scholarly journals Application ofThe Levenberg Marquardt Method In Predict The Amount of Criminality in Pematangsiantar City

2021 ◽  
Vol 3 (1) ◽  
pp. 21-29
Author(s):  
Widya Tri Charisma Gultom ◽  
Anjar Wanto ◽  
Indra Gunawan ◽  
Muhammad Ridwan Lubis ◽  
Ika Okta Kirana
Keyword(s):  
Neural Network ◽  
Research Data ◽  
Training Data ◽  
Criminal Investigation ◽  
Accuracy Rate ◽  
Marquardt Method ◽  
Testing Data ◽  
Levenberg Marquardt ◽  
Architectural Models ◽  
Over Time

Criminality is an act that violates the law that can disturb society and even harm society both economically and psychologically. The number of crimes cannot be ascertained over time because the numbers are uncertain. So that the police have difficulty in overcoming criminal acts. With this research, the police can find out the number of criminals that will occur through the prediction that has been made. So that the police can prevent the number of criminals and increase security in Pematangsiantar city. This study uses an artificial neural network with the Levenberg Marquardt method. The research data is sourced from the Pematangsiantar Police Criminal Investigation Agency (Reskrim) in 2014-2019. The data is divided into 2 parts, namely training data and testing data. There are 5 architectural models used in this study, namely 3-30-1, 3-31-1, 3-32-1, 3-36-1 and 3-38-1. Of the 5 architectural models used, the best architecture is 3-36-1 with an accuracy rate of 85%, MSE 0.1465119, and a maximum iteration of 10000, the results obtained from the best architecture in 2020 are 85% with the number of criminals 394 people, in 2021 it is 62 % totaled 238 people, in 2022, namely 69% amounted to 170 people, so this model is good for predicting the number of crimes in Pematangsiantar City.

scholarly journals Jaringan Saraf Tiruan dalam Memprediksi Produksi Kelapa Sawit di PT. KRE Menggunakan Algoritma Levenberg Marquardt

2019 ◽  
Vol 1 ◽  
pp. 249
Author(s):  
Yuli Andriani ◽  
Anjar Wanto ◽  
Handrizal Handrizal
Keyword(s):  
Neural Networks ◽  
Secondary Data ◽  
Research Data ◽  
Training Data ◽  
Accuracy Rate ◽  
Marquardt Method ◽  
Rate Of Increase ◽  
Testing Data ◽  
Levenberg Marquardt ◽  
Architectural Models

Predictions are used to determine how much the rate of increase or decrease in oil palm production at PT. Kerasaan Indonesia (KRE) in the future. This study uses Artificial Neural Networks (ANN) using the Levenberg Marquardt method. The research data is secondary data sourced from PT. Kerasaan Indonesia from 2002 to 2017. Data is divided into 2 parts, namely training data and testing data. There are 5 architectural models used in this study, 7-10-1, 7-20-1, 7-30-1, 7-40-1 and 7-50-1. Of the 5 architectural models used, the best architecture is 7-50-1 by producing an accuracy rate of 83%, MSE 1.1471332321 and a maximum iteration of 1000. So this model is good for predicting coconut production palm oil at PT. Indonesian feeling because of its accuracy between 80% and 90%.

scholarly journals Implementation of Expert System for Diabetes Diseases using Naïve Bayes and Certainty Factor Methods

2018 ◽  
Vol 5 (2) ◽  
pp. 185-193
Author(s):  
Muhammad Ilham Insani ◽  
Alamsyah Alamsyah ◽  
Anggyi Trisnawan Putra
Keyword(s):  
Expert Systems ◽  
Medical Record ◽  
Research Data ◽  
Training Data ◽  
Distribution Data ◽  
Certainty Factor ◽  
Accuracy Rate ◽  
Bayes Method ◽  
Is Implementation ◽  
Testing Data

Expert Systems is a computer systems that has been entered the base knowledge and a set of rules used to solve problems like an expert. Methods that can be used in the expert systems which is Naïve Bayes and Certainty Factor. Naïve Bayes method can handle quantitative calculations and discreate data and only requires a little research data to estimate the parameters needed in the clasification and Certainty Factor which is suitable for measuring something whether it is certain or not in diagnosing. Diabetes is one of the most frequent diseases suffered in Indonesia. The purpose of this research is implementation expert systems used Naïve Bayes and Certainty Factor in diagnosing diabetes and knowing the level of accuracyof the systems. Data that is used by researchers as much 100 data medical record, obtained from the medical record RSUD Bendan Kota Pekalongan. The variabels used in this research is age, gender, the symptoms of the desease diabetes and result diagnose desease from expert. The accuracy rate of this system derived from the scenario distribution data 70 training data and 30 testing data that is equal to 100% according to the doctor's diagnosis.

