Perbandingan Algoritma Linear Regression, Support Vector Regression, dan Artificial Neural Network untuk Prediksi Data Obat
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Suci Maharani Putri
*
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru,
Indonesia
- Rice Novita Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
- Mustakim Mustakim Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
- M Afdal Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia (*) Corresponding Author
Abstract
Regression is a crucial focus in various fields aiming to forecast future values to aid decision-making and strategic planning. Different regression algorithms have their advantages and disadvantages, and their performance can vary depending on the data characteristics. Therefore, further analysis is needed to identify the appropriate algorithm that provides the best solution for the problem at hand. This study compares three popular regression algorithms: Linear Regression (LR), Support Vector Regression (SVR), and Artificial Neural Network (ANN) to predict drug data at a pharmacy in Riau province. Currently, the pharmacy lacks an accurate method for estimating monthly drug needs, relying instead on rough estimates. This often results in either shortages or overstock, leading to losses, especially if the drugs expire. Three types of drugs, namely Amoxicillin, Antacids, and Paracetamol were selected to test the proposed algorithms. The analysis and comparison show that the SVR algorithm outperforms the others on all three drug types when focusing on the RMSE metric. However, when the focus is on the MAPE metric, the ANN algorithm proves to be superior. Although LR does not excel in any metric, all three algorithms (LR, SVR, and ANN) have MAPE values below 10%, indicating highly accurate predictions. This accuracy is evidenced by the prediction results of all proposed models, which effectively follow the patterns and trends in the actual data
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