scholarly journals PENERAPAN JARINGAN SARAF TIRUAN DALAM MEMPREDIKSI JUMLAH KEMISKINAN PADA KABUPATEN/KOTA DI PROVINSI RIAU

2018 ◽  
Vol 5 (1) ◽  
pp. 61
Author(s):  
Anjar Wanto
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Natural Resources ◽  
Error Rate ◽  
Poverty Rate ◽  
Central Bureau ◽  
Poor People ◽  
Accuracy Rate ◽  
Architectural Models ◽  
Artificial Neural

<p><em>Provinsi Riau yang kaya akan Sumber Daya Alam ternyata tidak sebanding dengan jumlah penduduk miskin yang menempati di sejumlah kabupaten/kota di Riau. Contohnya seperti pada tahun 2013 terdapat ± 68.600 penduduk miskin di kabupaten Kampar, atau merupakan yang tertinggi dibandingkan kabupaten/kota lainnya. Oleh karena itu dibutuhkan langkah-langkah strategis agar jumlah penduduk miskin tidak bertambah sepanjang tahun, salah satu nya adalah dengan melakukan prediksi jumlah penduduk miskin untuk tahun-tahun selanjutnya. Cara ini dilakukan agar angka kemiskinan bisa semakin ditekan dengan cara melakukan penganggulangan sejak dini. Data yang akan diprediksi adalah data jumlah kemiskinan kabupaten/kota di Provinsi Riau yang bersumber dari Badan Pusat Statistik Provinsi Riau tahun 2010 sampai dengan 2015. Algoritma yang digunakan untuk melakukan prediksi adalah jaringan saraf tiruan Backpropagation. Algoritma ini memiliki kemampuan untuk mengingat dan membuat generalisasi dari apa yang sudah ada sebelumnya. Ada 5 model arsitektur yang digunakan pada algoritma backpropagation ini, antara lain 4-2-5-1 yang nanti nya akan menghasilkan prediksi dengan tingkat akurasi 8%, 4-5-6-1=25%, 4-10-12-1=92%, 4-10-15-1=100% dan 4-15-18-1=33%. Arsitektur terbaik dari ke 5 model ini adalah 4-10-12-1 dengan tingkat keakurasian mencapai 100% dan tingkat error yang digunakan 0,001-0,05. Sehingga model arsitektur ini cukup baik digunakan untuk memprediksi jumlah kemiskinan. </em><br /> <br /><em><strong>Keywords</strong>: Penerapan, Jaringan Saraf Tiruan, Backpropagation, Prediksi, Kemiskinan</em></p><p><em>Riau is rich in Natural Resources is not comparable with the number of poor people who occupy in a number of districts/cities in Riau. For example, in 2013 there were ± 68,600 poor people in Kampar district, or the highest compared to other districts. Therefore, strategic steps are needed so that the number of poor people will not increase throughout the year, one of them is to predict the number of poor people for the next years. This way is done so that the poverty rate can be further suppressed by doing the countermeasures early on. The data to be predicted is the data of the number of poverty districts/cities in Riau Province sourced from the Central Bureau of Statistics of Riau Province in 2010 until 2015. Algorithm used to make prediction is the Backpropagation. This algorithm has the ability to remember and make generalizations of what has been there before. There are 5 architectural models, among others 4-2-5-1 which later will produce predictions with an accuracy rate of 8%, 4-56-1=25%, 4-10-12-1=92%, 4-10-15-1=100% and 4-15-18-1=33%. The best architecture of the 5 models is 4-10-12-1 with 100% accuracy and error rate of 0.001-0.05. So this model of architecture is good enough used to predict the amount of poverty. </em></p><p><em><strong>Kata kunci</strong>: Implementation, Artificial Neural Network, Backpropagation, Prediction, Poverty</em></p>

scholarly journals ANALISIS ALGORITMA BACKPROPAGATION DALAM PREDIKSI NILAI EKSPOR (JUTA USD)

