Backpropagation Algorithms

Wireless sensor networks (WSN) usage in numerous applications that increased need has risen to explore more efficient and effective manner. Localization is an important issue that can bring efficiency to WSNs. Localization depicts to calculation of the actual location of unknown nodes in network. This paper focuses on the comparison and analysis of Multi-Layer Perceptron Neural Networks (MLPNN) and Radial Basis Function Neural Networks (RBFNN) for the development of localization framework in WSNs. Using Received Signal Strength Indicator (RSSI) determination of the location a static sensor node on 100 100 m 2 grids from three anchor nodes with fixed positions. The simulation results effectually signify that MLPNN has better localization accuracy as compared to RBFNN.

The productivity value is a critical performance assessment criterion for Competency Certification Institutes LSK for electrical
engineering personnel in Indonesia, overseen by the Directorate General of Electricity DJK. This study aims to predict the productivity of
competency certificates issued by PT Eleska Iatki using an Artificial Neural Network ANN with the Backpropagation algorithm. Accurate
predictions are essential for developing business strategies and maintaining high performance amidst competitive pressures. Data from
2018 to 2022, consisting of certificate issuance records, were utilized. The studys methodology included data collection, preprocessing,
prediction modeling, and accuracy testing, resulting in a prediction accuracy of 91.3 with a Mean Absolute Percentage Error MAPE of
9.7

Fault diagnosis systems have an important role in industrial plants because the early fault detection and isolation (FDI) can minimize damages in the plants. The main aim of this work is to propose a two-stage neuro-fuzzy approach as a fault diagnosis system in dynamic processes. The first stage of the system is responsible for fault detection and is implemented using a neuro-fuzzy (N-F) model. The second stage of the system is responsible for fault isolation and is built using an hierarchical structure of fuzzy neural networks. The FDI system is applied to fault diagnosis in the actuators of one sugar factory.

There is multiple databases contain datasets of TP53 gene and its tumor protein P53 which believed to be involved in over 50% of human cancers cases, these databases are rich as datasets covered all mutations caused diseases (cancers), but they haven't efficient mining method can classify and diagnosis mutations patient's then predict the cancer of that patient. This paper proposed a novel mining of cancer via mutations because there is no mining method before offers friendly, effective and flexible predict or diagnosis of cancers via using whole common database of TP53 gene (tumor protein P53) as dataset and selecting a minimum number of fields in training and testing quick propagation algorithm which supporting this miming method. Simulating quick propagation network for the train dataset shows results the Correlation (0.9999), R-squared (0.9998) and mean of Absolute Relative Error (0.0029), while the training for the ALL datasets (train, test and validation dataset) have results the Correlation (0.9993), R-squared (0.9987) and mean of Absolute Relative Error (0.0057).

This paper is an attempt to survey the applications of computational intelligence techniques for predicting crude oil prices over a period of ten years. The purpose of this research is to provide an exhaustive overview of the existing literature which may assist prospective researchers. The reviewed literature covers a spectrum of publications on the proposed model, source of experimental data, period of data collection, year of publication and contributors. The overall trend of the publications in this area of research issued within the last decade is also addressed. The existing body of research has been analyzed and new research directions have been outlined that have been previously ignored. It is expected that researchers across the globe may thus be encouraged to redirect their attention and resources in order to keep on searching for an optimum solution.

The energy crisis hitting the Riau Province affects the economic impairment of society. One of the reason is that the country always depends on fossil energy resource. Government efforts to overcome the energy crisis has been done by replacing fossil energy with renewable alternative energy from palm oil waste. The production and productivity of palm oil in Riau Province has the highest rank in Indonesia, this becomes a future picture about realization of alternative energy in Riau. This research predicts on the future production and productivity by using Support Vector Regression. The results of experiments showed that the prediction value for the future have declined by 52% and increased only 5 out of the 74 locations at 8%. The best SVR kernel of this combination experiment are Radial Basis Function with a coefficient of determination (R2) at 95% and an error value galat (MSE) at 6% in fold 1 ranging from γ = 2 0 and C 2 3 . Research result for percentage prediction of productifi...

