Perbandingan Model Transfer Function Dan Model Neural Network Untuk Prediksi Banyak Kasus Demam Berdarah Di Kota Malang

2021 ◽  
Vol 11 (1) ◽  
pp. 1-9
Author(s):  
Nanta Sigit ◽  
Ida Ayu P K
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Transfer Function ◽  
Model Transfer ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Model Transfer Function

ABSTRAK Kota Malang adalah salah satu kota yang dinyatakan sebagai daerah endemis demam berdarah. Pada tahun 2015, jumlah penderita demam berdarah sebanyak 1629 kasus dengan jumlah kematian 13 orang. Ada banyak faktor yang berkontribusi menyebabkan penyakit, begitu juga dengan penyakit demam berdarah. Faktor-faktor tersebut berasal dari individu sendiri maupun dari lingkungan. Beberapa faktor yang terkait dalam penularan demam berdarah antara lain kepadatan penduduk, mobilitas penduduk, kualitas perumahan dan sikap hidup. Sedangkan faktor yang dapat memicu terjadinya demam berdarah adalah faktor lingkungan yang termasuk di dalamnya perubahan suhu, kelembaban udara, dan curah hujan yang mengakibatkan nyamuk lebih sering bertelur dan virus dengue berkembang biak dengan cepat. Parasit dan pembawa penyakit (nyamuk) sangat peka terhadap faktor iklim, khususnya suhu, curah hujan, kelembaban, permukaan air, dan angin. Oleh karena itu, penelitian ini bertujuan untuk mengembangkan suatu model yang  sesuai untuk peramalan demam berdarah dikota malang berdasarkan Transfer Function dan ANN. Data yang digunakan adalah Data demam berdarah tahun 2014 sampai 2019. Hasil penelitian menunjukkan bahwa nilai RMSE, MAPE, dan SMAPE yang terkecil dari kedua model tersebut adalah model Artificial Neural Network. Kata Kunci : Artificial Neural Network (ANN), Transfer Function, dan Demam Berdarah

Perbandingan Model Transfer Function Dan Model Neural Network Untuk Prediksi Banyak Kasus Demam Berdarah Di Kota Malang

2021 ◽  
Vol 11 (1) ◽  
pp. 1-9
Author(s):  
Nanta Sigit ◽  
Ida Ayu P K
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Transfer Function ◽  
Model Transfer ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Model Transfer Function

ABSTRAK Kota Malang adalah salah satu kota yang dinyatakan sebagai daerah endemis demam berdarah. Pada tahun 2015, jumlah penderita demam berdarah sebanyak 1629 kasus dengan jumlah kematian 13 orang. Ada banyak faktor yang berkontribusi menyebabkan penyakit, begitu juga dengan penyakit demam berdarah. Faktor-faktor tersebut berasal dari individu sendiri maupun dari lingkungan. Beberapa faktor yang terkait dalam penularan demam berdarah antara lain kepadatan penduduk, mobilitas penduduk, kualitas perumahan dan sikap hidup. Sedangkan faktor yang dapat memicu terjadinya demam berdarah adalah faktor lingkungan yang termasuk di dalamnya perubahan suhu, kelembaban udara, dan curah hujan yang mengakibatkan nyamuk lebih sering bertelur dan virus dengue berkembang biak dengan cepat. Parasit dan pembawa penyakit (nyamuk) sangat peka terhadap faktor iklim, khususnya suhu, curah hujan, kelembaban, permukaan air, dan angin. Oleh karena itu, penelitian ini bertujuan untuk mengembangkan suatu model yang  sesuai untuk peramalan demam berdarah dikota malang berdasarkan Transfer Function dan ANN. Data yang digunakan adalah Data demam berdarah tahun 2014 sampai 2019. Hasil penelitian menunjukkan bahwa nilai RMSE, MAPE, dan SMAPE yang terkecil dari kedua model tersebut adalah model Artificial Neural Network.   Kata Kunci : Artificial Neural Network (ANN), Transfer Function, dan Demam Berdarah

Simultaneous Quantitative Analysis of Clorsulon and Ivermectin in a Veterinary Formulation by Artificial Neural Network

2008 ◽  
Vol 59 (10) ◽  
Author(s):  
Gozde Pektas ◽  
Erdal Dinc ◽  
Dumitru Baleanu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Transfer Function ◽  
Back Propagation ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

Simultaneaous spectrophotometric determination of clorsulon (CLO) and invermectin (IVE) in commercial veterinary formulation was performed by using the artificial neural network (ANN) based on the back propagation algorithm. In order to find the optimal ANN model various topogical networks were tested by using different hidden layers. A logsig input layer, a hidden layer of neurons using the logsig transfer function and an output layer of two neurons with purelin transfer function was found suitable for basic configuration for ANN model. A calibration set consisting of CLO and IVE in calibration set was prepared in the concentration range of 1-23 �g/mL and 1-14 �g/mL, repectively. This calibration set contains 36 different synthetic mixtures. A prediction set was prepared in order to evaluate the recovery of the investigated approach ANN chemometric calibration was applied to the simultaneous analysis of CLO and IVE in compounds in a commercial veterinary formulation. The experimental results indicate that the proposed method is appropriate for the routine quality control of the above mentioned active compounds.

