scholarly journals Prediksi Pendonor Darah Potensial Menggunakan Algoritma Learning Vector Quantitation (LVQ) (Studi Kasus : Unit Transfusi Darah PMI Kota Palu, Sigi Dan Donggala)

2020 ◽  
Vol 17 (1) ◽  
pp. 29-40
Author(s):  
N K Susiani ◽  
A I Jaya
Keyword(s):  
Blood Donors ◽  
Blood Donation ◽  
Training Data ◽  
Rate Reduction ◽  
Accuracy Rate ◽  
The Public ◽  
Average Accuracy ◽  
Testing Data ◽  
Stage 1 ◽  
Health Test

Potential blood donors are blood donors who can donate their blood back after success through 2 stages of blood donation such as the physical health test (active) and the screening test (laboratory test). The purpose of this study are to obtain an application that can be used to predict potential blood donors who will donate their blood back at the PMI Palu, Sigi and Donggala Blood Transfusion Units, and to obtain their level of accuracy using the Learning Vector Quantitation algorithm. This prediction application for potential blood donors makes it easier for the public to know whether they can donate their blood or not. Classification is done using 300 data consisting of 70% training data and 30% testing data. The data used in this study are data taken in 2018. The accuracy of the best weighting in stage 1 is 95.56% obtained using the training rate (α) of 0.1≤α≤0.25 and the rate reduction training (decα) which is varied. While the best weighting results in stage 2 have an average accuracy rate of 100% obtained by using a training rate (α) of 0.000001≤α≤0.5 and a reduction in the rate of training (decα) which varies.

scholarly journals The development of the program of voluntary blood donation promotion in students population of the University of Belgrade

2015 ◽  
Vol 72 (6) ◽  
pp. 489-494 ◽  
Author(s):  
Snezana Jovanovic-Srzentic ◽  
Ivana Rodic ◽  
Mirjana Knezevic
Keyword(s):  
Blood Donors ◽  
Blood Donation ◽  
Population Group ◽  
Minimum Risk ◽  
Blood Donations ◽  
The Public ◽  
The Media ◽  
Donated Blood ◽  
The University ◽  
Academic Year

Background/Aim. Given that in each country students represent the most progressive population group, as of 2001, the Blood Transfusion Institute of Serbia (BTIS) has been carrying the program of voluntary blood donation promotion and education of volunteers at the University of Belgrade (UB). In 2011, the BTIS intensified all activities at the UB. The aim of this study was to present activities performed from 2001 at the Blood Donors` Motivation Department (DMD) of the BTIS related with increasing the level of awareness on voluntary blood donation in the Belgrade students` population, enhancing their motivation to become voluntary blood donors (VBDs), increasing the number of blood donations at faculties of the UB, and increasing the number of blood donations in the UB students population compared with the total number of blood units collected by BTIS in Belgrade, with the emphasis on the year 2013. Methods. Initially, the applied methodology was based on encouraging students to donate blood through discussions and preparatory lectures, followed by organized blood drives. Appropriate selection of volunteers at each faculty was crucial. Besides their recognisable identity, they had to have remarkable communication skills and ability to positivly affect persons in their environment. The applied principle was based on retention of volunteers all through the final academic year, with the inclusion of new volunteers each year and 1,000 preparatory lectures on the annual basis. The activities were realized using two Facebook profiles, SMS messages and continuous notification of the public through the media. Results. There was an increase in the average number of students in blood drives at the faculties from 2011, when the average number of the students per blood drive was 39, followed by 43 in 2012 and 46 in 2013. The number of students who donated blood in 2013 increased by 21.3% compared with 2012 data. Conclusion. The applied concept highly contributed to generation and retention of future VBDs willing to regularly donate blood in the coming years, with a minimum risk of transmission of transfusion transmissible diseases markers.