2018 ◽  
Vol 15 (2) ◽  
Author(s):  
Jonas Rayandi Saragih ◽  
Mhd. Billy Sandi Saragih ◽  
Anjar Wanto
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Error Rate ◽  
Research Data ◽  
Central Bureau ◽  
Accuracy Rate ◽  
Backpropagation Algorithm ◽  
Architectural Models ◽  
Artificial Neural ◽  
North Sumatra

In a study, the analysis is necessary for the accuracy and accuracy of an education. So also in prediction Export Value (Million USD). This research will discuss the value of export in general in North Sumatra based on Million USD. This research is conducted to know the export development in North Sumatera in the future. This research uses Artificial Neural Network with Backpropagation algorithm. The research data used comes from the Central Bureau of Statistics of North Sumatra from 2012 until 2017. This research will use five architectural models namely 4-5-1, 4-7-1, 4-9-1, 4-10-1 and 4-11-1. The best model of the five models is 4-7-1 with a 100% accuracy rate, with a time of 27 seconds. The error rate used is 0.001 - 0.05. Thus, this model is good enough to predict Export Value in North Sumatra, because its accuracy reaches 100%.

scholarly journals Analisis JST Dalam Memprediksi Jumlah Tamu Pada Hotel NonBintang

Techno Com ◽  
2018 ◽  
Vol 17 (4) ◽  
pp. 333-346
Author(s):  
Bil Klinton Sihotang ◽  
Anjar Wanto
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Data Analysis ◽  
Error Rate ◽  
Central Bureau ◽  
Material Research ◽  
Study Material ◽  
Study Materials ◽  
Architectural Models ◽  
Tourism And Hospitality

Analysis on a prediction (forecasting) is very important to do in a study, So with this data analysis will be obtained a clear picture of the issues discussed. As well as in predicting the number of  guests in non-star hotels. This research is expected to be useful for both Government and private parties as one of the study materials in the development of hotel business, as well as for academics as study material / research especially related to tourism and hospitality field. The data used in this study is data on the number of guests in non-star hotels by province from the Central Bureau of Statistics Indonesia from 2011 to 2016. This study uses the method of artificial neural network Backpropagation using 5 architectural models, those are 4-19-1, 4-50-1, 4-17-1, 4-16-1, 4-22-. From  architecture, the best architecture is 12-19-1 with an accuracy of 88.2%, MSE 0.10206089 with error rate used 0.001 - 0.05. Thus, this model is good enough to predict the number of guests indonesia in non-star hotels

Optimizing Error Rate in Intrusion Detection System Using Artificial Neural Network Algorithm

2018 ◽  
Vol 6 (9) ◽  
pp. 152
Author(s):  
S. Vijaya Rani ◽  
G. N. K. Suresh Babu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intrusion Detection ◽  
Error Rate ◽  
Learning Process ◽  
Nearest Neighbor ◽  
Detection System ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Artificial Neural

The illegal hackers  penetrate the servers and networks of corporate and financial institutions to gain money and extract vital information. The hacking varies from one computing system to many system. They gain access by sending malicious packets in the network through virus, worms, Trojan horses etc. The hackers scan a network through various tools and collect information of network and host. Hence it is very much essential to detect the attacks as they enter into a network. The methods  available for intrusion detection are Naive Bayes, Decision tree, Support Vector Machine, K-Nearest Neighbor, Artificial Neural Networks. A neural network consists of processing units in complex manner and able to store information and make it functional for use. It acts like human brain and takes knowledge from the environment through training and learning process. Many algorithms are available for learning process This work carry out research on analysis of malicious packets and predicting the error rate in detection of injured packets through artificial neural network algorithms.

scholarly journals Analisis Jaringan Syaraf Tiruan untuk prediksi volume ekspor dan impor migas di Indonesia