In this research article, an attempt has been made to predict the thermophysical properties of moist air in a solar still cavity with the help of Artificial Neural Network (ANN) modelling. Six training algorithms have been used to train, test, and validate the ANN model (viz. OSS, CGP, CGF, RP, SCG, and LM). Water and inner glass cover temperatures were selected as the input parameters whereas, the model output are: thermal conductivity, partial vapour pressure at water & glass surface, thermal conductivity, volumetric expansivity, specific heat, latent heat of vaporization, and dynamic viscosity. The findings have revealed that the proposed ANN model can be used to predict the thermophysical properties of moist air with excellent accuracy. The results of ANN model were tested against the well-established relations of Tsilingiris. Out of all the training algorithms used LM was found to be the best in all the stages of ANN modelling, as the results are well within an accuracy level of more than 95%. Hence, the developed LM algorithm-based ANN model is one of the most suitable algorithm for the prediction of the thermophysical properties of moist air.

Process monitoring and fault diagnosis is of considerable interest from an industrial perspective. In this paper, the general applicability of intelligent methods, like self-organizing maps (SOM) and multilayer feedforward networks with backpropagation , for the identification of sensor failure on combine harvesters will be illustrated. Both neural network types showed comparable results in order to classify normal and faulty sensor conditions.

Salah satu unsur untuk menjadi penilaian akreditasi adalah ketepatan waktu lulusan siswa. Adanya siswa yang tidak aktif tentu akan mempengaruhi ketepatan waktu kelulusan. Prediksi kinerja siswa diperlukan untuk mencegah siswa yang tidak aktif. Algoritma KNN digunakan untuk memprediksi kinerja siswa dengan menggunakan metode klasifikasi. Penelitian ini untuk mengoptimalkan algoritma KNN untuk memprediksi kinerja siswa dengan metode klasifikasi. Penelitian yang telah dilakukan dengan menggunakan data Jurusan Teknik Informatika Politeknik Harapan Bersama menyimpulkan bahwa nilai K terbaik adalah 3, 6, dan 9 untuk mendapatkan prediksi terbaik. Hasil ini diperoleh dengan mencoba nilai K, 3 hingga 60. Nilai prediksi kemudian dibandingkan, hasil yang salah diprediksi dimana persentase terkecil adalah yang terbaik.

This research is focused on the development of statistical downscaling model using principle component analysis (PCA) as feature extraction cascade with multivariate regression as model prediction. Sea Surface Temperature (SST) and the General Circular Model (GCM) derived from 18 ensemble members of ECHAM5 model under AID scenario are used as predictors, and the September, October, November, and december (SOND) rainfall in each station in Indramayu as the response variables. SST domains were reduced using the PCA that explain 80%, 85%, and 95% of the data variability, which were divided into three sets, namely SST lag 1 (August), lag 2 (July), and lag 3 (June). GCM domain-sized 5x5 with 2.8x2.8 km resolution is reduced by PCA. The number of components that are taken are such that the variables must still explain 90% of original data variability. The new variables yielded by peA are then take as the input of the multivariate regression with 13 observation data (from 13 station) as the output. The regression parameters are estimated by using the least sum of square error criteria. By assigning 0.4 as the correlation boundary to select the domain SST, the SST lag 3 (June) yields the best regression model with a correlation of about 0.7. But if the limit is increased to 0.55, only the SST lag I (August) that meet, and produce a model with a correlation above 0.7.

Untuk mencapai target konservasi energi sebesar 17% di tahun 2025 pemerintah menerapkan aturan management energi bagi industri/penguna energi lebih besar sama dengan 6000 TOE. Di sektor bangunan gedung di Indonesia, konsumsi energi terbesar adalah penggunaan pendingin udara. Salah satu cara untuk melakukan penghematan dalam tata udara dengan menggunakan perangkat yang hemat listrik tanpa mengorbankan kenyamanan penghuni gedung. Penelitian ini menggunakan metode jaringan saraf tiruan untuk memprediksi beban pendingin optimum untuk suatu gedung berdasarkan 8 karakteristik bangunan. Model yang dibuat memberikan hasil yang baik dengan nilai nilai loss 0,4-1,1%

Las Redes de Neuronas de Base Radial (RNBR) se comportan muy bien en la aproximación de funciones, siendo su convergencia extremadamente rápida comparada con las redes de neuronas de tipo perceptrón multicapa. Sin embargo, el diseño de una RNBR para resolver un problema dado, no es sencillo ni inmediato, siendo el número de neuronas de la capa oculta de una Red de Base Radial (RBR) un factor crítico en el comportamiento de este tipo de redes. En este trabajo, el diseño de una RNBR está basado en la cooperación de n+1 agentes: n agentes, cada uno de ellos formado por una RBR, que denominamos agentes RBR y 1 agente árbitro. Estos n+1 agentes se organizan formando un Sistema Multiagente. El proceso de entrenamiento se distribuye entre los n agentes RBR, cada uno de los cuales tiene un número diferente de neuronas. Cada agente RBR se entrena durante un número determinado de ciclos (una etapa), cuando el agente árbitro le envía un mensaje. Todo el proceso está gobernado por el árbitro que decide cuál es el mejor agente RBR en cada etapa. El resultado de los experimentos muestra una importante reducción del número de ciclos de entrenamiento utilizando la estrategia multiagente propuesta, en lugar de una estrategia secuencial.

