scholarly journals Peramalan Pelayanan Service Mobil (After-Sale) Menggunakan Backpropagation Neural Network (BPNN)

2021 ◽  
Vol 6 (3) ◽  
pp. 149-160
Author(s):  
Novianti Puspitasari ◽  
Haviluddin ◽  
Arinda Mulawardani Kustiawan ◽  
Hario Jati Setyadi ◽  
Gubtha Mahendra Putra
Keyword(s):  
Neural Network ◽  
Standard Method ◽  
Automotive Industry ◽  
Mobile Service ◽  
Mean Square ◽  
Backpropagation Neural Network ◽  
Forecasting Accuracy ◽  
Learning Function ◽  
Architectural Model ◽  
Service Data

The automotive industry in Indonesia, primarily cars, is getting more and more varied. Along with increasing the number of vehicles, Brand Holder Sole Agents (ATPM) compete to provide after-sale services (mobile service). However, the company has difficulty knowing the rate of growth in the number of mobile services handled, thus causing losses that impact sources of income. Therefore, we need a standard method in determining the forecasting of the number of car services in the following year. This study implements the Backpropagation Neural Network (BPNN) method in forecasting car service services (after-sale) and Mean Square Error (MSE) for the process of testing the accuracy of the forecasting results formed. The data used in this study is car service data (after-sale) for the last five years. The results show that the best architecture for forecasting after-sales services using BPNN is the 5-10-5-1 architectural model with a learning rate of 0.2 and the learning function of trainlm and MSE of 0.00045581. This proves that the BPNN method can predict mobile service (after-sale) services with good forecasting accuracy values.

scholarly journals Prediksi Kedatangan Turis Asing ke Indonesia Menggunakan Backpropagation Neural Networks

2016 ◽  
Vol 4 (4) ◽  
pp. 485
Author(s):  
Haviluddin Haviluddin ◽  
Zainal Arifin ◽  
Awang Harsa Kridalaksana ◽  
Dedy Cahyadi
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Mean Square Error ◽  
Time Series Data ◽  
Alternative Methods ◽  
Series Data ◽  
Mean Square ◽  
Backpropagation Neural Network ◽  
Forecasting Accuracy ◽  
The Mean

In this paper, a backpropagation neural network (BPNN) method with time series data have been explored. The BPNN method to predict the foreign tourist’s arrival to Indonesia datasets have been implemented. The foreign tourist’s arrival datasets were taken from the center agency on statistics (BPS) Indonesia. The experimental results showed that the BPNN method with two hidden layers were able to forecast foreign tourist’s arrival to Indonesia. Where, the mean square error (MSE) as forecasting accuracy has been indicated. In this study, the BPNN method is able and recommended to be alternative methods for predicting time series datasets. Also, the BPNN method showed that effective and easy to use. In other words, BPNN method is capable to producing good value of forecasting.Keywords - BPNN; foreign tourists; BPS; MSEPemanfaatan backpropagation neural network (BPNN) dengan data deret waktu telah digunakan dalam paper ini. Metode BPNN telah digunakan untuk memprediksi data kedatangan turis asing ke Indonesia, dimana data turis tersebut diambil dari badan pusat statistik Indonesia (BPS). Hasil pengujian menunjukkan bahwa metode BPNN dengan dua lapisan tersembunyi mampu memodelkan dan meramalkan data kedatangan turis asing ke Indonesia yang diindikasikan dengan nilai mean square error (MSE). Penelitian ini merekomendasikan bahwa metode BPNN mampu menjadi alternative metode dalam memprediksi data yang berjenis deret waktu karena metode BPNN efektif dan lebih mudah digunakan serta mampu menghasilkan akurasi nilai peramalan yang baik.

scholarly journals A Novel Neuron in Kernel Domain

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Zahra Khandan ◽  
Hadi Sadoghi Yazdi
Keyword(s):  
Neural Network ◽  
Online Learning ◽  
Mean Square ◽  
Least Mean Square ◽  
Backpropagation Neural Network ◽  
Adaptive Parameters ◽  
Kernel Parameter ◽  
Online Learning Algorithms ◽  
Adaptive Kernel ◽  
Kernel Least Mean Square

Kernel-based neural network (KNN) is proposed as a neuron that is applicable in online learning with adaptive parameters. This neuron with adaptive kernel parameter can classify data accurately instead of using a multilayer error backpropagation neural network. The proposed method, whose heart is kernel least-mean-square, can reduce memory requirement with sparsification technique, and the kernel can adaptively spread. Our experiments will reveal that this method is much faster and more accurate than previous online learning algorithms.

scholarly journals Crude Palm Oil Prediction Based on Backpropagation Neural Network Approach

2019 ◽  
Vol 2 (1) ◽  
pp. 1
Author(s):  
Hijratul Aini ◽  
Haviluddin Haviluddin
Keyword(s):  
Neural Network ◽  
Palm Oil ◽  
Good Accuracy ◽  
Activation Function ◽  
Crude Palm Oil ◽  
Backpropagation Neural Network ◽  
Neural Network Approach ◽  
Learning Function ◽  
Hidden Layer ◽  
Accuracy Of Prediction

