scholarly journals Penerapan Metode C4.5 Berbasis Particle Swarm Optimization Untuk Memprediksi Penjualan Obat Pada Apotek Bunda Azka

2021 ◽  
Vol 2 (3) ◽  
pp. 174-187
Author(s):  
Desi Lestari ◽  
Muhammad Nasir

The application of the C4.5 Algorithm based on Particle Swarm Optimization to classify the level of sales of drugs that are often sold at the Bunda Azka Pharmacy, is a strategic thing to reduce the problems experienced by the pharmacy. Classify the level of sales of drugs sold using the C4.5 method. based on particle swarm optimization, to find out whether the C4.5 method based on particle swarm optimization (PSO) can optimize drug sales in the future. This research method uses a descriptive method, namely by conducting case study research by studying activities in the field, observing and interviewing stakeholders. in the initial step of this research is the determination of the attributes that will be processed into data mining with the help of rapidminer tools, this study the author uses the KDD model as a standardization in the data mining process. at the pharmacy. The data will later be processed using the c4.5 algorithm based on Particle Swarm Optimization to find the accuracy results of the prediction of the data. The data sample used is the number of 65 drug transaction records at the Bunda Azka Pharmacy. In the test results, the accuracy of Particle Swarm Optimization was 78.10%, for class recall drug sales was 72.50% and after using Particle Swarm Optimization increased to 78.33%, while precision had an accuracy of 77.92% and after using Particle Swarm Optimization increased to 80.33%. From the results of testing with Particle Swarm Optimization, there is an increase in accuracy of 7.15% from the research application of the C4.5 Method Based on Particle Swarm Optimization to Predict Drug Sales at Bunda Azka Pharmacy.

2020 ◽  
Vol 4 (3) ◽  
pp. 569-575
Author(s):  
Dwi Meylitasari Tarigan ◽  
Dian Palupi Rini ◽  
Samsuryadi

Diabetes Mellitus (DM) is a disease caused by blood sugar level increased were higher than the maximum limit. Food consumed tends to contain uncontrolled sugar which could cause the drastic increase of blood sugar level. It is necessary to efforts, to increasing the public awareness to controlling blood sugar and the risks of increasing blood sugar level so as to determine of preventive and early detection measures One of used of data mining technique is information technology in the health sector which used a lot as a decision maker to predicting and diagnosing a several disease.  This research aims to optimizing the features on classification of the data mining with the C4.5 algorithm using Particle Swarm Optimization (PSO) to detect the blood sugar level in patient. The dataset used is the effect of physical activity to the Blood Sugar Level at H. Abdul Manan Simatupang Kisaran Regional Public Hospital.  The amount of dataset used is 42 record with 10 attributes.  The result of this research obtained that the Particle Swarm Optimization (PSO) may increasing the accuracy performance of C4.5 from 86% to 95%.  Whereas the evaluation result of the AUC Value increasing from 0,917 to 0,950. From those 10 attributes which are then selection with using PSO into 7 attributes used to determine the prediction of sugar level.  Therefore the Algorithm C4.5 using the Particle Swarm Optimization (PSO) may provide the best solution to the accuracy of detection blood sugar levels.


2019 ◽  
Vol 135 ◽  
pp. 368-381 ◽  
Author(s):  
Idelfonso B.R. Nogueira ◽  
Márcio A.F. Martins ◽  
Reiner Requião ◽  
Amanda R. Oliveira ◽  
Vinícius Viena ◽  
...  

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