scholarly journals Perancangan Sistem Pendukung Keputusan Untuk Prediksi Penerima Beasiswa Menggunakan Metode Neural Network Backpropagation

2018 ◽  
Vol 5 (2) ◽  
pp. 157 ◽  
Author(s):  
Ade Pujianto ◽  
Kusrini Kusrini ◽  
Andi Sunyoto
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Artificial Neural Network ◽  
Human Brain ◽  
Learning Rate ◽  
Neural Network Modeling ◽  
Complex Problems ◽  
Backpropagation Method ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

<p class="Judul21">Seleksi di Amikom masih mengalami kendala pada proses pengambilan keputusan, banyaknya data menyebabkan pengambil keputusan membutuhkan tools yang dapat membantu dalam menentukan penerima beasiswa, salah satu metode yang sering digunakan adalah artificial neural network (ANN). Metode ini meniru jaringan pemodelan saraf otak manusia berupa neuron-neuron untuk menyelesaikan suatu permasalahan. Salah satu penerapan neural network adalah untuk melakukan prediksi atau peramalan terhadap suatu peristiwa tertentu serta dianggap mampu menyelesaikan masalah yang komplek seperti penalaran otak manusia. Untuk menyelesaiakn masalah yang komplek neural network memerlukan banyak neuron atau yang biasa disebut layer (lapis). Salah satu metode neural network multi lapis adalah backpropagation yang mampu mengoptimalisasi bobot pada neuron dan menyelesaikan masalah yang komplek. Hasil dari penelitian ini adalah sebuah perancangan sistem prediksi dengan menggunakan metode neural network backpropagation untuk melakukan peramalan terhadap mahasiswa yang mendaftar beasiswa. hasil akhir penelitian ini adalah nilai akurasi sebesar 90% dan nilai error terkecil sebesar 0,000101 pada epoch ke 329 dengan jumlah 3000 data dengan pembagian data training 2.250 dan 750 data testing serta konfigurasi learning rate sebesar 0,2 dan momentum 0,2.</p><p class="Abstrak"> </p><p><strong>Kata kunci</strong>: <em>Artificial Neural netwok</em><em>, </em><em>Backpropagarion, </em><em>Prediksi, beasiswa, Pengambilan Keputusan.</em></p><p><em> </em></p><p class="Judul21"><em>Abstract</em></p><p class="Judul21"><em>Selection in Amikom is still constrained in the decision-making process, the number of data causing decision makers need tools that can assist in determining scholarship recipients, one of the most commonly used method is artificial neural network (ANN). This method mimics the neural network modeling of the human brain in the form of neurons to solve a problem. One application of neural network is to make predictions or forecasting of a particular event and is considered capable of solving complex problems such as human brain reasoning. To solve the problem the complex neural network requires many neurons or so-called layers. One method of multi layer neural network is backpropagation that is able to optimize the weight of neurons and solve complex problems. The result of this research is a prediction system design using neural network backpropagation method to forecast the students who apply for scholarship. the final result of this research is the accuracy value of 90% and the smallest error value of 0.000101 on epoch to 329 with the amount of 3000 data with sharing training 2,250 and 750 data testing and learning rate configuration of 0.2 and momentum 0.2.</em></p><p><strong>Keywords</strong>: <em>Artificial Neural Netwok, Backpropagarion, Prediction, Scholarship, Decision Making.</em></p>

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

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.

Prediction of Cutting Temperatures by Using Back Propagation Neural Network Modeling when Cutting Hardened H-13 Steel in CNC End Milling

2012 ◽  
Vol 576 ◽  
pp. 91-94 ◽  
Author(s):  
Erry Yulian Triblas Adesta ◽  
Muataz H.F. Al Hazza ◽  
M.Y. Suprianto ◽  
Muhammad Riza
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
End Milling ◽  
Back Propagation ◽  
Cutting Temperature ◽  
Neural Network Modeling ◽  
Feed Forward Back Propagation ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Cnc End Milling

