Klasifikasi Lonjakan Ekstrem Nilai Tukar USD/IDR Menggunakan Gaussian Naïve Bayes

Meini Syakinah Ritonga; Rita Novita Sari

doi:10.70340/jirsi.v5i2.457

Authors

Meini Syakinah Ritonga Universitas Islam Negeri Sumatera Utara
Rita Novita Sari Universitas Islam Negeri Sumatera Utara

DOI:

https://doi.org/10.70340/jirsi.v5i2.457

Keywords:

Classification, Data Mining, Exchange Rate, Gaussian Naïve Bayes, Volatility

Abstract

The fluctuation of the Rupiah exchange rate against the US Dollar (USD/IDR) potentially triggers macroeconomic instability. This study aims to classify potential extreme surges in the USD/IDR exchange rate using data mining techniques with the Gaussian Naïve Bayes algorithm. A total of 502 daily historical observation data were extracted into four continuous predictor features: volatility, closing difference, upper bound difference, and lower bound difference. The evaluation was conducted using an 80% training and 20% testing data split. The results show that the model can identify "Normal" and "Extreme" classes with an accuracy of 96.04%, a precision of 62.50%, and a recall of 83.33%. The 5-Fold Cross Validation test yielded an average cumulative accuracy of 95.43%, confirming that the model's performance is stable and does not experience overfitting. In conclusion, the Gaussian Naïve Bayes algorithm is proven to be effective and reliable as an early warning system against the risk of extreme foreign exchange rate surges.

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