scholarly journals PERBANDINGAN PERFORMA PREDIKSI TINGKAT KEMISKINAN ANTARA BACKPROPAGATION NEURAL NETWORK DAN GENERALIZED REGRESSION NEURAL NETWORK

2018 ◽  
Vol 6 (2) ◽  
pp. 89
Author(s):  
Rina Mamase ◽  
Ruli S. Sinukun
Keyword(s):  
Neural Network ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Generalized Regression Neural Network ◽  
Backpropagation Neural Network ◽  
Absolute Percentage Error ◽  
Generalized Regression

Menurunkan tingkat kemiskinan penduduk merupakan suatu program kerja Pemerintah Indonesia yang hingga saat ini masih berlangsung.  Pemberian bantuan secara merata, tepat dan cepat merupakan salah satu upaya pemerintah dalam menangani masalah kemiskinan. Upaya tersebut dapat diwujudkan dengan penyajian data kemiskinan secara cepat dan akurat melalui prediksi tingkat kemiskinan menggunakan suatu metode yang efektif. Kemiskinan adalah masalah multi dimensional, sehingga diperlukan kesepakatan pendekatan/metode  yang dipakai apabila ingin memprediksi tingkat kemiskinan. Masalah kemiskinan tidak hanya berasal dari ketidakmampuan dalam memenuhi kebutuhan dasar saja, melainkan ada juga faktor atau indikator lain yang dapat mempengaruhi tingkat kemiskinan penduduk disuatu daerah/wilayah, seperti indikator pertanian, perdagangan dan industri.  Selain penggunaan indikator kebutuhan dasar  seperti kependudukan, tenaga kerja, pendidikan, dan kesehatan, penelitian ini juga mencoba menambahkan indikator pertanian, industri, dan perdagangan dalam prediksi tingkat kemiskinan. Metode prediksi yang digunakan dalam penelitian ini adalah Backpropagation Neural Network (BPNN) dan Generalized Regression Neural Network (GRNN). Pengujian dilakukan dengan menggunakan data tingkat kemiskinan di Provinsi Gorontalo pada tahun 2016 dan 2017. Mean  Absolute Percentage Error (MAPE) digunakan sebagai kriteria evaluasi model prediksi. Hasil dari prediksi tingkat kemiskinan diperoleh bahwa metode GRNN memiliki performa 14-16% lebih baik jika dibandingkan dengan metode BPNN.

scholarly journals Novel Spatiotemporal Feature Extraction Parallel Deep Neural Network for Forecasting Confirmed Cases of Coronavirus Disease 2019

2020 ◽  
Author(s):  
Chiou-Jye Huang ◽  
Yamin Shen ◽  
Ping-Huan Kuo ◽  
Yung-Hsiang Chen
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Absolute Error ◽  
Mean Absolute Percentage Error ◽  
The Other ◽  
Percentage Error ◽  
Model Assessment ◽  
Absolute Percentage Error ◽  
Spatiotemporal Feature ◽  
The Mean

AbstractThe coronavirus disease 2019 pandemic continues as of March 26 and spread to Europe on approximately February 24. A report from April 29 revealed 1.26 million confirmed cases and 125 928 deaths in Europe. This study proposed a novel deep neural network framework, COVID-19Net, which parallelly combines a convolutional neural network (CNN) and bidirectional gated recurrent units (GRUs). Three European countries with severe outbreaks were studied—Germany, Italy, and Spain—to extract spatiotemporal feature and predict the number of confirmed cases. The prediction results acquired from COVID-19Net were compared to those obtained using a CNN, GRU, and CNN-GRU. The mean absolute error, mean absolute percentage error, and root mean square error, which are commonly used model assessment indices, were used to compare the accuracy of the models. The results verified that COVID-19Net was notably more accurate than the other models. The mean absolute percentage error generated by COVID-19Net was 1.447 for Germany, 1.801 for Italy, and 2.828 for Spain, which were considerably lower than those of the other models. This indicated that the proposed framework can accurately predict the accumulated number of confirmed cases in the three countries and serve as a crucial reference for devising public health strategies.

scholarly journals PERBANDINGAN METODE REGRESI LINEAR DAN NEURAL NETWORK BACKPROPAGATION DALAM PREDIKSI NILAI UJIAN NASIONAL SISWA SMP MENGGUNAKAN SOFTWARE R