Machining of hardened steel at high cutting speeds produces high temperatures in the cutting zone, which affects the surface quality and cutting tool life. Thus, predicting the temperature in early stage becomes utmost importance. This research presents a neural network model for predicting the cutting temperature in the CNC end milling process. The Artificial Neural Network (ANN) was applied as an effective tool for modeling and predicting the cutting temperature. A set of sparse experimental data for finish end milling on AISI H13 at hardness of 48 HRC have been conducted to measure the cutting temperature. The artificial neural network (ANN) was applied to predict the cutting temperature. Twenty hidden layer has been used with feed forward back propagation hierarchical neural networks were designed with Matlab2009b Neural Network Toolbox. The results show a high correlation between the predicted and the observed temperature which indicates the validity of the models.

scholarly journals Implementation of Autonomous Control System of The Chain Wheel Robot Using the Backpropagation Artificial Neural Network (ANN) Methods

2020 ◽  
Vol 5 (8) ◽  
pp. 1230-1235
Author(s):  
Nur Rachman Supadmana Muda ◽  
Nugraha Gumilar ◽  
R.Djoko Andreas. Navalino ◽  
Tirton. N ◽  
M.Iman Hidayat
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Control System ◽  
Robot Control ◽  
Back Propagation ◽  
Robot System ◽  
Output Data ◽  
Backpropagation Method ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The purpose of this research is to implement the Artificial Neural Network (ANN) method in combat robots so it can be directed to shoot targets well. The robot control system uses remote control and autonomous. In the autonomous robot system, ANN back propagation method is applied, where the weight value variable depends on ultrasonic sensor, GPS and camera. The microcontroller system will process automatically depending on the sensor input. Output data is used to direct the robot to the target, tracking and shooting. Robot is used chain wheel systems and weapons that used pistol types. The riffle is mounted on the robot can be moved mechanically azimuth and the elevation towards the target then triggered mechanically by the riffle through the activation of data relays from the microcontroller. Thus, the backpropagation method can be applied to robots so it can be functioned autonomously.

scholarly journals Artificial neural network for solving multi-parameter optimization problems

2021 ◽  
Vol 2092 (1) ◽  
pp. 012013
Author(s):  
Krivorotko Olga ◽  
Liu Shuang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optimization Problems ◽  
Three Dimensional ◽  
Test Functions ◽  
Backpropagation Method ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Quality Of Approximation ◽  
And Function

Abstract An artificial neural network (ANN) is a mathematical or computational model that simulates the structure and function of biological neural networks used to evaluate or approximate functions at given points. After developing the training algorithm, the resulting model will be used to solve image recognition problems, control problems, optimization, etc. In the process of ANN training, the algorithm of backpropagation is used in the case of convex optimization functions. The article is analyzed test functions for experiments and also study the effect of the number of ANN layers on the quality of approximation in cases one-, two- and three-dimensional. The backpropagation method is improved during the experiments with the help of adaptive gradient, as a result of which more accurate approximations of the functions are obtained. This article also presents the numerical results of test functions.

scholarly journals DETEKSI PENYAKIT PADA DAUN TEMBAKAU DENGAN MENERAPKAN ALGORITMA ARTIFICIAL NEURAL NETWORK

2013 ◽  
Vol 3 (1) ◽  
pp. 51
Author(s):  
Diana Laily
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Learning Rate ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

