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 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 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.

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 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 IMPLEMENTASI JARINGAN SYARAF TIRUAN BACKPROPAGATION DENGAN ALGORITMA CONJUGATE GRADIENT UNTUK KLASIFIKASI KONDISI RUMAH (Studi Kasus di Kabupaten Cilacap Tahun 2018)

2020 ◽  
Vol 9 (1) ◽  
pp. 41-49
Author(s):  
Johanes Roisa Prabowo ◽  
Rukun Santoso ◽  
Hasbi Yasin
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Conjugate Gradient ◽  
Training Data ◽  
Gradient Algorithm ◽  
Output Layer ◽  
Average Accuracy ◽  
Testing Data ◽  
Artificial Neural ◽  
Hidden Layer

House is one aspect of the welfare of society that must be met, because house is the main need for human life besides clothing and food. The condition of the house as a good shelter can be known from the structure and facilities of buildings. This research aims to analyze the classification of house conditions is livable or not livable. The method used is artificial neural networks (ANN). ANN is a system information processing that has characteristics similar to biological neural networks. In this research the optimization method used is the conjugate gradient algorithm. The data used are data of Survei Sosial Ekonomi Nasional (Susenas) March 2018 Kor Keterangan Perumahan for Cilacap Regency. The data is divided into training data and testing data with the proportion that gives the highest average accuracy is 90% for training data and 10% for testing data. The best architecture obtained a model consisting of 8 neurons in input layer, 10 neurons in hidden layer and 1 neuron in output layer. The activation function used are bipolar sigmoid in the hidden layer and binary sigmoid in the output layer. The results of the analysis showed that ANN works very well for classification on house conditions in Cilacap Regency with an average accuracy of 98.96% at the training stage and 97.58% at the testing stage.Keywords: House, Classification, Artificial Neural Networks, Conjugate Gradient

scholarly journals Autonomous Tomato Harvesting Robotic System in Greenhouses: Deep Learning Classification

Mekatronika ◽  
2019 ◽  
Vol 1 (1) ◽  
pp. 80-86
Author(s):  
Ooi Peng Toon ◽  
Muhammad Aizzat Zakaria ◽  
Ahmad Fakhri Ab. Nasir ◽  
Anwar P.P. Abdul Majeed ◽  
Chung Young Tan ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
South America ◽  
Solanum Lycopersicum ◽  
Training Data ◽  
New Classification ◽  
The Past ◽  
Testing Data ◽  
Harvesting Robot

Solanum lycopersicum or generally known as tomato came from countries of South America and has been growing in many tropical countries and its healthy nutrients in tomato becomes one of the food demand by the locals in Malaysia when their lifestyle shifted to more concern for healthy food. Since export value and production has increased for the past few years, a vast amount of labours considered for the fruit-picking process. Hence, farmers are now preferring to look for automation to replace labour problems and high cost that they are facing. To pick a correct fruit within clusters, a harvesting robot requires guidance so that it can detect a fruit accurately. In this study, a new classification algorithm using deep learning specifically convolution neural network to classify the image is either a tomato or not tomato and next, the image is classified into either a ripe or unripe tomato. Furthermore, there are two classification neural networks which are tomato or not tomato and ripe and unripe tomato. Each network consists of 600 training data and 33 testing data. The accuracies that obtained from network 1 (tomato or not tomato) and network 2 (ripe or unripe tomato) are 76.366% and 98.788% respectively.

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