Peramalan Harga Emas Menggunakan Pendekatan Long-Short Term Memory (LSTM)
DOI:
https://doi.org/10.26418/jp.v10i2.78332Keywords:
Harga Emas, Investasi, Long-Short Term Memory, PeramalanAbstract
Inventasi menjadi salah satu pilihan masyarakat untuk mengelola kelebihan dana agar nilainya meningkat dikemudian hari. Emas menjadi salah satu komoditi yang sering dijadikan instrumen investasi favorit. Harga emas yang fluktuatif menimbulkan efek kerugian bagi investor. Peramalan harga emas dimasa yang akan datang menjadi penting untuk meminimalisir resiko kerugian. Pendekatan machine learning lebih baik dibandingkan inferensial seperti Autoregressive Integrated Moving Average (ARIMA) dalam meramalkan data yang fluktuatif. Metode dengan pendekatan machine learning seperti Long-Short Term Memory (LSTM) memiliki performasi yang baik pada data yang fluktuatif. Metode LSTM digunakan untuk untuk meramalkan harga emas. Penelitian ini membagi data training dan data testing sebesar 80% dan 20%. Metode evaluasi model Mean Absolute Percentage Error (MAPE) digunakan untuk melihat kebaikan model. Penelitian ini menerapkan enam scenario tunning parameter. Parameter terbaik metode LSTM yaitu learning rate 0,01, neuron 10, dan Epoch 100 dengan nilai MAPE sebesar 3,499%. Hasil MAPE pada data training dan data testing tidak menunjukan terjadinya overfitting atau underfitting pada metode LSTM terbaik. Hasil peramalan tiga puluh periode cenderung fluktuatif, terjadi kenaikan yang signifikan pada periode ke dua puluh empat ke dua puluh lima.
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