JOUTICA ◽  
2020 ◽  
Vol 5 (1) ◽  
pp. 331
Author(s):  
Masruroh Masruroh
Keyword(s):  
Neural Network ◽  
Mean Squared Error ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Output Data ◽  
Root Mean Squared Error ◽  
Absolute Percentage Error ◽  
Squared Error

Metode regresi linear dan neural network backpropagation merupakan metode yang kerap digunakan dalam model prediksi. Penelitian ini bertujuan untuk membandingkan akurasi metode regresi linear dan backpropagation dalam prediksi nilai Ujian Nasional siswa SMP. Data yang digunakan berupa data nilai ujian akhir semester dan ujian sekolah sebagai input dan nilai ujian nasional sebagai output. Data didapatkan dari SMPN 1 dan SMPN 2 Lamongan.. Jumlah dataset sebanyak 701 dibagi menjadi 75% data training dan 25% data testing. Simulasi prediksi dilakukan menggunakan software R. Parameter akurasi yang digunakan adalah Root Mean Squared Error (RMSE) dan Mean Absolute Percentage Error (MAPE). Hasil penelitian menunjukkan model prediksi menggunakan metode regresi linear menghasilkan RMSE sebesar 9,04 dan MAPE sebesar 3,94%, sedangkan model prediksi menggunakan backpropagation menghasilkan RMSE sebesar 7,28 dan MAPE sebesar 0,55%. Dengan demikian dalam penelitian ini metode neural network backpropagation memiliki akurasi yang lebih baik dalam prediksi nilai Ujian Nasional siswa SMP.

scholarly journals Rainfall Analysis and Forecasting Using Deep Learning Technique

2021 ◽  
Vol 2 (2) ◽  
pp. 1-11
Author(s):  
Pragati Kanchan ◽  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Mean Squared Error ◽  
Predictive Accuracy ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Root Mean Squared Error ◽  
Rainfall Prediction ◽  
Absolute Percentage Error ◽  
Squared Error

Rainfall forecasting is very challenging due to its uncertain nature and dynamic climate change. It's always been a challenging task for meteorologists. In various papers for rainfall prediction, different Data Mining and Machine Learning (ML) techniques have been used. These techniques show better predictive accuracy. A deep learning approach has been used in this study to analyze the rainfall data of the Karnataka Subdivision. Three deep learning methods have been used for prediction such as Artificial Neural Network (ANN) - Feed Forward Neural Network, Simple Recurrent Neural Network (RNN), and the Long Short-Term Memory (LSTM) optimized RNN Technique. In this paper, a comparative study of these three techniques for monthly rainfall prediction has been given and the prediction performance of these three techniques has been evaluated using the Mean Absolute Percentage Error (MAPE%) and a Root Mean Squared Error (RMSE%). The results show that the LSTM Model shows better performance as compared to ANN and RNN for Prediction. The LSTM model shows better performance with mini-mum Mean Absolute Percentage Error (MAPE%) and Root Mean Squared Error (RMSE%).

scholarly journals Comparison of Neural Network and Recurrent Neural Network to Predict Rice Productivity in East Java

2021 ◽  
Vol 5 (3) ◽  
Author(s):  
Andi Hamdianah
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Weather Factors ◽  
Absolute Percentage Error ◽  
The Neural Network ◽  
Input Parameters ◽  
Neural Network Algorithm ◽  
Backpropagation Learning

Rice is the staple food for most of the population in Indonesia which is processed from rice plants. To meet the needs and food security in Indonesia, a prediction is required. The predictions are carried out to find out the annual yield of rice in an area. Weather factors greatly affect production results so that in this study using weather parameters as input parameters. The Input Parameters are used in the Recurrent Neural Network algorithm with the Backpropagation learning process. The results are compared with Neural Networks with Backpropagation learning to find out the most effective method. In this study, the Recurrent Neural Network has better prediction results compared to a Neural Network. Based on the computational experiments, it is found that the Recurrent Neural Network obtained a Means Square Error of 0.000878 and a Mean Absolute Percentage Error of 10,8832%, while the Neural Network obtained a Means Square Error of 0.00104 and a Mean Absolute Percentage Error of 10,3804.

