PERAMALAN INDEKS HARGA SAHAM GABUNGAN MENGGUNAKAN MODEL FUZZY MARKOV CHAIN GAUSSIAN EGARCH

    Bunga Cintya Dewi, - and Nar Herrhyanto, - and Fitriani Nar Herrhyanto, - (2025) PERAMALAN INDEKS HARGA SAHAM GABUNGAN MENGGUNAKAN MODEL FUZZY MARKOV CHAIN GAUSSIAN EGARCH. S1 thesis, Universitas Pendidikan Indonesia.

    Abstract

    Fuzzy Time Series (FTS) merupakan salah satu teknik peramalan data runtun waktu dengan menggunakan prinsip-prinsip dari teori himpunan fuzzy untuk memproyeksikan data berdasarkan nilai-nilai historis yang dinyatakan dalam bentuk linguistik. Exponential Generalized Autoregressive Conditional Heteroscedastisity (EGARCH) merupakan salah satu model peramalan yang digunakan dalam analisis deret waktu untuk mengukur volatilitas yang tidak konstan. Pada penelitian ini, dilakukan kombinasi model EGARCH dengan teknik fuzzy time series Markov chain menggunakan fungsi keanggotaan Gaussian yang dilakukan dengan tujuan untuk meningkatkan ketepatan peramalan pada peramalan data Indeks Harga Saham Gabungan. Peramalan dilakukan guna pengambilan keputusan dalam investasi saham sehingga dapat mengatasi risiko investasi saham dan memperoleh keuntungan yang maksimal bagi para investor saham. Data yang digunakan adalah Data Indeks Harga Saham Gabungan dengan periode bulanan dimulai dari Januari 2013 – Mei 2024. Pengolahan data dilakukan dengan menggunakan software R, Microsoft Excel, dan Eviews. Berdasarkan pengolahan data yang sudah dilakukan, peramalan Indeks Harga Saham Gabungan dengan model Fuzzy Markov Chain Gaussian EGARCH menghasilkan nilai akurasi peramalan berupa Mean Absolute Percentage Error sebesar 4,55%.
    Kata kunci: Peramalan, Fuzzy Time Series Markov Chain, Fungsi Keanggotaan Gaussian, EGARCH.

    Fuzzy Time Series (FTS) is one of the data forecasting techniques that uses principles from fuzzy set theory to project data based on historical values expressed in linguistic form. Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH) is one of the forecasting models used in time series analysis to measure non-constant volatility. In this study, a combination of the EGARCH model with the fuzzy time series Markov chain technique using the Gaussian membership function was carried out with the aim of increasing disclosure in forecasting the Indeks Harga Saham Gabungan. Forecasting is done for decision making in stock investment so that it can overcome the risk of stock investment and obtain maximum profit for stock investors. The data used is the Indeks Harga Saham Gabungan data with a monthly period starting from January 2013 - May 2024. The software used for data processing in this study is R, Microsoft Excel, and Eviews. Based on the data processing that has been done, forecasting the Indeks Harga Saham Gabungan with the Fuzzy Markov Chain Gaussian EGARCH model produces a forecasting accuracy value in the form of a Mean Absolute Percentage Error of 4.55%.
    Keywords: Forecasting, Fuzzy Time Series Markov Chain, Gaussian Membership Function, EGARCH.

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    Official URL: https://repository.upi.edu/
    Item Type: Thesis (S1)
    Additional Information: ID SINTA Dosen Pembimbing: Fitriani Agustina: 5981275
    Uncontrolled Keywords: Peramalan, Fuzzy Time Series Markov Chain, Fungsi Keanggotaan Gaussian, EGARCH. Forecasting, Fuzzy Time Series Markov Chain, Gaussian Membership Function, EGARCH.
    Subjects: Q Science > QA Mathematics
    Divisions: Fakultas Pendidikan Matematika dan Ilmu Pengetahuan Alam > Program Studi Matematika - S1 > Program Studi Matematika (non kependidikan)
    Depositing User: Bunga Cintya Dewi
    Date Deposited: 06 May 2025 04:20
    Last Modified: 06 May 2025 04:20
    URI: http://repository.upi.edu/id/eprint/132998

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