Rain is one of the 4 weather conditions on earth. The rain itself is sometimes erratic. For some people, rain is sometimes seen as an obstacle in carrying out their daily activities. Therefore, predictions about rain are needed. Predictions about rain really help people in carrying out their daily activities. The research aims to compare the predictive performance of SVM and ARIMA in forecasting rain. The data used in this case study research is data regarding daily weather for 10 years in the Albury area, Australia from 2008-2017 with a lot of 3040 data. The results obtained from SVM for forecasting rain using daily weather data for 10 years in the Albury area, Australia with the best accuracy rate with the SVM model is 97.532% with an error rate of 2.468%, while in the ARIMA model, the results are MAE 0.181, RMSE 0.254 and MAPE 0.159. So it can be concluded that the ARIMA model has a better performance in predicting rain than the SVM method.

This paper seeks to develop a single pass closed form solution to the multipass training problem on MLPs. It works for any local transfer function such as tanh(), ReLo (), sig() , or global functions such as polynomials, trigonometrics, or series decomposition, requiring only that there exist a well defined inverse of the neuron transfer function. The close form will permit a number of well defined methods for solving the learning phase with priority to speed or stability for large datasets. The algorithms, known as pseudoinverses, are present in a variety of systems and languages and quite mature.

Weather information has an important role in human life in various fields, such as agriculture, marine, and aviation. The accurate weather forecasts are needed in order to improve the performance of various fields. In this study, use artificial neural network method with backpropagation learning algorithm to create a model of weather forecasting in the area of ??South Bali. The aim of this study is to determine the effect of the number of neurons in the hidden layer and to determine the level of accuracy of the method of artificial neural network with backpropagation learning algorithm in weather forecast models. Weather forecast models in this study use input of the factors that influence the weather, namely air temperature, dew point, wind speed, visibility, and barometric pressure.The results of testing the network with a different number of neurons in the hidden layer of artificial neural network method with backpropagation learning algorithms show that the increase in the numbe...

The current paper illustrates the application of computational intelligence tools in slope performance prediction both in static and dynamic conditions. We present the results obtained by using the back-propagation algorithm, the theory of Bayesian neural networks and the Kohonen self-organizing maps, one of the most realistic models of the biological brain functions. We estimate slope stability controlling variables by combining computational intelligence tools with generic interaction matrix theory. Our emphasis is given to the prediction and estimation of the following: slope stability, coefficient of critical acceleration, earthquake induced displacements, unsaturated soil classification, classification according to the status of stability and failure mechanism for dry and wet slopes. Finally, we present an integrated methodology for assessing landslide hazard coupling computational intelligence tools and geographical information systems.

Un importante numero de Contingencias simuladas en la Evaluacion de la Seguridad Dinamica de un Sistema de Potencia (ESDSP), no afectan de manera significativa las variables de estado. Su exclusion del conjunto que requiere analizarse, permitiria una importante reduccion en los tiempos de computacion, posibilitando la Evaluacion En Linea (Tiempo Real) adoptando acciones correctivas solo sobre las Contingencias consideradas Criticas. El Metodo clasico de Seleccion de Contingencias Criticas resulta de un Analisis Fuera de Linea, que cubre escenarios tipicos considerando aspectos tales como: frecuencia del sistema, tensiones de barra y angulos internos de los generadores. Sobre tal seleccion, el operador experto implementa las acciones correctivas pertinentes. En este trabajo es propuesto un nuevo Modelo Soft-Computing , que identifica Contingencias Criticas en Linea para la ESDSP, soportado en Redes Neuronales de Retropropagacion Simple y Multiple (RNRS/RNRM), Conjuntos Difusos y MatH...

The increase in utilisation of mobile location-based services for commercial, safety and security purposes among others are the key drivers for improving location estimation accuracy to better serve those purposes. This paper proposes the application of Levenberg Marquardt training algorithm on new robust multilayered perceptron neural network architecture for mobile positioning fitting for the urban area in the considered GSM network using received signal strength (RSS). The key performance metrics such as accuracy, cost, reliability and coverage are the major points considered in this paper. The technique was evaluated using real data from field measurement and the results obtained proved the proposed model provides a practical positioning that meet Federal Communication Commission (FCC) accuracy requirement.

