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Image of PREDIKSI NILAI TUKAR RUPIAH DENGAN ALGORITMA LONG SHORT-TERM MEMORY

Text

PREDIKSI NILAI TUKAR RUPIAH DENGAN ALGORITMA LONG SHORT-TERM MEMORY

POERNOMO, AYU - Personal Name; ROKHIM, ROFIKOH - Personal Name;

Fluktuasi nilai tukar Rupiah terhadap mata uang asing di kawasan Asia merupakan tantangan yang signifikan dalam menjaga stabilitas ekonomi Indonesia. Penelitian ini bertujuan untuk memprediksi nilai tukar Rupiah menggunakan algoritma Long Short-Term Memory (LSTM). Data harian nilai tukar mata uang dari tahun 2020 hingga 2024 dianalisis menggunakan pendekatan machine learning. Proses melibatkan normalisasi data, pelatihan data, dan evaluasi menggunakan Mean Absolute Percentage Error (MAPE) dan R-Squared. Hasil penelitian menunjukkan bahwa model LSTM mampu menangkap pola non-linear dalam data deret waktu dengan tingkat akurasi tinggi. Implementasi model ini memberikan manfaat bagi pengambil keputusan di sektor keuangan, regulator, dan akademisi dalam memahami dinamika pasar valuta asing.


Availability
300075727572RLC MM (Rak Tesis)Available
Detail Information
Series Title
-
Statement of Responsibility
Ayu Poernomo
Call Number
7572
Publisher
Salemba, Jakarta : Magister Manajemen FEB UI., 2025
Collation
xii, 97 p. : ill. ; 30 cm.
Language
Indonesia
ISBN/ISSN
-
Classification
650
Content Type
-
Edition
-
Subject(s)
Manajemen Keuangan
Tesis
Machine Learning
Rupiah Exchange Rate
Long Short-Term Memory
Mean Absolute Percentage Error
R-Squared
Specific Detail Info
-
Other version/related

No other version available

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RLC MM FEB-UI
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RLC MM-FEBUI (Library) occupies the right side of the ground floor of the MM FEB UI Building with a reading room capacity of more than 60 people.
 
The MM-FEB UI library service system is closed (closed access); where the user does not have direct access to the collection shelf. Or in other words, users are not allowed to take their own books from the collection shelf

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