scholarly journals COMBINATION OF SYNTHETIC MINORITY OVERSAMPLING TECHNIQUE (SMOTE) AND BACKPROPAGATION NEURAL NETWORK TO CONTRACEPTIVE IUD PREDICTION

2020 ◽  
Vol 13 (1) ◽  
pp. 36-46
Author(s):  
Mustaqim Mustaqim ◽  
Budi Warsito ◽  
Bayu Surarso
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Contraceptive Use ◽  
Imbalanced Data ◽  
Training Data ◽  
Backpropagation Neural Network ◽  
Accuracy Rate ◽  
Minority Class ◽  
Testing Data ◽  
Major Data

Data imbalance occurs when the amount of data in a class is more than other data. The majority class is more data, while the minority class is fewer. Imbalance class will decrease the performance of the classification algorithm. Data on IUD contraceptive use is imbalanced data. National IUD failure in 2018 was 959 or 3.5% from 27.400 users. Synthetic minority oversampling technique (SMOTE) is used to balance data on IUD failure. Balanced data is then predicted with neural networks. The system is for predicting someone when using IUD whether they have a pregnancy or not. This study uses 250 data with 235 major data (not pregnant) and 15 minor data (pregnant). From 250 data divided into two parts, 225 training and 25 testing data. Minority class on training data will be duplicated to 1524%, so that the amount of minority data become balanced with  the majority data. The results of predictive with an accuracy rate of  99.9% at 1000 epoch.

scholarly journals ANALISIS ALGORITMA BACKPROPAGATION DALAM PREDIKSI NILAI EKSPOR (JUTA USD)

2018 ◽  
Vol 15 (2) ◽  
Author(s):  
Jonas Rayandi Saragih ◽  
Mhd. Billy Sandi Saragih ◽  
Anjar Wanto
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Error Rate ◽  
Research Data ◽  
Central Bureau ◽  
Accuracy Rate ◽  
Backpropagation Algorithm ◽  
Architectural Models ◽  
Artificial Neural ◽  
North Sumatra

In a study, the analysis is necessary for the accuracy and accuracy of an education. So also in prediction Export Value (Million USD). This research will discuss the value of export in general in North Sumatra based on Million USD. This research is conducted to know the export development in North Sumatera in the future. This research uses Artificial Neural Network with Backpropagation algorithm. The research data used comes from the Central Bureau of Statistics of North Sumatra from 2012 until 2017. This research will use five architectural models namely 4-5-1, 4-7-1, 4-9-1, 4-10-1 and 4-11-1. The best model of the five models is 4-7-1 with a 100% accuracy rate, with a time of 27 seconds. The error rate used is 0.001 - 0.05. Thus, this model is good enough to predict Export Value in North Sumatra, because its accuracy reaches 100%.

scholarly journals Analisis dalam Melihat Perkembangan Indeks Harga Perdagangan Besar Menurut Sektor di Indonesia Menggunakan Algoritma Backpropagation

2019 ◽  
Vol 1 ◽  
pp. 359
Author(s):  
Zulfikar Zulfikar ◽  
Anjar Wanto ◽  
Zulaini Masruro Nasution
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Secondary Data ◽  
Training Data ◽  
Accuracy Rate ◽  
Price Indexes ◽  
Testing Data ◽  
Backpropagation Method ◽  
Architectural Models ◽  
Artificial Neural

The Large Trade Price Index (IHPB) is one of the economic indicators that contains index numbers and shows changes in the price of goods purchased by traders from consumers. This study uses Artificial Neural Networks (ANN) with the Backpropagation method. Artificial neural networks are branches of artificial intelligence that mimic or imitate the workings of the human brain. The data of this study are secondary data sourced from the Central Statistics Agency (BPS) from 2000 to 2017. The data is divided into 2 parts, namely training data and testing data. There are 5 architectural models used in this study. 8-15-1, 8-25-1, 8-26-1, 8-30-1 and 8-40-1. From the 5 architectural models used 1 best model was obtained, namely 8-25-1 with an accuracy rate of 85%, MSE 0.00100074 and 10000 iterations. So this model is good for predicting large trade price indexes according to sectors in Indonesia in the future.