Abstract: Source Localization is a very well established technique that has a wide range of applications from remote sensing to the Global Positioning System. Sound source localization techniques are used in commercial applications like improving speech quality in hands free telephony, video conferencing to military applications like SONAR, surveillance systems and devices to locate the sources of artillery fire. A method is proposed to localize an acoustic source within a frequency band from 100Hz to 4 KHz in two dimensions using microphone array by calculating the direction of arrival (DOA) of the acoustic signals. Direction of arrival (DOA) estimation of acoustic signals using a set of spatially separated microphones uses the phase information present in signals. For this the time-delays are estimated for each pair of microphones in the array. From the known array geometry and the direction of arrival, the location of source can be obtained.

This paper applies Neural Network to predict WTI crude oil futures prices on basis of intraday minutely high frequency data. We looked into the recent market crash in the WTI crude oil futures market in April before the Delivery day. The results indicate that Neural Network could be misleading. More specifically, the paper shows that in normal situations Neural Network works well in sample and out of sample but it could give predictions with the opposite signs when the there exists a crash such as the one happened on April 20th, 2020. The evidence demonstrates that the prediction based on Neural Network may not be suitable to predict the market crash which is due to extreme shocks or financial or economic crises. This study gives a new insight in the relation between short term price discovery and the extreme market crash movement.

Dalam tulisan ini dibahas mengenai pengenalan nomor plat kendaraan berbasis foto digital dengan metode moment invariant dan jaringan syaraf tiruan menggunakan metode algoritma backpropagatioan. Metode moment invariant dan jaringan syaraf tiruan digunakan untuk ekstraksi citra karakter yang kemudian diimplementasikan dengan Matlab untuk mengenal nomor plat kendaraan menggunakan algoritma backpropagation dengan multi layer. Kata Kunci : Moment invariant, nomor plat kendaraan, citra karakter, jaringan syaraf tiruan, algoritma backpropagation, multi layer. tiruan, algoritma backpropagation, multi layer.

Artificial Neural Networks (ANNs) have been widely used to determine future demand for power in the short, medium, and long terms. However, research has identified that ANNs could cause inaccurate predictions of load when used for longterm forecasting. This inaccuracy is attributed to insufficient training data and increased accumulated errors, especially in long-term estimations. This study develops an improved ANN model with an Adaptive Backpropagation Algorithm (ABPA) for best practice in the forecasting long-term load demand of electricity. The ABPA includes proposing new forecasting formulations that adjust/adapt forecast values, so it takes into consideration the deviation between trained and future input datasets' different behaviours. The architecture of the Multi-Layer Perceptron (MLP) model, along with its traditional Backpropagation Algorithm (BPA), is used as a baseline for the proposed development. The forecasting formula is further improved by introducing adjustment factors to smooth out behavioural differences between the trained and new/future datasets. A computational study based on actual monthly electricity consumption inputs from 2011 to 2020, provided by the Iraqi Ministry of Electricity, is conducted to verify the proposed adaptive algorithm's performance. Different types of energy consumption and the electricity cut period (unsatisfied demand) factor are also considered in this study as vital factors. The developed ANN model, including its proposed ABPA, is then compared with traditional and popular prediction techniques such as regression and other advanced machine learning approaches, including Recurrent Neural Networks (RNNs), to justify its superiority amongst them. The results reveal that the most accurate long-term forecasts with the minimum Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) values of (1.195.650) and (0.045), respectively, are successfully achieved by applying the proposed ABPA. It can be concluded that the proposed ABPA, including the adjustment factor, enables traditional ANN techniques to be efficiently used for longterm forecasting of electricity load demand. Keywords MLP neural networks Á Load demand Á Long-term forecasting Á Adaptive backpropagation Á Linear regression Á Radial basis function networks Á Recurrent neural networks

Back propagation Algorithm is a technique to train Artificial Neural Networks to calculate the gradient of the weights to propagation networks. Open used to produce more efficient neural networks. This technique executes the algorithm in parallel. This paper summarizes the basic Back the serial code. like code profiling, code optimization are being used. Also, the need to perform benchmarking is required to check the relative performance of the program on different system architectures.

