Penerapan Backpropagation dan Analisis Sensitivitas pada Prediksi Indikator Terpenting Perusahaan Listrik

Fachri, Barany; Windarto, Agus Perdana; Parinduri, Ikhsan

doi:10.26418/jp.v5i2.31650

Cited by 13 publications

(10 citation statements)

References 7 publications

(9 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Each pattern of input and output information that is given into the artificial neural network is processed in neurons where the neurons are collected in layers called neuron layers. The layers making up the artificial neural network can be divided into 3, including: input layer, hidden layer dan output layer [10][11][12][13][14][15].…”

Section: Backpropagation Methodsmentioning

confidence: 99%

Increasing Prediction Accuracy with the Backpropagation Algorithm (Case Study: Pematangsiantar City Rainfall)

et al. 2019

View full text Add to dashboard Cite

The more advanced science and technology from various disciplines, currently rainfall can be predicted by carrying out various empirical approaches, one of which is by using Artificial Neural Networks (ANN). This study aims to apply ANN with backpropogation algorithm in predicting rainfall. The research data used is BPS data of the transfer city. The results of the study state that of the 6 models (4-5-1, 4-10-1, 4-25-1, 4-5-10-1, 4-5-25-1 and 4-5-50-1) architecture that was trained and tested using Matlab 6.1 application software, the results showed that the 4-5-25-1 architectural model was the best model for making predictions with 75% truth accuracy, Training MSE 0.001004582, Testing MSE 0.021882712 and Epoch 59,076 . It is expected that research can provide input to the government, especially BMKG Pematangsiantar city in predicting Rainfall based on computer science so as to improve the quality of services in the fields of Meteorology, Climatology, Air Quality and Geophysics in accordance with applicable laws and regulations.

show abstract

Section: Backpropagation Methodsmentioning

confidence: 99%

Increasing Prediction Accuracy with the Backpropagation Algorithm (Case Study: Pematangsiantar City Rainfall)

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Terkait dengan hal tersebut, Metode Jaringan Saraf Tiruan Backpropagation banyak digunakan untuk menyelesaik an permasalahanpermasalahan terkait dengan prediksi [3]- [8]. Selain itu Backpropagation juga cepat, sederhana dan mudah diprogram [9], tidak memiliki parameter tuning selain dari jumlah input [10], fleksibel karena tidak memerlukan pengetahuan mengenai nework sebelumnya [11], [12] dan tidak perlu fitur khusus dari fungsi yang akan dipelajari [13]- [15]. Pada penelitian [16] yang menerapkan Algoritma Backpropagation Prediksi Harga Komoditi terhadap Karakteristik Konsumen Produk Kopi Lokal Nasional.…”

Section: Pendahuluanunclassified

“…Dari permasalahan tersebut dirumuskan pemecahan masalah untuk memprediksi jumlah siswa Sekolah Dasar dengan menggunakan Jaringan Saraf Tiruan Backpropagation [12], [13], [15], [17]. Tujuan penelitian ini adalah membangun model Jaringan Saraf Tiruan Backpropagation yang tepat untuk memprediksi jumlah siswa Sekolah Dasar di Kecamatan Siantar Barat.…”

Section: Pendahuluanunclassified

Penerapan Pemilihan Model Arsitektur Terbaik pada Neural Network pada Prediksi Jumlah Siswa SD di Kecamatan Siantar Barat

Ramadhani

Siregar

Rahadjeng

et al. 2022

bits

View full text Add to dashboard Cite

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.

show abstract

“…Baik data tersebut disimpan dalam sebuah komputer maupun dalam sebuah email. Agar data dapat dikatakan benar-benar aman maka hal yang harus dilakukan adalah tentunya melakukan sistem pengamanan yang sangat baik, yang terbebas dari jangkauan orang-orang yang tidak berhak mengetahuinya, baik dari segi pengamanan fisik maupun secara sistem [1].…”

Section: Metode Penelitian 21 Keamananunclassified

Pengamanan Data Teks Menggunakan Algoritma DES Berbasis Android

Fachri

Sembiring

2020

mib

Self Cite

View full text Add to dashboard Cite

Android smartphone is certainly no stranger to the public and of course has spread widely to the world. Android smartphone has many applications in it and also has the function of each of these applications. One application that is commonly used is sending SMS, which functions as sending and receiving data quickly. SMS is a text message that at the time of sending has been limited and set its size, the size limit is 160 characters per SMS. But every use and use of SMS is certainly subject to shipping fees that have been determined from the operator. With this, security is one of the most important aspects to maintain the confidentiality and authenticity of the SMS messages so that they avoid being tapped, stolen or irresponsible. So it is very necessary to apply security in this SMS application that uses cryptographic DES (Data Encryption Standard) algorithm so that messages sent to their destinations can be kept confidential and authentic

show abstract

Penerapan Backpropagation dan Analisis Sensitivitas pada Prediksi Indikator Terpenting Perusahaan Listrik

Cited by 13 publications

References 7 publications

Increasing Prediction Accuracy with the Backpropagation Algorithm (Case Study: Pematangsiantar City Rainfall)

Increasing Prediction Accuracy with the Backpropagation Algorithm (Case Study: Pematangsiantar City Rainfall)

Penerapan Pemilihan Model Arsitektur Terbaik pada Neural Network pada Prediksi Jumlah Siswa SD di Kecamatan Siantar Barat

Pengamanan Data Teks Menggunakan Algoritma DES Berbasis Android

Contact Info

Product

Resources

About