Improving Pattern Recognition Rate by Gaussian Hopfield Neural Network

2011 ◽  
Vol 189-193 ◽  
pp. 2042-2045 ◽  
Author(s):  
Shang Jen Chuang ◽  
Chiung Hsing Chen ◽  
Chien Chih Kao ◽  
Fang Tsung Liu
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Gaussian Distribution ◽  
Test Pattern ◽  
Recognition Rate ◽  
Hopfield Neural Network ◽  
Training Data ◽  
New Method ◽  
Gaussian Filter ◽  
Testing Data

English letters cannot be recognized by the Hopfield Neural Network if it contains noise over 50%. This paper proposes a new method to improve recognition rate of the Hopfield Neural Network. To advance it, we add the Gaussian distribution feature to the Hopfield Neural Network. The Gaussian filter was added to eliminate noise and improve Hopfield Neural Network’s recognition rate. We use English letters from ‘A’ to ‘Z’ as training data. The noises from 0% to 100% were generated randomly for testing data. Initially, we use the Gaussian filter to eliminate noise and then to recognize test pattern by Hopfield Neural Network. The results are we found that if letters contain noise between 50% and 53% will become reverse phenomenon or unable recognition [6]. In this paper, we propose to uses multiple filters to improve recognition rate when letters contain noise between 50% and 53%.

An Investigation of the Characteristics of a Bayesian Military Impulse Noise Classifier

2008 ◽  
Author(s):  
Brian Bucci ◽  
Jeffrey Vipperman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Bayesian Methods ◽  
False Positive ◽  
Impulse Noise ◽  
False Negative ◽  
Training Data ◽  
Maximum Accuracy ◽  
First Case ◽  
Testing Data

In extension of previous methods to identify military impulse noise in the civilian environmental noise monitoring setting by means of a set of computed scalar metrics input to artificial neural network structures, Bayesian methods are investigated to classify the same dataset. Four interesting cases are identified and analyzed: A) Maximum accuracy achieve on training data, B) Maximum overall accuracy on blind testing data, C) Maximum accuracy on testing data with zero false positive detections, D) Maximum accuracy on testing data with zero false negative rejections. The first case is used to illustrative example and the later three represent actual monitoring modes. All of the cases are compared and contrasted to illuminate respective strengths and weaknesses. Overall accuracies of up to 99.8% are observed with no false negative rejections and accuracies of up to 98.4% are also achieved with no false positive detections.

scholarly journals Helmet Monitoring System using Hough Circle and HOG based on KNN

2021 ◽  
Vol 12 (1) ◽  
pp. 13
Author(s):  
Rachmad Jibril Al Kautsar ◽  
Fitri Utaminingrum ◽  
Agung Setia Budi
Keyword(s):  
Police Officers ◽  
Nearest Neighbor ◽  
Training Data ◽  
K Nearest Neighbor ◽  
Accuracy Rate ◽  
Histogram Of Oriented Gradient ◽  
Surveillance Camera ◽  
Testing Data ◽  
Motorized Vehicles ◽  
Traffic Operation

 Indonesian citizens who use motorized vehicles are increasing every year. Every motorcyclist in Indonesia must wear a helmet when riding a motorcycle. Even though there are rules that require motorbike riders to wear helmets, there are still many motorists who disobey the rules. To overcome this, police officers have carried out various operations (such as traffic operation, warning, etc.). This is not effective because of the number of police officers available, and the probability of police officers make a mistake when detecting violations that might be caused due to fatigue. This study asks the system to detect motorcyclists who do not wear helmets through a surveillance camera. Referring to this reason, the Circular Hough Transform (CHT), Histogram of Oriented Gradient (HOG), and K-Nearest Neighbor (KNN) are used. Testing was done by using images taken from surveillance cameras divided into 200 training data and 40 testing data obtained an accuracy rate of 82.5%.

scholarly journals Levenberg-Marquardt Neural Network for Eye States Detection Based on Electroencephalography Data

2016 ◽  
Vol 2 (1) ◽  
pp. 23
Author(s):  
Untari Novia Wisesty
Keyword(s):  
Neural Network ◽  
Learning Algorithm ◽  
Computer Interface ◽  
Data Normalization ◽  
Sigmoid Function ◽  
Marquardt Method ◽  
Brain Signals ◽  
Weak Convergence Rate ◽  
Levenberg Marquardt ◽  
Hidden Layer