In recent years, there has been a lot of research related to pattern recognition is conducted to identify various forms of patterns and controlling system. Utilizing backpropagation neural network in pattern identifying is very useful to solve problems with unknown parameter and difficult to determined. And then the data of the pattern are trained and tested. The results obtained from the recognition rate indicates a backpropagation neural network, provide excellent performance, which is an average of 98%. This neural network is expected to be developed by other researchers for the advancement of knowledge in all fields.

There are various types of wheat scattered in the world. Usually it takes a long time to recognize the type of wheat seed by manual method because wheat germ has a physical appearance that looks the same as others. One method that can be used is an Artificial Neural Network. In this study, the data used were secondary data which consisted of data from the variable physical characteristics of wheat germ. The types of wheat seeds that are classified are 3. The Artificial Neural Network architecture used in this study is 5. By comparing the 5 Artificial Neural Network architectures, it is concluded that the architecture consisting of 3 layers and 4 layers is more precise in the classification of wheat germ types. The accuracy obtained by the 2 Artificial Neural Network architectures is 90% and 90%, respectively.

Weather information has an important role in human life in various fields, such as agriculture, marine, and aviation. The accurate weather forecasts are needed in order to improve the performance of various fields. In this study, use artificial neural network method with backpropagation learning algorithm to create a model of weather forecasting in the area of ??South Bali. The aim of this study is to determine the effect of the number of neurons in the hidden layer and to determine the level of accuracy of the method of artificial neural network with backpropagation learning algorithm in weather forecast models. Weather forecast models in this study use input of the factors that influence the weather, namely air temperature, dew point, wind speed, visibility, and barometric pressure.The results of testing the network with a different number of neurons in the hidden layer of artificial neural network method with backpropagation learning algorithms show that the increase in the numbe...

In this paper, we investigated an ensemble neural network for the prediction of oil prices. Daily data from 1999 to 2012 were used to predict the West Taxes, Intermediate. Data were separated into four phases of training and testing using different percentages and obtained seven sub-datasets after implementing different attribute selection algorithms. We used three types of neural networks: Feed forward, Recurrent and Radial Basis Function networks. Finally a good ensemble neural network model is formulated by the weighted average method. Empirical results illustrated that the ensemble neural network outperformed other models.

Las Redes de Neuronas de Base Radial (RNBR) se comportan muy bien en la aproximación de funciones, siendo su convergencia extremadamente rápida comparada con las redes de neuronas de tipo perceptrón multicapa. Sin embargo, el diseño de una RNBR para resolver un problema dado, no es sencillo ni inmediato, siendo el número de neuronas de la capa oculta de una Red de Base Radial (RBR) un factor crítico en el comportamiento de este tipo de redes. En este trabajo, el diseño de una RNBR está basado en la cooperación de n+1 agentes: n agentes, cada uno de ellos formado por una RBR, que denominamos agentes RBR y 1 agente árbitro. Estos n+1 agentes se organizan formando un Sistema Multiagente. El proceso de entrenamiento se distribuye entre los n agentes RBR, cada uno de los cuales tiene un número diferente de neuronas. Cada agente RBR se entrena durante un número determinado de ciclos (una etapa), cuando el agente árbitro le envía un mensaje. Todo el proceso está gobernado por el árbitro que decide cuál es el mejor agente RBR en cada etapa. El resultado de los experimentos muestra una importante reducción del número de ciclos de entrenamiento utilizando la estrategia multiagente propuesta, en lugar de una estrategia secuencial.

In this research a prediction model for the cellular uptake efficiency of nanoparticles (NPs), which is the rate that NPs adhere to a cell surface or enter a cell, is investigated via an artificial neural network (ANN) method. An appropriate mathematical model for the prediction of the cellular uptake rate of NPs will significantly reduce the number of time-consuming experiments to determine which of the thousands of possible variables have an impact on NP uptake rate. Moreover, this study constitutes a basis for targeted drug delivery and cell-level detection, treatment and diagnosis of existing pathologies through simulating NP-cell interactions. Accordingly, this study will accelerate nanomedicine research. Our research focuses on building a proper ANN model based on a multilayered feed-forward back-propagation algorithm that depends on NP type, size, surface charge, concentration and time for prediction of cellular uptake efficiency. The NP types for in-vitro NP-healthy cell interaction analysis are polymethyl methacrylate (PMMA), silica and polylactic acid (PLA), all of whose shapes are spheres. The proposed ANN model has been developed on MATLAB Programming Language by optimizing a number of hidden layers (HLs), node numbers and training functions. The datasets are obtained from in-vitro NP-cell interaction experiments conducted by Nanomedicine and Advanced Technology Research Center. The dispersion characteristics and cell interactions with different NPs in organisms are explored using an optimal ANN prediction model. Simulating the possible interactions of targeted NPs with cells via an ANN model will be faster and cheaper compared to the excessive experimentation currently necessary.

