Prediksi Jumlah Kunjungan Wisata Mancanegara Dengan Algoritma Backpropagation

Bukittinggi City is known as a tourist destination that is very attractive in foreign tourist interest. Diverse types of tours are presented naturally and man-made the beauty of mountains, valleys and the beauty of the existing architectural buildings is Bukittinggi Clock Tower. Not only that, the type of culinary tourism and traditional market snacks are also an attraction for foreign tourists to travel in the city of Bukittinggi. In this study, the problem that will be discussed is the process of predicting tourist visits conducted by foreign tourists to the city of Bukittinggi. The prediction process uses the concept of artificial neural network backpropagation algorithm. The data set that will be used as a discussion is the data foreign tourist visits recorded in the Tourism Office of Bukittinggi City from 2018 to 2019. The prediction results generated with the concept of artificial neural network backpropagation algorithm produce output numbers of number of visits with an accuracy value of 95,64% and level value the resulting error is 4,36%. The benefits generated from this research are helping the government of the city of Bukittinggi especially the Tourism Office in providing input to manage the tourism sector.

Download Full-text

IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORK IN PREDICTING BIRTH RATE IN BATAM CITY USING BACKPROPAGATION METHOD

JURTEKSI ◽

10.33330/jurteksi.v8i1.1228 ◽

2021 ◽

Vol 8 (1) ◽

pp. 85-94

Author(s):

Muhammad Jufri

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Family Planning ◽

Population Growth ◽

Birth Rate ◽

The Past ◽

Backpropagation Method ◽

Artificial Neural ◽

The Government ◽

The City

Abstract: The population growth in Indonesia is increasing rapidly every year, so to help the government control the population growth through family planning programs, especially in the city of Batam. This study explains and describes one of the Artificial Terms Network methods, namely Backpropagation, where this method can predict what will happen in the future using data and information in the past. This study aims to predict the birth rate in the city of Batam to help the government with the family planning program. The data used is the annual data on the number of births in the city of Batam in 2016-2020 at The Civil Registry Office. To facilitate the analysis of research data, the data were tested using Matlab R2015b. In this study, the training process was carried out using 3 network architectures, namely 4-10-1, 5-18-1, and 4-43-1. Of these 3 architectures, the best is the 4-43-1 architecture with an accuracy rate of 91% and an MSE value of 0.0012205. The Backpropagation method can predict the amount of population growth in the city of Batam based on existing data in the past. Keywords: artificial neural network; backpropagation; prediction Abstrak: Pertumbuhan jumlah penduduk diindonesia yang setiap tahun meningkat dengan pesat, maka untuk membantu pemerintah mengendalikan jumlah pertumbuhan penduduk melalui program keluarga berencana khususnya dikota Batam. Penelitian ini menjelaskan dan memaparkan tentang salah satu metode Jaringan Syarat Tiruan yaitu Backpropagation, dimana metode ini dapat memprediksi apa yang akan terjadi masa yang akan datang dengan menggunakan data dan informasi dimasa lalu. Penelitian ini bertujuan untuk memprediksi tingkat kelahiran di kota Batam sehingga membatu pemerintah untuk perencanaan keluarga berencana. Data yang digunakan yaitu data tahunan jumlah kelahiran di kota Batam pada tahun 2016-2020 pada Dinas Kependudukan dan Catatan Sipil. Untuk mempermudah analisis data penelitian maka, data diuji menggunakan Matlab R2015b. Pada penelitian ini dilakukan proses pelatihan menggunakan 3 arsitektur jaringan yaitu 4-10-1, 5-18-1, dan 4-43-1. Dari ke-3 arsitektur ini yang terbaik adalah arsitektur 4-43-1 dengan tingkat akurasi sebesar 91% dan nilai MSE 0,0012205. Metode backpropagation mampu memprediksi jumlah pertumbuhan penduduk di kota Batam berdasarkan data yang ada dimasa lalu. Kata kunci: backpropagation; jaringan syaraf tiruan; prediksi