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 Online Blood Bank

2021 ◽  
pp. 173-179
Author(s):  
Ameya Kambe ◽  
Arbaaz Sheikh ◽  
Nilisha Bhishma ◽  
Dr. Manisha Khorgade
Keyword(s):  
Low Income ◽  
Blood Donors ◽  
Blood Donation ◽  
High Income ◽  
Low Income Countries ◽  
Middle Income ◽  
The Public ◽  
Middle Income Countries ◽  
Blood Banks ◽  
Online Web

Out of 118.5 million blood donations collected globally, 40% are collected in high income countries which compromises 16% of the world’s population. The median annual donations per blood centre are very low in low-income countries as compared to high-income countries. In high-income countries, the median blood donation rate is 31.5 donations per 1000 people, 15.9 per 1000 people in upper-middle-income countries and five donations per 1000 people in low-income countries, which is very low when compared. This indicates there is a huge gap between demand and supply. Most of the patients, due to the lack of communication between the donor and the recipient do not receive the blood in time and this may cost them their lives. The necessity for synchronisation between blood donors, hospitals, and blood banks is critical. The usage of online web portals has made it easy for the public to access and connect to other people and organizations. Using a portal we will be able to make blood donors easily available for the receivers. An online web portal where the hospitals and blood banks are integrated will make it easy for the receivers to get access to blood.

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 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 Diagnosis of Diseases Based on Iridology Using Fuzzy Logic

2021 ◽  
Author(s):  
Zakaria Madhouse ◽  
Ammar Kayli ◽  
Luna Himmami
Keyword(s):  
Fuzzy Logic ◽  
Disease Rate ◽  
Accuracy Rate ◽  
The Public ◽  
Fuzzy Models ◽  
Average Accuracy ◽  
Automatic Methods ◽  
Input Variables ◽  
The Brain ◽  
Diagnosis Of Diseases

Many automatic methods have been introduced in iridology to predict diseases according to the iridology chart. This is important to prevent diseases before they develop. This research aims to find a computer model for the early diagnosis of diseases in the brain, back, pelvis, abdomen, and chest using the iridology chart based on fuzzy logic. Image preprocessing for the iris aims to find the ring, code, and features of the iris. Five fuzzy models have been built for diagnosis and to determine a person's disease rate based on specific features that were extracted from the iris as the input variables. Each model contains four membership functions for each input or output variables and 64 fuzzy rules for fuzzification and defuzzification. The five models that were built to diagnose the five diseases of iridology have an accuracy rate of over 98%, with an average accuracy of 98.6223%. The results mean that the models are qualified for use by doctors as medical tools to diagnose specific diseases or as a tool for the public to reassure them about their health.

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 IMPLEMENTASI KLASIFIKASI NAIVE BAYES DALAM MEMPREDIKSI LAMA STUDI MAHASISWA (STUDI KASUS : UNIVERSITAS DHYANA PURA)

2021 ◽  
Vol 4 (2) ◽  
pp. 202-209
Author(s):  
Kelvin Hennry Loudry Malelak ◽  
I Made Dwi Ardiada ◽  
Gerson Feoh
Keyword(s):  
Data Mining ◽  
Undergraduate Students ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Accuracy Rate ◽  
Testing Data ◽  
Study Programs ◽  
Bayes Algorithm ◽  
Academic Year

Under normal conditions, undergraduate or undergraduate students from a university can complete their studies for 4 years or 8 semesters. In fact, many students complete their study period of more than 4 years. Is known that in fact in the 2015/2016 academic year there were 744 people who were accepted as students. Of the 744 people who were accepted, 405 people had completed a study period of about 4 years and the remaining 39 people completed their studies for 5 years and 300 of them did not continue their studies. Based on the problem on, so This study implements a classification that can help Dhyana Pura University in predicting the length of study for students who are currently studying in various study programs at Dhyana Pura University. The author's method serves in the classification to predict long student study period is the Naive Bayes algorithm. By using the Java-based Rapid Miner tool to classify graduation data. Then the implementation of data mining which is divided into 968 training data and 193 data testing data with naive Bayes has succeeded in obtaining an accuracy rate of 100% which also has very good parameters.

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