Intensitas curah hujan dikatakan besar apabila  hujan lebat dan kondisi ini sangat berbahaya karena  dapat menimbulkan banjir dan longsor, untuk itu perlu dilakukan peramalan untuk memperkirakan seberapa besar curah hujan yang akan datang. Metode Jaringan Saraf Tiruan (JST) adalah paradigma pengolahan informasi yang terinspirasi oleh sistem saraf secara biologis, seperti pada proses informasi pada otak manusia. Metode JST yang digunakan dalam meramal curah hujan pada penelitian ini adalah metode Backpropagation dan Adaline. Hasil peramalan dengan tingkat kesalahan yang lebih kecil dari kedua metode JST tersebut akan menunjukkan bahwa metode tersebut baik digunakan untuk peramalan. Berdasarkan pengujian yang telah dilakukan pada iterasi 1000 dihasilkan RMSE dengan metode Backpropagation  sebesar 0.0435, sedangkan Adaline sebesar 0.0674. Berdasarkan perbandingan nilai RMSE metode Backpropagation lebih baik  dibandingkan dengan metode Adaline

PT Gaikindo adalah sebuah perusahaan otomotif dan produsen kendaraan bermotor yang mengirimkan produknya hingga ke luar negeri. Berdasarkan data ekspor mobil pada  PT Gaikindo, terdapat banyak mobil dengan jumlah ekspor yang bervariasi. Oleh karena itu, PT. Gaikindo membutuhkan data mobil mana yang terlaris dan yang tidak laris untuk mengatur volume pengiriman mobil ke luar negeri. Penelitian ini menggunakan metode clustering dengan algoritma k-means. Metode ini mengelompokkan data dengan cara memasukkan data brand dan tipe mobil yang dikirim pada setiap negara. Hasil penelitian ini menunjukkan terdapat 3 cluster yang terbentuk dengan kategori sangat diminati, cukup diminati, dan sedikit diminati.

This paper describes a neural network speech enhancement system using Multilayer Perceptron (MLP) network and trained using the back propagation algorithm (BPA). Speech enhancement is generally refers to map noisy speech into cleaner speech. Noisy speech signals are obtained by adding random noise to the clean signals .Speech enhancement is then performed on the noisy signals by using the ADALINE. Here we show that neural nets can be used to significantly boost recognition accuracy, without retraining the speech recognizer.

Landslides are one of the dangerous natural phenomena that hinder the development in Penang Island, Malaysia. Therefore, finding the reliable method to predict the occurrence of landslides is still the research of interest. In this paper, two models of artificial neural network, namely, Multilayer Perceptron (MLP) and Cascade Forward Neural Network (CFNN), are introduced to predict the landslide hazard map of Penang Island. These two models were tested and compared using eleven machine learning algorithms, that is, Levenberg Marquardt, Broyden Fletcher Goldfarb, Resilient Back Propagation, Scaled Conjugate Gradient, Conjugate Gradient with Beale, Conjugate Gradient with Fletcher Reeves updates, Conjugate Gradient with Polakribiere updates, One Step Secant, Gradient Descent, Gradient Descent with Momentum and Adaptive Learning Rate, and Gradient Descent with Momentum algorithm. Often, the performance of the landslide prediction depends on the input factors beside the prediction metho...

Curah hujan adalah informasi penting di bidang transportasi, pertanian, industri dll. Dengan mengetahui informasi curah hujan, tindakan dapat diambil secara tepat di beberapa bidang tersebut. sehingga tidak ada kerugian karena kesalahan dalam informasi curah hujan. Makalah ini bertujuan untuk menemukan metode yang sesuai dalam peramalan curah hujan yang terkait dengan metode pemrosesan data imputasi dan nilai parameter dalam Support Vector Regression (SVR). Hasil percobaan menunjukkan bahwa metode preprocessing data imputasi terbaik diperoleh untuk digunakan ke dalam SVR berdasarkan nilai Mean Squared Error (MSE) dan Mean Absolute Error (MAE). Berdasarkan hasil MSE, k-nearest neighbor adalah metode terbaik yang digunakan untuk preprocessing data imputasi. Data preprocessing menghasilkan eksperimen pada SVR Polinomial dengan parameter C 1000, toleransi 0,001, epsilon 0,01 dan iterasi tak terbatas. Di sisi lain, hasil MAE menunjukkan bahwa Artificial Neural Network (ANN) adalah metode...

