Prediksi Pendapatan Pemerintah Indonesia Menggunakan Long Short-Term Memory

• MAHMUD
• 14210139

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ABSTRAK

ABSTRAK

Nama               : Mahmud

NIM                  : 14210139

Program Studi : Ilmu Komputer

Fakultas           : Teknologi Informasi

Jenjang             : Strata Dua (S2)

Konsentrasi       : Data Mining

Judul Tesis    : “Prediksi Pendapatan Pemerintah Indonesia Menggunakan Long Short-Term Memory”

Pendapatan pemerintah memegang peranan penting dalam mencapai tujuan pembangunan nasional. Dalam rangka pengelolaan kas negara yang optimal, diperlukan prediksi pendapatan pemerintah yang akurat sehingga kas dapat dimanfaatkan dengan maksimal untuk periode yang akan datang. Penelitian ini mengkaji metode yang tepat untuk melakukan prediksi pendapatan pemerintah berdsarkan data history pendapatan pemerintah tahun 2013 sampai dengan 2022. Dalam penelitian ini, diusulkan penerapan model Long Short-Term Memory (LSTM) untuk prediksi pendapatan pemerintah. Eksperimen menunjukkan model LSTM dengan menggunakan empat hidden layer dan hyperparameter yang tepat dapat menghasilkan performa Mean Absolute Percentage Error (MAPE) sebesar 11,14% dan Root Mean Square Error (RMSE) sebesar 15,43%. Ini lebih baik dibandingkan pemodelan konvensional Autoregressive Integrated Moving Average (ARIMA) dan Seasonal Autoregressive Integrated Moving Average (SARIMA) sebagai model pembanding.

KATA KUNCI

Pendapatan pemerintah,machine learning,LSTM,ARIMA,SARIMA

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DAFTAR PUSTAKA

DAFTAR PUSTAKA

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