2018 ◽  
Vol 4 (1) ◽  
pp. 30
Author(s):  
Yuli Andriani ◽  
Hotmalina Silitonga ◽  
Anjar Wanto
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Crude Oil ◽  
Oil And Gas ◽  
Oil Products ◽  
Oil Exports ◽  
Directorate General ◽  
Architectural Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Analisis pada penelitian penting dilakukan untuk tujuan mengetahui ketepatan dan keakuratan dari penelitian itu sendiri. Begitu juga dalam prediksi volume ekspor dan impor migas di Indonesia. Dilakukannya penelitian ini untuk mengetahui seberapa besar perkembangan ekspor dan impor Indonesia di bidang migas di masa yang akan datang. Penelitian ini menggunakan Jaringan Syaraf Tiruan (JST) atau Artificial Neural Network (ANN) dengan algoritma Backpropagation. Data penelitian ini bersumber dari dokumen kepabeanan Ditjen Bea dan Cukai yaitu Pemberitahuan Ekspor Barang (PEB) dan Pemberitahuan Impor Barang (PIB). Berdasarkan data ini, variabel yang digunakan ada 7, antara lain: Tahun, ekspor minyak mentah, impor minyak mentah, ekspor hasil minyak, impor hasil minyak, ekspor gas dan impor gas. Ada 5 model arsitektur yang digunakan pada penelitian ini, 12-5-1, 12-7-1, 12-8-1, 12-10-1 dan 12-14-1. Dari ke 5 model yang digunakan, yang terbaik adalah 12-5-1 dengan menghasilkan tingkat akurasi 83%, MSE 0,0281641257 dengan tingkat error yang digunakan 0,001-0,05. Sehingga model ini bagus untuk memprediksi volume ekspor dan impor migas di Indonesia, karena akurasianya antara 80% hingga 90%.   Analysis of the research is Imporant used to know precision and accuracy of the research itself. It is also in the prediction of Volume Exports and Impors of Oil and Gas in Indonesia. This research is conducted to find out how much the development of Indonesia's exports and Impors in the field of oil and gas in the future. This research used Artificial Neural Network with Backpropagation algorithm. The data of this research have as a source from custom documents of the Directorate General of Customs and Excise (Declaration Form/PEB and Impor Export Declaration/PIB). Based on this data, there are 7 variables used, among others: Year, Crude oil exports, Crude oil Impors, Exports of oil products, Impored oil products, Gas exports and Gas Impors. There are 5 architectural models used in this study, 12-5-1, 12-7-1, 12-8-1, 12-10-1 and 12-14-1. Of the 5 models has used, the best models is 12-5-1 with an accuracy 83%, MSE 0.0281641257 with error rate 0.001-0.05. So this model is good to predict the Volume of Exports and Impors of Oil and Gas in Indonesia, because its accuracy between 80% to 90%.

Predictive Models for Pre-Operative Diagnosis of Rotator Cuff Tear: A Comparison Study of Two Methods between Logistic Regression and Artificial Neural Network

2014 ◽  
Vol 595 ◽  
pp. 263-268
Author(s):  
Chen Chiang Lin ◽  
Hsin Hui Chan ◽  
Chen Yuan Huang ◽  
Nang Shu Yang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Rotator Cuff ◽  
Logistic Model ◽  
Predictive Accuracy ◽  
Rotator Cuff Tears ◽  
Comparison Study ◽  
Accuracy Rate ◽  
Artificial Neural

Rotator cuff tears are the most common disorder of the shoulders.agnetic resonance Image (MRI) is the diagnostic gold standard of rotator cuff tears. However, there are some dilemmas in the rotator cuff tears treatment. Clinically, surgical results of rotator cuff tears are sometimes different from MRI results of rotator cuff tears. The main purpose of this study is to build up predicative models for pre-operative diagnosis of rotator cuff tears There are two models of this study are proposed: logistic regression model and artificial neural network model. Patients are divided into two sets: Set1 is patients with full thickness rotators cuff tears. Set 2 is patients with partial thickness rotators cuff tears. The charts of 158 patients are completely reviewed and the collected data were analyzed. The results showed that the predictive accuracy of artificial neural networks model is higher than the predictive accuracy of logistic model. The application of this study can assist doctors to increase the accuracy rate of pre-operative diagnosis and to decrease the legal problems.