Diseñar un sistema que permita controlar el riego de forma automática de los sembríos en la Granja “La Pradera”, aplicando técnicas de Aprendizaje Automático para ayudar a reducir el consumo innecesario de agua y lograr obtener productos de mejor calidad.Este proyecto describe el desarrollo de un sistema electrónico para automatizar el proceso de riego en cultivos basado en redes neuronales artificiales, para este caso el cultivo de muestra es de plantas de alfalfa. Un sistema de riego obedece a las condiciones ambientales tales como: temperatura ambiente, humedad relativa, humedad del suelo y luminosidad. El sistema hace uso de una red de sensores implementada en (Domínguez, 2020) donde se recolectan datos de variables ambientales mencionadas anteriormente; estos datos van a ser utilizados primeramente en el proceso de entrenamiento y aprendizaje de la red neuronal artificial y posteriormente, servirán para poner en funcionamiento el algoritmo de aprendizaje automático en tiempo re...

Indonesia terkenal dengan Negara yang beriklim tropis. Selain dari itu, Indonesia memiliki daratan yang luas tetapi memiliki luas laut yang tidak kalah luas dari daratannya, selain itu Indonesia tentunya memiliki curah hujan yang cukup tinggi, dilihat dari sekeliling Indonesia adalah lautan yang luas, sehingga proses penguapan dan pengumpulan awan-awan semakin cepat dan proses hujan pun akan terjadi dapat setiap hari. Negara tropis selalu memprediksi tiga kemungkinan, yakni : hujan, berawan dan panas/cerah. Curah hujan didapat menggunakan bantuan alat yang bernama automatic weather stasion. Alat tersebut disimpan di beberapa titik di pulau Batam, di antaranya adalah : Nongsa, Mukakuning, Jembatan 2 Barelang, Sekupang, Pagoda, Sei Beduk, Sengkuang, Punggur, dan Batu Ampar. Sehingga data yang di dapat dari setiap titik yang ada, dapat membantu petugas kantor stasiun meteorologi Hang Nadim Batam menginformasikan ke semua orang yang membutuhkan data tersebut. Jaringan syaraf tiruan bany...

Teknologi sistem jaringan syaraf tiruan (JST) merupakan salah satu paradigma untuk sistem cerdas. JST dapat diaplikasikan pada banyak bidang seperti pengenalan pola, identifikasi, klasifikasi, dan sistem kendali. Tujuan utama dari studi ini adalah untuk memprediksi jumlah kasus penderita demam berdarah dengue (DBD) berdasarkan pada parameter curah hujan, kelembaban, suhu udara dan jumlah kasus yang terjadi di wilayah kabupaten Sleman dalam kurun waktu tahun 2005 sampai dengan bulan Mei 2009. Hasil simulasi menunjukkan bahwa secara umum model prediksi dengan mennggunakan data latih mempunyai fit yang cukup baik. Hal ini dapat terlihat bahwa prediksi jumlah penderita dari tahun 2005 sampai 2007 mempunyai nilai MSE 99.94x10-5 dengan koefisien korelasi r sebesar 0.99, sedangkan hasil prediksi untuk tahun 2008 sampai dengan bulan Mei 2009 mempunyai nilai MSE sebesar 99.71 x10-5 dengan nilai korelasi r adalah 0.989. Kata kunci-DBD, Prediksi, JST

This paper is an attempt to survey the applications of computational intelligence techniques for predicting crude oil prices over a period of ten years. The purpose of this research is to provide an exhaustive overview of the existing literature which may assist prospective researchers. The reviewed literature covers a spectrum of publications on the proposed model, source of experimental data, period of data collection, year of publication and contributors. The overall trend of the publications in this area of research issued within the last decade is also addressed. The existing body of research has been analyzed and new research directions have been outlined that have been previously ignored. It is expected that researchers across the globe may thus be encouraged to redirect their attention and resources in order to keep on searching for an optimum solution.

