memprediksi Harga Saham (Kasus: PT. Bank BCA, Tbk) Ridho, Ihda Innar; Ramadhani, Cerah Fitri; Windarto, Agus Perdana
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 8, No 1 (2023): Edisi Februari
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v8i1.612

Abstract

The Indonesia Stock Exchange (IDX) is a marketplace where individuals and investors can purchase or invest their capital in stocks for potential profits. There are currently 800 listed companies on the IDX, and one of them is PT Bank BCA Tbk, the largest private bank in Indonesia with a capital of Rp 42.93 trillion. Stocks serve as securities that demonstrate an investor's ownership in a company. In order to predict the future stock prices of companies, especially PT Bank BCA, and to increase the chances of profit for investors, an analysis is necessary. The purpose of this study is to create a forecast model using the Artificial Neural Network (ANN) method to predict the stock price of Bank BCA. Historical stock price data from Yahoo Finance (finance.yahoo.com) from 2016 to 2022 was used as the dataset. The goal of this research is to examine the effectiveness of the Backpropagation method in predicting Bank BCA's stock price. This research provides valuable information and considerations for investors when deciding whether to buy, hold, or sell their stocks. The accuracy rate of this research is 91.66666667%, with a testing MSE of 0.0010000650, and a total of 7695 epochs.
Co-Authors Agus Perdana Windarto Indra Riyana Rahadjeng Muhammad Noor Hasan Siregar Ridho, Ihda Innar