Download Full-text

Forecasting breaks in cast iron water mains in the city of Kingston with an artificial neural network model

Canadian Journal of Civil Engineering ◽

10.1139/cjce-2014-0114 ◽

2014 ◽

Vol 41 (10) ◽

pp. 918-923 ◽

Cited By ~ 6

Author(s):

Michael Nishiyama ◽

Yves Filion

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Confusion Matrix ◽

Ann Model ◽

Operating Characteristics ◽

Data Set ◽

Water Mains ◽

Artificial Neural ◽

Water Main ◽

The City

Predictive water main break models can assist municipalities in prioritizing the replacement and rehabilitation of water mains. The aim of the paper is to develop an artificial neural network (ANN) model to forecast water main breaks in the water distribution network of the City of Kingston, Ontario, Canada. The ANN model includes variables of diameter, age, length, and soil type to forecast breaks. Historical break data from the 1998 to 2011 period is used to develop the ANN model and forecast pipe breaks over a 5 year planning period. The mean square error, receiver operating characteristics curves, and a confusion matrix are used to evaluate the ANN model training and testing. The trained neural network correctly classified 85% of the data set at the training, validation, and testing stages. Model forecasts showed lower pipe break rates in Kingston West, Kingston Central, and Kingston East. The reduction in break rate in the Kingston system was attributed to the removal of old pipes, and the favourable performance of pipes that are in the usage phase of their life cycle. The ANN model provided Utilities Kingston with a tool to assist them in the planning and management of their water main rehabilitation program.

Download Full-text

Penerapan Jaringan Saraf Tiruan dalam Menentukan Kelayakan Calon Penerima Bantuan Renovasi Rumah pada Kantor Pangulu Nagori Tangga Batu Dengan Metode Backpropogation

Prosiding Seminar Nasional Riset Information Science (SENARIS) ◽

10.30645/senaris.v1i0.46 ◽

2019 ◽

Vol 1 ◽

pp. 405

Author(s):

Chyntia Irwana ◽

M. Safii ◽

Iin Parlina

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Poverty Alleviation ◽

Basic Needs ◽

Backpropagation Algorithm ◽

Neural Network Method ◽

Network Method ◽

Artificial Neural Network Method ◽

Artificial Neural ◽

The Government

Home is one of the basic needs for humans, where the house serves as a place to shelter and shelter. Apart from having a function as a place to live, the house also functions as a place for fostering and chatting with a family. Poverty is a condition where a person is unable to fulfill his basic needs. In Nagori Tangga Batu there are still many people who have homes that are not habitable. Based on these problems, the government organized a poverty alleviation program through a home renovation route for residents of Nagori Tangga Batu village. In determining whether or not a house is suitable for renovation, it is necessary to use an Artificial Neural Network using the Backpropogation algorithm to determine whether or not the house of Nagori Tangga Batu residents is eligible for home renovation assistance. The best research with Artificial Neural Network method in determining the feasibility of recipients of home renovation assistance using the backpropagation algorithm is the model 6-3-1 with the repetition process (epoch) during training with epoch value = 1673 and MSE achievement during testing with MSE = 0.00797068 . This research is expected to be a reference for further researchers relating to the user algorithm used.

Download Full-text

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

Engineering and Technology Journal ◽

10.30684/etj.v38i4a.399 ◽

2020 ◽

Vol 38 (4A) ◽

pp. 510-514

Author(s):

Tay H. Shihab ◽

Amjed N. Al-Hameedawi ◽

Ammar M. Hamza

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Artificial Neural Network ◽

Random Forest ◽

Satellite Image ◽

Landsat 8 ◽

Optical Remote Sensing ◽

Data Set ◽

Artificial Neural ◽

Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019. They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

Download Full-text

Watermelon Sorting Process by Frequency Identification and Artificial Neural Network

Applied Science and Engineering Progress ◽

10.14416/j.asep.2021.12.004 ◽

2021 ◽

Author(s):

