scholarly journals Literature Review Klasifikasi Data Menggunakan Metode Cosine Similarity dan Artificial Neural Network

2021 ◽  
Vol 20 (2) ◽  
pp. 307
Author(s):  
Lely Meilina ◽  
I Nyoman Satya Kumara ◽  
I Nyoman Setiawan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Literature Review ◽  
Cosine Similarity ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Dampak positif yang ditimbulkan dari perkembangan teknologi salah satunya adalah kemudahan dalam menyampaikan aspirasi dan dalam mendapatkan informasi dengan sangat cepat. Manfaat dari perkembangan teknologi ini dapat dirasakan oleh semuassektor, termasuk sektor pemerintahan yang harus mengayomi masyarakat dan negara. Dalam meningkatkan kualitas pelayanan publik, pemerintah harus menerapkan pemerintahan yang berbasis teknologi informasi digital.  Oleh karena itu, Pemerintah pusat maupun daerah telah menyediakan layanan pengaduan masyarakat yang berbasis online. Untuk meningkatkan kualitas pelayanan maka sistem pengaduan online harus berjalan dengan optimal. Metode yang banyak digunakan untuk mencari kemiripan teks pengaduan adalah metode cosine similarity dan metode Artificial Neural Network (ANN) untuk klasifikasi data pengaduan. Penelitian ini mereview penerapan kedua metode tersebut untuk mengetahui tingkat akurasinya sebelum dapat di implementasikan pada sistem pengaduan online.  Hasil dari review menyatakan bahwa metode Cosine Similarity memiliki tingkat akurasi sebesar 71,5% dan ANN memiliki tingkat akurasi sebesar 77%. Sedangkan metode lainnya memiliki tingkat akurasi sebesar 67%. Dari presentase nilai tersebut dapat disimpulkan bahwa penggunaan metode Cosine Similarity dan ANN layak untuk digunakan dalam mengklasifikasikan data pada Sistem Pengaduan Masyarakat Online

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