Skripsi

IMPLEMENTASI FUZZY TIME SERIES CHEN DAN K-MEANS CLUSTERING PADA PERAMALAN NILAI TUKAR RUPIAH TERHADAP US DOLLAR

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Penilaian

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One of the forecasting methods based on time series is Fuzzy Time Series Chen. However, Fuzzy Time Series Chen has a weakness in the interval section because determining the interval value is static or constant. The determination of the length of the interval itself is very influential in the results of the forecasting result. Therefore, to determine the appropriate interval length, one of the methods used to determine the length of the interval is K-Means Clustering. In this research, Fuzzy Time Series Chen and K-Means Clustering is used as a forecasting model. The benchmark for error use Means Absolute Percentage Error (MAPE). The error results for FTS Chen at the selling rate is 0.6784 % and for buying rate is 0. 6779 % while the FTS Chen - K-Means error percentage for the selling rate is 0,422% and buying rate is 0.4429%. It can be concluded that FTS Chen and K-Means results are better than FTS Chen. Keywords: Fuzzy Time Series Chen, K-Means Clustering, Mean Absolute Percentage Error, Forecasting.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2107002242	T54526	T545262021	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T545262021

Publisher

Inderalaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2021

Collation

xvii, 61 hlm,: ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

004.07

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Pemrosesan Data, Teknik Informatika
Prodi Teknik Informatika

Specific Detail Info

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Statement of Responsibility

MURZ

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