Crude palm oil (CPO) production at PT. Perkebunan Nusantara (PTPN) XIII from January 2015 to January 2018 have been treated. This paper aims to predict CPO production using intelligent algorithms called Backpropagation Neural Network (BPNN). The accuracy of prediction algorithms have been measured by mean square error (MSE). The experiment showed that the best hidden layer architecture (HLA) is 5-10-11-12-13-1 with learning function (LF) of trainlm, activation function (AF) of logsig and purelin, and learning rate (LR) of 0.5. This architecture has a good accuracy with MSE of 0.0643. The results showed that this model can predict CPO production in 2019.

scholarly journals MODEL ARSITEKTUR NEURAL NETWORK DENGAN BACKPROPOGATION PADA PREDIKSI TOTAL LABA RUGI KOMPREHENSIF BANK UMUM KONVENSIONAL

2018 ◽  
Vol 5 (2) ◽  
pp. 147 ◽  
Author(s):  
Agus Perdana Windarto ◽  
Muhammad Ridwan Lubis ◽  
Solikhun Solikhun
Keyword(s):  
Neural Network ◽  
Mean Square Error ◽  
Financial Services ◽  
Back Propagation ◽  
Mean Square ◽  
Comprehensive Income ◽  
Income Statement ◽  
Architectural Model ◽  
Testing Accuracy ◽  
Architectural Models

<p><em>determine the marketing strategy in increasing the total comprehensive income. This study aims to create the best architectural model using Backpropogation where this model can later be made to make predictions of total comprehensive income. The variable used in this study is the total comprehensive income statement data of PT. Bank Mandiri, Tbk (January - November 2016). Data sourced from the Financial Services Authority (www.ojk.go.id). From a series of trials conducted with 4 architectural models tested, namely 4-25-1; 4-50-1; 4-100-1 and 4-50-75-1, obtained the best architectural model 4-50-1 with Epoch training = 1977, Mean Square Error (MSE) of 0,000997867 with the correctness of testing accuracy reaching 80%.</em></p><p><strong><em>Keywords</em></strong><em>: Artificial Neural Network, Back-propagation, Comprehensive Income, Prediction, Economy, Architecture</em><em> </em></p><p><em>Prediksi total laba rugi komprehensif sangatlah penting untuk memprediksi dimana posisi angka total laba rugi komprehensif pada suatu bank.  Informasi tersebut berguna bagi masayarkat dalam menentukan arah investasi masyarakat ke depan, begitu juga bagi pihak bank berguna untuk menentukan kebijakan strategi pemasaran dalam meninggkatkan total laba komprehensif tersebut. Penelitian ini bertujuan untuk membuat model arsitektur terbaik dengan menggunakan Backpropogation dimana model ini nantinya dapat dilakukan untuk membuat prediksi terhadap total laba rugi komprehensif. Variabel yang digunakan pada penelitian ini adalah data total laba rugi komprehensif PT. Bank Mandiri,Tbk (Januari – November 2016). Data bersumber dari Otoritas Jasa Keuangan (<a href="http://www.ojk.go.id/">www.ojk.go.id</a>). Dari serangkaian uji coba yang dilakukan dengan 4 model arsitektur yang diuji yakni 4-25-1; 4-50-1; 4-100-1 dan 4-50-75-1, diperoleh model arsitektur terbaik 4-50-1 dengan </em><em>Epoch training = 1977</em><em>, </em><em>Mean Square Error (MSE) sebesar </em><em>0,000997867 dengan </em><em>tingkat akurasi pengujian mencapai kebenaran 80%.</em><em> </em></p><p><strong><em>Kata kunci</em></strong><em>: Jaringan saraf tiruan, Back-propagation, Laba Rugi Komprehensif, Prediksi, Ekonomi, Arsitektur</em></p>

scholarly journals Prediksi Tingkat Inflasi Dengan Menggunakan Metode Backpropagation Neural Network

2019 ◽  
Vol 1 (2) ◽  
pp. 8
Author(s):  
Kelvin Wong ◽  
Aji Prasetya Wibawa ◽  
Herman Santoso Pakpahan ◽  
Anton Prafanto ◽  
Hario Jati Setyadi
Keyword(s):  
Neural Network ◽  
Mean Square Error ◽  
Mean Square ◽  
Backpropagation Neural Network

Artikel ini bertujuan untuk memprediksi tingkat inflasi di Kota Samarinda, Kalimantan Timur dengan mengimplementasikan algoritma cerdas, Backpropagation Neural Network (BPNN). Data tingkat inflasi diperoleh dari Biro Pusat Statistik Provinsi (BPS) Kota Samarinda https://samarindakota.bps.go.id/ periode Januari 2012 hingga Januari 2017. Pengukuran akurasi prediksi algoritma BPNN menggunakan metode mean square error (MSE). Berdasarkan hasil percobaan, metode BPNN dengan parameter arsitektur 5-5-5-1; fungsi pembelajaran adalah trainlm; fungsi aktivasi adalah logsig dan purelin; laju pembelajaran adalah 0.1 mampu menghasilkan tingkat kesalahan prediksi yang baik dengan nilai MSE sebesar 0.00000424. Hasil penelitian menunjukkan bahwa algoritma BPNN ini dapat digunakan sebagai alternatif metode dalam memprediksi tingkat inflasi dalam rangka mendukung pertumbuhan ekonomi yang berkesinambungan sehingga dapat meningkatkan kesejahteraan masyarakat di Kota Samarinda, Kalimantan Timur.