scholarly journals Analisis Jaringan Syaraf Tiruan untuk Memprediksi Jumlah Narapidana pada Lembaga Pemasyarakatan Simalungun dengan Metode Backpropagation

2019 ◽  
Vol 1 ◽  
pp. 762
Author(s):  
Vicky Adriani ◽  
Irfan Sudahri Damanik ◽  
Jaya Tata Hardinata
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Human Brain ◽  
Network Architecture ◽  
Accuracy Rate ◽  
Backpropagation Algorithm ◽  
Artificial Neural ◽  
Detention Facilities

The author has conducted research at the Simalungun District Prosecutor's Office and found the problem of prison rooms that did not match the number of prisoners which caused a lack of security and a lack of detention facilities and risked inmates to flee. Artificial Neural Network which is one of the artificial representations of the human brain that always tries to simulate the learning process of the human brain. The application uses the Backpropagation algorithm where the data entered is the number of prisoners. Then Artificial Neural Networks are formed by determining the number of units per layer. Once formed, training is carried out from the data that has been grouped. Experiments are carried out with a network architecture consisting of input units, hidden units, and output units. Testing using Matlab software. For now, the number of prisoners continues to increase. Predictions with the best accuracy use the 12-3-1 architecture with an accuracy rate of 75% and the lowest level of accuracy using 12-4-1 architecture with an accuracy rate of 25%.

scholarly journals SPEAKER IDENTIFICATION MENGGUNAKAN TRANSFORMASI WAVELET DISKRIT DAN JARINGAN SARAF TIRUAN BACK-PROPAGATION

2008 ◽  
Vol 2 (1) ◽  
pp. 1
Author(s):  
Anny Tandyo ◽  
Martono Martono ◽  
Adi Widyatmoko
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Speaker Recognition ◽  
Speaker Identification ◽  
Back Propagation ◽  
Identification Accuracy ◽  
Identification System ◽  
Accuracy Rate ◽  
Discrete Transformation ◽  
Artificial Neural

Article discussed a speaker identification system. Which was a part of speaker recognition. The system identified asubject based on the voice from a group of pattern had been saved before. This system used a wavelet discrete transformationas a feature extraction method and an artificial neural network of back-propagation as a classification method. The voiceinput was processed by the wavelet discrete transformation in order to obtain signal coefficient of low frequency as adecomposition result which kept voice characteristic of everyone. The coefficient then was classified artificial neural networkof back-propagation. A system trial was conducted by collecting voice samples directly by using 225 microphones in nonsoundproof rooms; contained of 15 subjects (persons) and each of them had 15 voice samples. The 10 samples were used as atraining voice and 5 others as a testing voice. Identification accuracy rate reached 84 percent. The testing was also done onthe subjects who pronounced same words. It can be concluded that, the similar selection of words by different subjects has noinfluence on the accuracy rate produced by system.Keywords: speaker identification, wavelet discrete transformation, artificial neural network, back-propagation.

scholarly journals Artificial Neural Network with Backpropogation Method in Predicting Sukuk Retail State Based on Age Categories in Promoting Economic Growth

2018 ◽  
Author(s):  
dedisuhendro
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Data Series ◽  
Accuracy Rate ◽  
Public Funds ◽  
The Public ◽  
Artificial Neural ◽  
Input Variables ◽  
The Government ◽  
Age Category