Komsan Wongkalasin ◽

Teerapon Upachaban ◽

Wacharawish Daosawang ◽

Nattadon Pannucharoenwong ◽

Phadungsak Ratanadecho

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Selection Process ◽

Network Processor ◽

Data Set ◽

The Neural Network ◽

Frequency Identification ◽

Sorting Process ◽

Artificial Neural ◽

Reasonable Prediction

This research aims to enhance the watermelon’s quality selection process, which was traditionally conducted by knocking the watermelon fruit and sort out by the sound’s character. The proposed method in this research is generating the sound spectrum through the watermelon and then analyzes the response signal’s frequency and the amplitude by Fast Fourier Transform (FFT). Then the obtained data were used to train and verify the neural network processor. The result shows that, the frequencies of 129 and 172 Hz were suit to be used in the comparison. Thirty watermelons, which were randomly selected from the orchard, were used to create a data set, and then were cut to manually check and match to the fruits’ quality. The 129 Hz frequency gave the response ranging from 13.57 and above in 3 groups of watermelons quality, including, not fully ripened, fully ripened, and close to rotten watermelons. When the 172 Hz gave the response between 11.11–12.72 in not fully ripened watermelons and those of 13.00 or more in the group of close to rotten and hollow watermelons. The response was then used as a training condition for the artificial neural network processor of the sorting machine prototype. The verification results provided a reasonable prediction of the ripeness level of watermelon and can be used as a pilot prototype to improve the efficiency of the tools to obtain a modern-watermelon quality selection tool, which could enhance the competitiveness of the local farmers on the product quality control.

Download Full-text

Identification Of Number Using Artificial Neural Network Backpropagation

MATEC Web of Conferences ◽

10.1051/matecconf/201821501011 ◽

2018 ◽

Vol 215 ◽

pp. 01011

Author(s):

Sitti Amalia

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Training Data ◽

Simulation Test ◽

Backpropagation Algorithm ◽

Network Parameter ◽

Artificial Intelligent ◽

Design And Implementation ◽

Network Training ◽

Artificial Neural

This research proposed to design and implementation system of voice pattern recognition in the form of numbers with offline pronunciation. Artificial intelligent with backpropagation algorithm used on the simulation test. The test has been done to 100 voice files which got from 10 person voices for 10 different numbers. The words are consisting of number 0 to 9. The trial has been done with artificial neural network parameters such as tolerance value and the sum of a neuron. The best result is shown at tolerance value varied and a sum of the neuron is fixed. The percentage of this network training with optimal architecture and network parameter for each training data and new data are 82,2% and 53,3%. Therefore if tolerance value is fixed and a sum of neuron varied gave 82,2% for training data and 54,4% for new data

Download Full-text

A Model on the Correlation between Composition and Mechanical Properties of Mg-Al-Zn Alloys by Using Artificial Neural Network

Materials Science Forum ◽

10.4028/www.scientific.net/msf.488-489.793 ◽

2005 ◽

Vol 488-489 ◽

pp. 793-796 ◽

Cited By ~ 1

Author(s):

Hai Ding Liu ◽

Ai Tao Tang ◽

Fu Sheng Pan ◽

Ru Lin Zuo ◽

Ling Yun Wang

Keyword(s):

Neural Network ◽

Mechanical Properties ◽

Artificial Neural Network ◽

Magnesium Alloys ◽

Tensile Yield Strength ◽

Data Set ◽

The Neural Network ◽

Artificial Neural ◽

Prediction Of Mechanical Properties ◽

Artificial Neural Network Ann

A model was developed for the analysis and prediction of correlation between composition and mechanical properties of Mg-Al-Zn (AZ) magnesium alloys by applying artificial neural network (ANN). The input parameters of the neural network (NN) are alloy composition. The outputs of the NN model are important mechanical properties, including ultimate tensile strength, tensile yield strength and elongation. The model is based on multilayer feedforward neural network. The NN was trained with comprehensive data set collected from domestic and foreign literature. A very good performance of the neural network was achieved. The model can be used for the simulation and prediction of mechanical properties of AZ system magnesium alloys as functions of composition.

