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PREDIKSI HARGA INDEKS SAHAM KOMPAS 100 DENGAN MODEL LONG SHORT-TERM MEMORY DA…
ULPAH, MARIAZULKARNAIN, AMMAR ISKANDAR

Penelitian ini bertujuan untuk menganalisis dan membandingkan kinerja dua model deep learning, yaitu Long Short-Term Memory (LSTM) dan Convolutional Neural Network (CNN), dalam memprediksi harga sa…

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-
Call Number
7790
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NEURAL NETWORKS AND THE FINANCIAL MARKETS: PREDICTING, COMBINING AND PORTFOLI…
SHADBOLT, JIMMYTAYLOR, JOHN G.

This book looks at how research into predicting the financial markets has progressed in recent years. The first section of the book describes the financial markets and asks whether they are indeed …

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Call Number
006.32 SHA n
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NEURAL NETWORKS IN FINANCE: GAINING PREDICTIVE EDGE IN THE MARKET
MCNELIS, PAUL D.

This book explores the intuitive appeal of neural networks and the genetic algorithm in finance. It demonstrates how neural networks used in combination with evolutionary computation outperform cla…

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Call Number
332.0285 MCN n
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BURNOUT KARYAWAN DI TENGAH PANDEMI COVID-19: PREDIKSI TINGKAT BURNOUT MENGGUN…
HANIF, IMAM RAFIFALVERSIA, YESHIKA

Burnout mengacu pada reaksi psikologis terhadap stres kerja kronis. Fenomena burnout merupakan fenomena yang harus diatasi oleh perusahaan karena membawa pengaruh negatif terhadap produktivitas dan…

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-
Call Number
6799
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Found 4 from your keywords: Subject : "Neural Network"
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STRATEGI PEMASARAN HOTEL. STUDI KASUS HOTEL KARTIKA CHANDRA
ARAFAH, IMAS NOOR - PAWITRA, TEDDY
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FINANCIAL FUTURES, OPTIONS, AND SWAPS
TUCKER, ALAN L.
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PENGELOMPOKAN SAHAM BERDASARKAN ANALISA FUNDAMENTAL DENGAN MENGGUNAKAN CLUSTER ANALYSIS
PRIHANDINI, WIWIEK - HENDIYANTO, ARMAN
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PENGUKURAN RISIKO OPERASIONAL LAYANAN MARKET CLAIM DAN STRATEGI MITIGASI (STUDI KASUS PADA PT. X MOTOR)
SETIAWAN, WAWAN
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KORELASI ANTARA RETURN EMAS, MINYAK, DAN NILAI TUKAR MATA UANG TERHADAP VOLATILITAS SEKTOR PASAR SAHAM DI INDONESIA SELAMA PANDEMI
CHALID, DONY ABDUL - ADIMANGGALA, MUHAMAD PUTRA
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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