ABSTRAK Artificial Neural Network ( ANN ) Perceptron merupakan salah satu dari metode AI yang telah terbukti cukup handal untuk digunakan sebagai teknik pengenalan atau pengindentifikasian.Tujuan dari dibuatnya penelitian ini adalah untuk menerapkan metode Jaringan Syaraf Tiruan atau Artificial Neural Network dengan algortima Perceptron dalam menentukan penyakit cacar daun dan bercak daun pada daun tembakau serta daun cengkeh, dimana sampel daun-daun tersebut dianalisis melalui kedelapan gejala atau ciri yang ditimbulkannya.Tahapan awal yang dilakukan yaitu mengumpulkan beberapa sampel daun tembakau dan daun cengkeh, baik yang terkena penyakit maupun tidak. Kemudian mengelompokkan gejala atau ciri khusus yang ditimbulkan pada setiap daunnya dari penyakit cacar daun dan bercak daun. Ciri penyakit yang positif terlihat pada masing-masing daun akan direpresentasikan dengan nilai bipolar [1, -1], dimana ciri tersebut akan digunakan sebagai nilai masukan pada tahap pelatihan (training) dan pengujian (testing) dalam metode ANN. Dari hasil pengujian terhadap sampel sebanyak 20 daun untuk tahap training dan 10 sampel daun untuk tahap testing, dengan perbandingan penyakit bercak daun dan cacar daun adalah 50 : 50, learning rate sebesar 0,7, lapisan masukan sebanyak 8 buah, dan 1 buah lapisan luaran, didapat bahwa metode ANN Perceptron memiliki persentase keberhasilan pengenalan penyakit sebesar 61% - 73% untuk data non-learning, dan 100% untuk data learning pada kedua jenis daun tersebut. Kata kunci : ANN Perceptron, bipolar, learning rate, cacar daun, bercak daun.

scholarly journals Studi prakiraan beban listrik menggunakan metode artificial neural network

TEKNO ◽  
2019 ◽  
Vol 28 (2) ◽  
pp. 116
Author(s):  
Yuan Octavia ◽  
Arif Nur Afandi ◽  
Hari Putranto
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Learning Rate ◽  
Layer 2 ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

Pada penelitian ini, dilakukan prakiraan beban listrik jangka panjang menggunakan metode Artificial Neural Network (ANN) dengan penerapan algoritma backpropagation pada studi kasus distribusi energi listrik Area Mojokerto. Pada penelitian ini digunakan 8 variabel, dimana untuk variabel dependent berupa beban listrik, sedangkan untuk variabel independent digunakan 7 variabel yaitu jumlah penduduk, PDRB, jumlah pelanggan sektor rumah tangga, jumlah pelanggan sektor industri, jumlah pelanggan sektor usaha, jumlah pelanggan sektor sosial, dan susut distribusi. Berdasarkan hasil percobaan beberapa arsitektur ANN, diperoleh hasil MAPE pengujian terbaik sebesar 0.512% yang berarti memiliki tingkat akurasi tinggi. Hal ini berarti metode ANN dengan algoritma backpropagation dapat diterapkan sebagai metode prakiraan beban listrik untuk studi kasus pada distribusi energi listrik Area Mojokerto. Model ANN-backpropagation terbaik pada penelitian ini adalah variasi bobot dan bias awal diatur secara manual dengan modifikasi menggunakan algoritma inisialisasi Nguyen Widrow, jaringan memiliki 2 hidden layer dengan penyusunan 5 neuron pada hidden layer 1 dan 15 neuron pada hidden layer 2, nilai learning rate dan momentum berturut-turut adalah 0.9 dan 0.1. Berdasarkan arsitektur ANN terbaik, prakiraan beban listrik distribusi area Mojokerto pada tahun 2018 sampai dengan 2030 cenderung mengalami kenaikan dari tahun ke tahun, meskipun ada penurunan sebesar 0.157% dari tahun 2027 ke tahun 2028. Hasil prakiraan terendah ada pada tahun 2018 dengan hasil 312.7489 MW dan beban tertinggi ada pada tahun 2030 dengan hasil 383.5597MW. Hasil prakiraan beban listrik Area Mojokerto dari tahun 2018 sampai dengan 2030 mengalami kenaikan sebesar 22.641% dengan kenaikan rata-rata 1.728% per tahunnya

scholarly journals Artificial Neural Network Modeling of MoS2 Supercapacitor for Predicative Synthesis

2019 ◽  
Vol 9 (2) ◽  
pp. 554-560
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Energy Storage ◽  
Neural Network Modeling ◽  
Ann Model ◽  
Energy Storage Devices ◽  
Storage Devices ◽  
Artificial Neural ◽  
Forward Calculation ◽  
Artificial Neural Network Ann