Perpustakaan sebagai sarana sumber informasi dan ilmu pengetahuan untuk menyimpan bahan pustaka yang dipakai oleh pemakai untuk menggali ilmu sumber informasi. Penelitian dilakukan pada salah satu Perpustakaan yang ada di kota Batam. Perpustakaan ini memiliki beragam koleksi buku seperti buku umum, karya ilmiah, bahasa, sejarah, dan lain sebagainya. Permasalahan yang sering terjadi adalah buku yang dipinjam kadang tidak tersedia, selain itu pihak perpustakaan juga mengalami kesulitan karena tidak tau berapa jumlah buku yang dipinjam sehingga pihak Perpustakaan melihat kembali catatan transaksi meminjam buku pada buku tamu. Untuk itu dibuatlah suatu sistem dengan pengolahan jumlah data yang besar dengan teknik data mining metode k-means. Dari hasil yang diperoleh data peminjam buku yang telah diproses mendapatkan buku yang banyak dipinjam terdapat pada cluster 1 sebanyak 9 item, buku yang paling sedikit dipinjam terdapat pada cluster 2 sebanyak 15 item, buku yang cukup banyak dipinja...

In this paper, the author introduces a classification approach using  Artificial Neural Network(ANN) with Back-Propagation learning technique for human diseases like Cancer and heart problems from clinical diagnosis data. Clinical diagnosis is done mostly by experienced doctors with expertise in this field. In many cases, the test results are not effective towards the diagnosis of the disease. The author is particular about the wrong diagnosis which leads to a wrong treatment. The author is using Artificial Neural Network technique to classify the disease with reduced number of DNA sequence. The accuracy is differing based on the training data set and validation data set.  The other major issue is the privacy preserving of the patients. As we are sharing the critical data from clinical diagnostic centers, there is good chance of patient’s anonymity is revealed. To avoid this, the author is using a simple Privacy Preserving in Data Mining (PPDM) technique to crypt the identity of...

The stock market has become a popular investment channel in recent years because of the low return rates of other investment. The stock price prediction is in the interest of both private and institution investors. Accurate forecasting of stock prices is an appealing yet difficult activity in the business world. Therefore, stock prices forecasting is regarded as one of the most challenging topics in business. The forecasting techniques used in the literature can be classified into two categories: linear models and non linear models. One of forecasting techniques in nonlinear models is support vector regression (SVR). Basically, SVR adopts the structural risk minimization principle to estimate a function by minimizing an upper bound of the generalization. The optimal parameters of SVR can be use Grid Search Algorithm method. Concept of this method is using cross validation (CV). In this paper, the SVR model use linear kernel function. The accurate prediction of stock price, in telecommunication, is 92.47% for training data and 83.39% for testing data.

Pengangguran terbuka (Open Unemployment) adalah penduduk usia kerja yang tidak mempunyai pekerjaan apapun, yang secara aktif mencari pekerjaan. Backpropagation adalah salah satu algoritma pada jaringan syaraf tiruan (JST) yang digunakan untuk meramalkan atau prediksi data. Dalam penelitian ini akan diramalkan Tingkat Pengangguran Terbuka (TPT) di Provinsi Maluku. Berdasarkan hasil analisis pelatihan  diperoleh rata-rata keberhasilan permalan  sebesar 92.59%  dengan error terkecil yaitu MAPE = 0.0741%. Hasil peramalan 5 tahun ke depan menunjukan data fluktuatif dari tahun 2019 (9.5134) sampai tahun 2023 (9.7924).

Untuk mencapai target konservasi energi sebesar 17% di tahun 2025 pemerintah menerapkan aturan management energi bagi industri/penguna energi lebih besar sama dengan 6000 TOE. Di sektor bangunan gedung di Indonesia, konsumsi energi terbesar adalah penggunaan pendingin udara. Salah satu cara untuk melakukan penghematan dalam tata udara dengan menggunakan perangkat yang hemat listrik tanpa mengorbankan kenyamanan penghuni gedung. Penelitian ini menggunakan metode jaringan saraf tiruan untuk memprediksi beban pendingin optimum untuk suatu gedung berdasarkan 8 karakteristik bangunan. Model yang dibuat memberikan hasil yang baik dengan nilai nilai loss 0,4-1,1%

In recent years, there has been a lot of research related to pattern recognition is conducted to identify various forms of patterns and controlling system. Utilizing backpropagation neural network in pattern identifying is very useful to solve problems with unknown parameter and difficult to determined. And then the data of the pattern are trained and tested. The results obtained from the recognition rate indicates a backpropagation neural network, provide excellent performance, which is an average of 98%. This neural network is expected to be developed by other researchers for the advancement of knowledge in all fields.

MANET is collection of mobile nodes that form a network independently of any centralized administration. Since those mobile devices are battery operation based and extending the battery lifetime has become an main aim. Generally all the researchers have recently started to consider power-aware development of efficient protocols for MANETs. When each mobile node in a MANETs performs the routing function for effective establishment of communication among different mobile nodes the "Death" of even an any of the nodes due to power