scholarly journals Penerapan Data Science Menggunakan Artificial Neural Network (ANN) Metode Self Organizing Mapping (SOM) untuk Klasifikasi Industri

WARTA AKAB ◽  
2021 ◽  
Vol 45 (2) ◽  
Author(s):  
Ismail Ismail
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Data Science ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Self Organizing

Artificial Neural Network (ANN) sebagai sebuah metode analitik dalam data science memiliki kemampuan pada bidang  klasifikasi, asosiasi, self organizing dan optimasi. Dengan kemampuan tersebut ANN dapat menciptakan suatu pola pengetahuan melalui pengaturan diri atau kemampuan belajar (self-organizing) bahkan mampu memetakan kumpulan data berdasarkan ciri-ciri kesamaan yang ada pada data tersebut dengan mekanisme Self Organizing Mapping (SOM). Kemampuan SOM dalam melakukan pemetaan terhitung cukup valid dan rendah bias, karena mengkalkulasi secara mandiri dan cepat berdasarkan kesamaan ciri atau karakter data yang diinput. Pada penelitian ini SOM diterapkan untuk pemetaan industi pada industri mesin pertanian dan kehutanan. Mengingat selama ini kekuatan industri mesin pertanian dan kehutanan dengan kode Kelompok Lapangan Usaha Industri (KLUI) 2921 belum dipetakan dengan sempurna. Data industri yang menjadi input pada program terlebih dahulu ditransformasi dengan aktivasi sigmoid biner dan  selanjutnya dimasukkan ke dalam program jaringan competitive kohonen. Berdasarkan perhitungan metode SOM dihasilkan klasifikasi industri dengan jenis menengah dan besar dengan capaian epoch 1000. Kata kunci: Data Science; Artificial Neural Network (ANN); Self Organizing Mapping (SOM); Klasifikasi Industri.

scholarly journals Self-Organizing and Error Driven (SOED) artificial neural network for smarter classifications

2017 ◽  
Vol 4 (4) ◽  
pp. 282-304 ◽  
Author(s):  
Ruholla Jafari-Marandi ◽  
Mojtaba Khanzadeh ◽  
Brian K. Smith ◽  
Linkan Bian
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Learning Styles ◽  
Health Care Systems ◽  
Self Organizing Map ◽  
Care Systems ◽  
Classification Tasks ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Self Organizing

Abstract Classification tasks are an integral part of science, industry, business, and health care systems; being such a pervasive technique, its smallest improvement is valuable. Artificial Neural Network (ANN) is one of the strongest techniques used in many disciplines for classification. The ANN technique suffers from drawbacks such as intransparency in spite of its high prediction power. In this paper, motivated by learning styles in human brains, ANN's shortcomings are assuaged and its prediction power is improved. Self-Organizing Map (SOM), an ANN variation which has strong unsupervised power, and Feedforward ANN, traditionally used for classification tasks, are hybridized to solidify their benefits and help remove their limitations. The proposed method, which we name Self-Organizing Error-Driven (SOED) Artificial Neural Network, shows significant improvements in comparison with usual ANNs. We show SOED is a more accurate, more reliable, and more transparent technique through experimentation with five different datasets. Highlights A synthesis of MLP and SOM is presented for tackling classification challenges. The superiority of SOED over MLP in addressing 5 classification tasks is presented. SOED is compared with other states of the art techniques such as DT, KNN, and SVM. It is shown that SOED is a more accurate and reliable in comparison with MLP. It is shown SOED is more accurate, reliable and transparent in comparison with MLP.

scholarly journals Comparison of Backpropagation and Kohonen Self Organising Map (KSOM) Methods in Face Image Recognition

2021 ◽  
Vol 7 (2) ◽  
pp. 149
Author(s):  
Lady Silk Moonlight ◽  
Fiqqih Faizah ◽  
Yuyun Suprapto ◽  
Nyaris Pambudiyatno
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Face Image ◽  
Self Organizing Map ◽  
Training Process ◽  
Face Images ◽  
Backpropagation Method ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Self Organizing

Background: Human face is a biometric feature. Artificial Intelligence (AI) called Artificial Neural Network (ANN) can be used in recognising such a biometric feature. In ANN, the learning process is divided into two: supervised and unsupervised learning. In supervised learning, a common method used is Backpropagation, while in the unsupervised learning, a common one is Kohonen Self Organizing Map (KSOM). However, the application of Backpropagation and KSOM need to be adjusted to improve the performance.Objective: In this study, Backpropagation and KSOM algorithms are rewritten to suit face image recognition, applied and compared to determine the effectiveness of each algorithm in solving face image recognition.Methods: In this study, the methods used and compared in the case of face image recognition are Backpropagation dan Kohonen Self Organizing Map (KSOM) Artificial Neural Network (ANN).Results: The smallest False Acceptance Rate (FAR) value of Backpropagation is 28%, and KSOM is 36%, out of 50 unregistered face images tested. While the smallest False Rejection Rate (FRR) value of Backpropagation is 22%, and KSOM is 30%, out of 50 registered face images. The fastest time for the training process using the backpropagation method is 7.14 seconds, and the fastest time for recognition is 0.71 seconds. While the fastest time for the training process using the KSOM method is 5.35 seconds, and the fastest time for recognition is 0.50 seconds.Conclusion: Backpropagation method is better in recognising face images than KSOM method, but the training process and the recognition process by KSOM method are faster than Backpropagation method due to the hidden layers. Keywords: Artificial Neural Network (ANN), Backpropagation, Kohonen Self Organizing Map (KSOM), Supervised learning, Unsupervised learning 

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.

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