scholarly journals Pengenalan Karakter Tulisan Menggunakan Metode Backpropagation Neural Network

2019 ◽  
Vol 1 (2) ◽  
pp. 14
Author(s):  
Ni’mah Moham ◽  
Felix Andika Dwiyanto ◽  
Herman Santoso Pakpahan ◽  
Islamiyah Islamiyah ◽  
Hario Jati Setyadi
Keyword(s):  
Neural Network ◽  
Mean Square Error ◽  
Learning Rate ◽  
Parameter Learning ◽  
Mean Square ◽  
Backpropagation Neural Network ◽  
Hidden Layer

Artikel ini bertujuan untuk menjelaskan langkah-langkah kerja metode Backpropagation Neural Network (BPNN) dalam mengenali pola Aksara Lontara Bugis Makassar dan menjelaskan seberapa akurat dalam mengenali pola aksara Lontara Bugis Makassar. Dari hasil pengujian, diperoleh tingkat akurasi sebesar 76.08%, dengan parameter learning rate sebesar 0,02, epoch maksimum sebesar 50 epoch dan hidden layer sebanyak 90 neuron berdasarkan ciri 8. Adapun, performa mean square error (MSE) sebesar 0.00424 telah diperoleh. Namun demikian, waktu yang dibutuhkan saat proses pembelajaran terbilang cukup lama yaitu 16 menit 56 detik. Berdasarkan hasil pengujian metode BPNN dapat direkomendasikan untuk mengenali pola aksara Lontara Bugis Makassar dalam rangka menunjang pembelajaran kepada masyarakat.

scholarly journals Peramalan KLBCampakMenggunakanGabunganMetode JST Backpropagationdan CART

2014 ◽  
Vol 8 (1) ◽  
pp. 49
Author(s):  
Sulistyowati Sulistyowati ◽  
Edi Winarko
Keyword(s):  
Neural Network ◽  
Binary Tree ◽  
Backpropagation Neural Network ◽  
Widespread Occurrence ◽  
Forecasting Accuracy ◽  
Periodic Data ◽  
Measles Outbreak ◽  
Accuracy Determination ◽  
Tree Method

Forecasting Measles Outbreak  in an area is necessary because to prevent widespread occurrence in an area. One way that is done in this study is to predict the incidence of measles by using a combination of backpropagation ANN and CART. Backpropagation ANN is used to predict the incidence of measles periodic data, then the CART method used to perform the determination of an outbreak or non-outbreak area.Backpropagation neural network is one of the most commonly used methods for forecasting which can result in a better level of accuracy than other ANN methods. While the methods of CART is a binary tree method is also popular for the classification, which can produce models or classification rules.Results of this study show that the number of the best window for backpropagation neural network to forecast the outcome affect forecasting accuracy. Determination of the number of windows of a backpropagation neural network forecasting on each attribute gives different results and directly affects the forecasting results. ANN can do the forecasting in time series using siliding window with accuracy 90.01% and then CART method can be use for classification with accuracy 83.33%.

scholarly journals Prediksi Produksi Minyak Kelapa Sawit Menggunakan Metode Backpropagation Neural Network

2019 ◽  
Vol 1 (1) ◽  
pp. 24
Author(s):  
Hijratul Aini ◽  
Haviluddin Haviluddin ◽  
Edy Budiman ◽  
Masna Wati ◽  
Novianti Puspitasari
Keyword(s):  
Neural Network ◽  
Mean Square Error ◽  
Mean Square ◽  
Backpropagation Neural Network

Artikel ini bertujuan untuk memprediksi produksi minyak kelapa sawit mentah (CPO) di PT. Perkebunan Nusantara (PTPN) XIII, Desa Long Pinang. Kabupaten Paser, Kalimantan Timur dengan menggunakan algoritma cerdas, jaringan saraf tiruan (JST) yang disebut Backpropagation Neural Network (BPNN). Data penelitian berasal dari produksi CPO periode Januari 2015 hingga Januari 2018. Pengukuran akurasi prediksi algoritma BPNN menggunakan metode mean square error (MSE).  Berdasarkan hasil percobaan, metode BPNN dengan parameter arsitektur 5-10-11-12-13-1; fungsi pembelajaran adalah trainlm; fungsi aktivasi adalah logsig dan purelin; laju pembelajaran adalah 0.7 mampu menghasilkan tingkat kesalahan prediksi yang baik dengan nilai MSE sebesar 0.0069. Hasil penelitian menunjukkan bahwa model ini dapat digunakan sebagai alternatif metode dalam memprediksi produksi CPO pada tahun 2019.

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