scholarly journals Prediction of Dengue Fever Cases in Malang City using a Neural Network Model

2021 ◽  
Author(s):  
Nanta Sigit ◽  
Ida Ayu P K
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Transfer Function ◽  
Dengue Fever ◽  
Climatic Factors ◽  
Water Levels ◽  
Suitable Model ◽  
Population Mobility ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Dengue fever has been declared endemic in many cities of Indonesia, one of them being the Malang City. In 2015, the incidence of dengue fever in the region was recorded at 1,629 with 13 deaths. There are many factors that contribute to the disease. The factors associated with dengue-fever transmission include population density, population mobility, quality of housing and attitude of life. However, the factors that can trigger dengue fever are environmental in nature, and include changes in temperature, humidity and rainfall, which cause mosquitoes to lay eggs more often and facilitates a rapid reproduction of the dengue virus. Parasites and disease carriers (mosquitoes) are very sensitive to climatic factors, especially temperature, rainfall, humidity, water levels and wind. Therefore, this study aimed to develop a suitable model for forecasting dengue fever in Malang City based on the Transfer Function and Artificial Neural Network (ANN). Data used were dengue fever data from 2004 to 2019. The results showed that the smallest RMSE, MAPE and SMAPE values of the two models were ANN models. Keywords: Artificial Neural Network (ANN), transfer function, dengue fever

scholarly journals PREDICTION OF THE SIZE OF NANOPARTICLES AND MICROSPORE SURFACE AREA USING ARTIFICIAL NEURAL NETWORK

2018 ◽  
Vol 1 (1) ◽  
pp. 65
Author(s):  
Dženana Sarajlić ◽  
Layla Abdel-Ilah ◽  
Adnan Fojnica ◽  
Ahmed Osmanović
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Transfer Function ◽  
Surface Area ◽  
Network Architecture ◽  
Neural Network Architecture ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer ◽  
Experimental Values

This paper presents development of Artificial Neural Network (ANN) for prediction of the size of nanoparticles (NP) and microspore surface area (MSA). Developed neural network architecture has the following three inputs: the concentration of the biodegradable polymer in the organic phase, surfactant concentration in the aqueous phase and the homogenizing pressure. Two-layer feedforward network with a sigmoid transfer function in the hidden layer and a linear transfer function in the output layer is trained, using Levenberg-Marquardt training algorithm. For training of this network, as well as for subsequent validation, 36 samples were used. From 36 samples which were used for subsequent validation in this ANN, 80,5% of them had highest accuracy while 19,5% of output data had insignificant differences comparing to experimental values.

Application of the Artificial Neural Network (ANN) Method as MPPT Photovoltaic for DC Source Storage

2019 ◽  
Vol 12 (3) ◽  
pp. 145 ◽  
Author(s):  
Epyk Sunarno ◽  
Ramadhan Bilal Assidiq ◽  
Syechu Dwitya Nugraha ◽  
Indhana Sudiharto ◽  
Ony Asrarul Qudsi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

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

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.

Detection of Biomedical Images by Using Bio-inspired Artificial Intelligent

2020 ◽  
Vol 38 (2A) ◽  
pp. 255-264
Author(s):  
Hanan A. R. Akkar ◽  
Sameem A. Salman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Square Error ◽  
Average Rate ◽  
Mean Square ◽  
Performance Measurements ◽  
Artificial Intelligent ◽  
Average Mean Square Error ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Computer vision and image processing are extremely necessary for medical pictures analysis. During this paper, a method of Bio-inspired Artificial Intelligent (AI) optimization supported by an artificial neural network (ANN) has been widely used to detect pictures of skin carcinoma. A Moth Flame Optimization (MFO) is utilized to educate the artificial neural network (ANN). A different feature is an extract to train the classifier. The comparison has been formed with the projected sample and two Artificial Intelligent optimizations, primarily based on classifier especially with, ANN-ACO (ANN training with Ant Colony Optimization (ACO)) and ANN-PSO (training ANN with Particle Swarm Optimization (PSO)). The results were assessed using a variety of overall performance measurements to measure indicators such as Average Rate of Detection (ARD), Average Mean Square error (AMSTR) obtained from training, Average Mean Square error (AMSTE) obtained for testing the trained network, the Average Effective Processing Time (AEPT) in seconds, and the Average Effective Iteration Number (AEIN). Experimental results clearly show the superiority of the proposed (ANN-MFO) model with different features.

Sign in / Sign up

Export Citation Format

Share Document