Energy storage systems are fundamental to the activity of intensity frameworks. They guarantee coherence of vitality supply and improve the dependability of the framework. The first area is centered on various energy storage frameworks, considering capacity limit, voltage and current proportions, and energy accessibility. Among the energy storage devices, supercapacitor is widely used because it is a high-limit capacitor with capacitance esteem a large amount than different capacitors. In the supercapacitor we have used MoS2 material synthesized with various Electrolytes. In perspective on the above mentioned, we report an Artificial Neural Network (ANN) strategy to achieve the predictable results. Levenberg- Marquardt feed-forward calculation prepares the neural network. We measure the exhibition of the ANN model with respect to mean square error (MSE) and the relationship coefficient between anticipated yield and yield given by the system. Results confirm the stability of supercapacitor over the other energy storage devices. To show such kind of conduct, we give Synthesis technique, Electrolyte, Cycle Life as an info esteems and Specific limit as yield esteem. For the amalgamation technique info esteem we have taken both compound and physical strategies by normalizing it. The practiced ANN demonstrating confirmations a higher number of concealed neuron design showing ideal execution as respects to expectation exactness

scholarly journals Fluoride contamination - Artificial neural network modeling and inverse distance weighting approach

2012 ◽  
Vol 25 (2) ◽  
pp. 165-182 ◽  
Author(s):  
Imran Ahmad Dar ◽  
K. Sankar ◽  
Mithas Ahmad Dar ◽  
Mrinmoy Majumder
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Neural Network Modeling ◽  
Chemical Parameters ◽  
Underground Waters ◽  
Distance Weighting ◽  
Distance Weighted ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Inverse Distance

The underground waters in the Mamundiyar basin, India, present real chemical quality problems. Their fluoride content always exceeds the recommended levels. The Inverse Distance Weighted (IDW) method has been used for spatial interpolation of various key chemical parameters. Artificial Neural Network (ANN) modeling was applied to understand the correlation and sensitivity of all chemical parameters with respect to fluorides. The correlation of all the considered parameters is found to be poor where the highest correlation observed was only 0.37. This result showed that four of the parameters, namely pH, chlorides, sulphates and calcium, were found to have greater capacity of influencing fluorides than the other eight parameters. Chlorides were found to be the parameter that was the most sensitive and most correlated to fluorides.

scholarly journals Penerapan Prediksi Produksi Padi Menggunakan Artificial Neural Network Algoritma Backpropagation

2020 ◽  
Vol 6 (2) ◽  
pp. 100-107
Author(s):  
Hasdi Putra ◽  
Nabilah Ulfa Walmi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Learning Rate ◽  
Processing Data ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

Prediksi produksi padi menjadi penting dilakukan untuk menunjang pembangunan nasional sektor pertanian pada suatu negara atau wilayah. Artificial Neural Network (ANN) termasuk metode yang terbaik dalam melakukan prediksi. Masalah utamanya adalah bagaimana menentukan jumlah neuron dan hidden layer yang optimal sehingga akurasi prediksinya tinggi. Artikel ini bertujuan untuk merancang arsitektu ANN unutk melakukan prediksi terhadap produksi padi menggunakan ANN dengan algortima backpropagation. Tahapan penelitian yang dilakukan adalah mengumpulkan data produksi padi, melakukan pre-processing data, memproses prediksi, dan pengujian akurasi dan error serta implementasi. Dalam memproses prediksi dilakukan sesuai dengan rancangan model prediksi, yaitu parameter epoch, momentum, learning rate, hidden layer untuk menghasilkan keakuratan yang tinggi. Temuan yang diperolah berupa rancangan optimal untuk melakukan prediksi yaitu dengan menggunakan multilayer. Hasil pengujian sistem prediksi produksi padi yang terdiri dari 75 kali pengujian pada di 19 daerah di Sumatera Barat, diperoleh tingkat akurasi mencapai 88,14% atau dengan tingkat error yang relatif rendah yaitu 11,86%.

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