The eye state detection is one of various task toward Brain Computer Interface system. The eye state can be read in brain signals. In this paper use EEG Eye State dataset (Rosler, 2013) from UCI Machine Learning Repository Database. Dataset is consisting of continuous 14 EEG measurements in 117 seconds. The eye states were marked as “1” or “0”. “1” indicates the eye-closed and “0” the eye-open state. The proposed schemes use Multi Layer Neural Network with Levenberg Marquardt optimization learning algorithm, as classification method.  Levenberg Marquardt method used to optimize the learning algorithm of neural network, because the standard algorithm has a weak convergence rate. It is need many iterations to have minimum error. Based on the analysis towards the experiment on the EEG dataset, it can be concluded that the proposed scheme can be implemented to detect the Eye State. The best accuracy gained from combination variable sigmoid function, data normalization and number of neurons are 31 (95.71%) for one hidden layer, and 98.912% for two hidden layers with number of neurons are 39 and 47 neurons and linear function.

scholarly journals PENERAPAN JARINGAN SARAF TIRUAN DALAM MEMPREDIKSI JUMLAH KEMISKINAN PADA KABUPATEN/KOTA DI PROVINSI RIAU

2018 ◽  
Vol 5 (1) ◽  
pp. 61
Author(s):  
Anjar Wanto
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Natural Resources ◽  
Error Rate ◽  
Poverty Rate ◽  
Central Bureau ◽  
Poor People ◽  
Accuracy Rate ◽  
Architectural Models ◽  
Artificial Neural

<p><em>Provinsi Riau yang kaya akan Sumber Daya Alam ternyata tidak sebanding dengan jumlah penduduk miskin yang menempati di sejumlah kabupaten/kota di Riau. Contohnya seperti pada tahun 2013 terdapat ± 68.600 penduduk miskin di kabupaten Kampar, atau merupakan yang tertinggi dibandingkan kabupaten/kota lainnya. Oleh karena itu dibutuhkan langkah-langkah strategis agar jumlah penduduk miskin tidak bertambah sepanjang tahun, salah satu nya adalah dengan melakukan prediksi jumlah penduduk miskin untuk tahun-tahun selanjutnya. Cara ini dilakukan agar angka kemiskinan bisa semakin ditekan dengan cara melakukan penganggulangan sejak dini. Data yang akan diprediksi adalah data jumlah kemiskinan kabupaten/kota di Provinsi Riau yang bersumber dari Badan Pusat Statistik Provinsi Riau tahun 2010 sampai dengan 2015. Algoritma yang digunakan untuk melakukan prediksi adalah jaringan saraf tiruan Backpropagation. Algoritma ini memiliki kemampuan untuk mengingat dan membuat generalisasi dari apa yang sudah ada sebelumnya. Ada 5 model arsitektur yang digunakan pada algoritma backpropagation ini, antara lain 4-2-5-1 yang nanti nya akan menghasilkan prediksi dengan tingkat akurasi 8%, 4-5-6-1=25%, 4-10-12-1=92%, 4-10-15-1=100% dan 4-15-18-1=33%. Arsitektur terbaik dari ke 5 model ini adalah 4-10-12-1 dengan tingkat keakurasian mencapai 100% dan tingkat error yang digunakan 0,001-0,05. Sehingga model arsitektur ini cukup baik digunakan untuk memprediksi jumlah kemiskinan. </em><br /> <br /><em><strong>Keywords</strong>: Penerapan, Jaringan Saraf Tiruan, Backpropagation, Prediksi, Kemiskinan</em></p><p><em>Riau is rich in Natural Resources is not comparable with the number of poor people who occupy in a number of districts/cities in Riau. For example, in 2013 there were ± 68,600 poor people in Kampar district, or the highest compared to other districts. Therefore, strategic steps are needed so that the number of poor people will not increase throughout the year, one of them is to predict the number of poor people for the next years. This way is done so that the poverty rate can be further suppressed by doing the countermeasures early on. The data to be predicted is the data of the number of poverty districts/cities in Riau Province sourced from the Central Bureau of Statistics of Riau Province in 2010 until 2015. Algorithm used to make prediction is the Backpropagation. This algorithm has the ability to remember and make generalizations of what has been there before. There are 5 architectural models, among others 4-2-5-1 which later will produce predictions with an accuracy rate of 8%, 4-56-1=25%, 4-10-12-1=92%, 4-10-15-1=100% and 4-15-18-1=33%. The best architecture of the 5 models is 4-10-12-1 with 100% accuracy and error rate of 0.001-0.05. So this model of architecture is good enough used to predict the amount of poverty. </em></p><p><em><strong>Kata kunci</strong>: Implementation, Artificial Neural Network, Backpropagation, Prediction, Poverty</em></p>

Sign in / Sign up

Export Citation Format

Share Document