Download Full-text

PENDUGAAN FLUKS PANAS DAN EVAPOTRANSPIRASI DENGAN JARINGAN SYARAF TIRUANHEAT FLUX AND EVAPOTRANSPIRATION ESTIMATION USING ARTIFICIAL NEURAL NETWORK

Agromet ◽

10.29244/j.agromet.25.1.24-28 ◽

2011 ◽

Vol 25 (1) ◽

pp. 24

Author(s):

Satyanto Krido Saptomo

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Latent Heat ◽

Measurement Data ◽

Heat Fluxes ◽

Similar Process ◽

Observation Data ◽

Data Set ◽

Dissipation Process ◽

Artificial Neural

Artificial neural network (ANN) approach was used to model energy dissipation process into sensible heat and latent heat (evapotranspiration) fluxes. The ANN model has 5 inputs which are leaf temperature Tl, air temperature Ta, net radiation Rn, wind speed uc and actual vapor pressure ea. Adjustment of ANN was conducted using back propagation technique, employing measurement data of input and output parameters of the ANN. The estimation results using the adjusted ANN shows its capability in resembling the heat dissipation process by giving outputs of sensible and latent heat fluxes closed to its respective measurement values as the measured input values are given. The ANN structure presented in this paper suits for modeling similar process over vegetated surfaces, but the adjusted parameters are unique. Therefore observation data set for each different vegetation and adjustment of ANN are required.

Download Full-text

Adaptation of the Diagnostic Artificial Neural Network Structure When New Training Examples Appear

Proceedings of Telecommunication Universities ◽

10.31854/1813-324x-2020-6-4-120-126 ◽

2020 ◽

Vol 6 (4) ◽

pp. 120-126

Author(s):

A. Malikov

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Network ◽

Information Security ◽

Classification Problem ◽

Data Set ◽

Neural Network Structure ◽

Artificial Neural ◽

Training Examples ◽

Information Image

In this paper we can see that identified computer incidents are subject for diagnostics, during which the characteristics of information security violations are clarified (purpose, causes, consequences, etc.). To diagnose computer incidents, we can use methods of automation while collection and processing the events that occur as a result of the implementation of scenarios for information security violations. Artificial neural networks can be used to solve the classification problem of assigning diagnostic data set (information image of a computer incident) to one of the possible values of the violation characteristic. The purpose of this work is to adapt the structure of an artificial neural network that allows the accuracy diagnostics of computer incidents when new training examples appear.

Download Full-text

Penerapan Algoritma Artificial Neural Network untuk Klasifikasi Opini Publik Terhadap Covid-19

Generation Journal ◽

10.29407/gj.v5i2.16125 ◽

2021 ◽

Vol 5 (2) ◽

pp. 109-118

Author(s):

Euis Saraswati ◽

Yuyun Umaidah ◽

Apriade Voutama

Keyword(s):

Neural Network ◽

Social Media ◽

Artificial Neural Network ◽

Text Mining ◽

Ann Model ◽

Rapid Spread ◽

Backpropagation Method ◽

Artificial Neural ◽

The Government ◽

Artificial Neural Network Ann

Coronavirus disease (Covid-19) or commonly called coronavirus. This virus spreads very quickly and even almost infects the whole world, including Indonesia. A large number of cases and the rapid spread of this virus make people worry and even fear the increasing spread of the Covid-19 virus. Information about this virus has also been spread on various social media, one of which is Twitter. Various public opinions regarding the Covid-19 virus are also widely expressed on Twitter. Opinions on a tweet contain positive or negative sentiments. Sentiments of sentiment contained in a tweet can be used as material for consideration and evaluation for the government in dealing with the Covid-19 virus. Based on these problems, a sentiment analysis classification is needed to find out public opinion on the Covid-19 virus. This research uses Artificial Neural Network (ANN) algorithm with the Backpropagation method. The results of this test get 88.62% accuracy, 91.5% precision, and 95.73% recall. The results obtained show that the ANN model is quite good for classifying text mining.

Download Full-text