Comparison Predictions of the Demam Berdarah Dengue (DBD) using Model Exponential Smoothing: Pegel’s Classification and ChatGPT

Wiwik Wiyanti; Bakti  Siregar

doi:10.20956/j.v20i2.32122

Authors

Wiwik Wiyanti Universitas Matana
Bakti Siregar

DOI:

https://doi.org/10.20956/j.v20i2.32122

Keywords:

prediction, pegel's classification, demam berdarah, GPPT-3, AI

Abstract

The evolution of AI since the Covid-19 pandemic has developed very rapidly. Until 2023, AI is claimed to be a threat to several professional jobs, especially data analysts and scientists. The purpose of this research is to check the effectiveness chat-GPT to predict about demam berdarah dengue (DBD) case. Method of the analyzing the data in this research is Mixed method. Quantitative method using exponential smoothing: pegel’s classification and qualitative method using GPT-3. The aim of this research is to check whether ChatGPT can predict the demam berdarah dengue (DBD) data time series. The prediction result are check it by exponential smoothing: pegel’s classification method. The benefit of this research is it can be used to reference how far the evolution of AI can be threaten the profession of data analyst or data scientist. The result of this study conclude that the ChatGPT (GPT-3) can’t predict DBD’d data correctly.

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