Sukuk Retail State has fixed remuneration that paid every month. The government gains equity from the useof public funds, while the public gets a profit from the investment. The contribution of this researchprovides benefits for promoting optimally on the next sukuk issuance. Referral data sourced from Ministryof Finance through website www.djppr.kemenkeu.go.id. The data are sukuk sales data series 003 - 009which are grouped into several categories namely geography, profession and age category. The method usedis Artificial Neural Network Backpropogation. The input variables used are age category &lt;25 (X1), agecategory 25 - 40 (X2), age category 41 - 55 (X3), and age category&gt; 55 (X4) with model of trainingarchitecture and test of 4 architecture ie 4-2-1, 4-5-1, 4-2-5-1 and 4-5-2-1. The results of this study providethe best architecture 4-2-1 with epoch 1593, MSE 0.00099950214 and 71% accuracy rate. Furthermore, thesensitivity analysis was performed to determine the best performing variables, resulting in the 41-55 (X3)age category variable with a score of 0.4089. Thus obtained the prediction of most investors on the purchaseof sukuk series 010 is the age category 41 - 55.

scholarly journals Business Intelligence Design for Data Visualization and Drug Stock Forecasting

Intelmatics ◽  
2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Novenia Eka Warestika ◽  
Dedy Sugiarto ◽  
Teddy Siswanto
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Error Rate ◽  
Business Intelligence ◽  
Neural Network Method ◽  
Good Ability ◽  
Network Method ◽  
Artificial Neural Network Method ◽  
Operational Decision Making ◽  
Artificial Neural

Klinik Pratama is one form of service provided by the Ministry of Communication and Information of the Republic of Indonesia in protect employees from health disorders that could affect employee productivity. In its development, the clinic often finds problems, one of them is often a shortage or excess the drug stock on a running period. Therefore, it be required a design of an Business Intelligence that manages complex data into a data visualization forecasting of the future stock of drugs. Historical data processing of the drug is done with process of Extract, Transform and Load (ETL) using the Spoon Pentaho Data Integration tools. While the visualization of drug stock data and forecast results is done using Microsoft Power BI (Business Intelligence) tools and for forecasting is done with Artificial Neural Network method by RStudio tools. The results of forecasting the amount of stock out of drug samples using the Artificial Neural Network method obtained an MSE value of 67.72 and RMSE 8.22 which means that this forecast has a good ability with the resulting error rate is relatively small. From this research, the Klinik Pratama of the Ministry of Communication and Information can easily understand and analyze drug stock data and can support operational decision making The results of forecasting the amount of stock out of drug samples using the Artificial Neural Network method obtained an MSE value of 67.72 and RMSE 8.22 which means that this forecast has a good ability because the resulting error rate is relatively small. From this research, Klinik Pratama of the Ministry of Communication and Information can easily understand and analyze drug stock data and can support operational decision making The results of forecasting the amount of stock out of drug samples using the Artificial Neural Network method obtained an MSE value of 67.72 and RMSE 8.22 which means that this forecast has a good ability with the resulting error rate is relatively small. From this research, Klinik Pratama of the Ministry of Communication and Information can easily understand and analyze drug stock data and can support operational decision making.

scholarly journals A Conceptual Artificial Neural Network Model in Warehouse Receiving Management

2021 ◽  
Vol 11 (2) ◽  
pp. 130-136
Author(s):  
Judy X Yang ◽  
◽  
Lily D Li ◽  
Mohammad G. Rasul
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mechanical Design ◽  
Joint Probability ◽  
Initial Result ◽  
Ann Model ◽  
Accuracy Rate ◽  
Proposed Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The purpose of this research is to explore a suitable Artificial Neural Network (ANN) method applying to warehouse receiving management. A conceptual ANN model is proposed to perform identification and counting of components. The proposed model consists of a standard image library, an ANN system to present objects for identification from the real-time images and to count the number of objects in the image. The authors adopted four basic mechanical design shapes as the attributes of images for shape analysis and pre-defined features; the joint probability from Bayes theorem and image pixel values for object counting is applied in this research. Compared to other ANNs, the proposed conceptual model is straightforward to perform classification and counting. The model is tested by employing a mini image dataset which is industrial enterprise relevant. The initial result shows that the proposed model has achieved an accuracy rate of 80% in classification and a 97% accuracy rate in counting. The development of the model is associated with a few challenges, including exploring algorithms to enhance the accuracy rate for component identification and testing the model in a larger dataset.

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