Forecasting and Analysis the Demand of Agricultural Mechanization for Economic Development

2013 ◽  
Vol 694-697 ◽  
pp. 3512-3515 ◽  
Author(s):  
Jin Yan Ju ◽  
Rong Xin Zhu ◽  
Lei Geng
Keyword(s):  
Neural Network ◽  
Economic Development ◽  
Bp Neural Network ◽  
Forecast Model ◽  
Mean Absolute Percentage Error ◽  
Total Power ◽  
Percentage Error ◽  
Absolute Percentage Error ◽  
Agricultural Mechanization ◽  
Forecast Models

The development of agricultural mechanization not only has to consider its development speed, but also should coordinate with economic development. Therefore, taking economic development as the independent variable, and agricultural mechanization development as the dependent variable, the nonlinear relationship model was established. Then, on the basis of forecasting GDP which on behalf of the economic development level, the demands of agricultural mechanization for economic development was predicted. Given the limitations of single forecast model, the nonlinear combination forecast models based on BP neural network was established to forecast the development relationship between economic and agricultural mechanization. The predicted results show that the fitting mean absolute percentage error is 2.61% for the relationship of economic development with agricultural mechanization development, and the fitting mean absolute percentage error is 2.14% for the GDP, which are all far less than the fitting error of traditional forecast models. The validation forecast was carried out; the results show that the combined forecast model can effectively improve the prediction accuracy. The demand of agricultural mechanization for economic development was forecasted from 2012 to 2020 in China using the established nonlinear combined forecast model based on BP neural network. The results show that the demand of total power of agricultural machinery for economic will be 1232298.2 MW by 2015 and 1560579.6 MW by 2020.

scholarly journals Metode Hibrida K-Means dan Generalized Regression Neural Network Untuk Prediksi Arus Lalu Lintas

2016 ◽  
Vol 7 (3) ◽  
Author(s):  
Saprina Mamase ◽  
Joko Lianto Buliali
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Cross Validation ◽  
Percentage Error ◽  
Generalized Regression Neural Network ◽  
Smoothing Factor ◽  
Traffic Flow Forecasting ◽  
Training Samples ◽  
Generalized Regression ◽  
Leave One Out

Abstract. Traffic flow forecasting is a popular research topic in the development of Intelligent Transportation System. There have been many forecasting methods used for traffic flow forecasting, such as Generalized Regression Neural Network (GRNN) which has a fairly good accuracy. One of the GRNN’s characteristics is that the number of neurons in pattern layer increases as the number of training samples raise and this can cause overfitting problem. In this research, a hybrid method to predict traffic flow is proposed, that is K-means and GRNN algorithm. K-means method aims to solve overfitting problem in GRNN model by choosing training samples based on their similar characteristics. Leave One Out Cross Validation (LOOCV) is used to select an appropriate smoothing factor parameter at each GRNN’s model. Mean Absolute Percentage Error (MAPE) is used as the evaluation criterion in the testing process. The results show that the proposed method could improve the accuracy of predictions by reducing the value of MAPE by 0.82-3.81%. Keywords: Traffic flow forecasting, K-means, Generalized Regression Neural Network, Leave One Out Cross Validation  Abstrak. Prediksi arus lalu lintas telah menjadi tren topik penelitian untuk pengembangan sistem transportasi cerdas. Telah banyak metode yang digunakan terkait prediksi arus lalu lintas, diantaranya yaitu Generalized Regression Neural Network (GRNN) yang memiliki akurasi yang cukup baik. Salah satu karakteristik GRNN adalah jumlah neuron pada pattern layer akan bertambah seiring meningkatnya jumlah data latih yang akan mengakibatkan masalah overfitting. Dalam penelitian ini diusulkan metode hibrida K-means dan GRNN untuk prediksi arus lalu lintas. Metode K-means bertujuan untuk mengatasi masalah overfitting pada model GRNN dengan memilih data latih berdasarkan kemiripan karateristiknya. Algoritma Leave One Out Cross Validation (LOOCV) digunakan untuk memilih parameter smoothing factor terbaik pada setiap model GRNN. Mean Absolute Percentage Error (MAPE) digunakan sebagai kriteria evaluasi model prediksi. Hasil menunjukkan bahwa metode yang diusulkan dapat meningkatkan akurasi prediksi dengan penurunan nilai MAPE sebesar 0,82-3,81%.Kata Kunci: Prediksi arus lalu lintas, K-means, Generalized Regression Neural Network, Leave One Out Cross Validation

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