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dc.contributor.advisorSetiawaty, Berlian
dc.contributor.advisorBudiarti, Retno
dc.contributor.authorSyamsudin, Smaragdy Radhiya
dc.date.accessioned2025-12-23T08:45:45Z
dc.date.available2025-12-23T08:45:45Z
dc.date.issued2025
dc.identifier.urihttp://repository.ipb.ac.id/handle/123456789/171814
dc.description.abstractPenelitian ini bertujuan untuk mengidentifikasi penggunaan model ARMA– GARCH dalam memprediksi volatilitas Bitcoin serta mengevaluasi akurasinya berdasarkan MAPE. Data harga harian Bitcoin periode 1 Januari 2023–31 Desember 2023 dipraproses melalui differencing satu kali, translasi agar seluruh nilai positif, dan transformasi Box–Cox. Berdasarkan analisis ACF–PACF dan perbandingan AIC, model mean terbaik adalah ARMA(0,0). Uji Ljung–Box menunjukkan residual tidak berkorelasi, sedangkan uji ARCH–LM mengindikasikan heteroskedastisitas bersyarat sehingga volatilitas dimodelkan menggunakan GARCH(0,1). Evaluasi menunjukkan MAPE 20,223% pada data train, 22,930% pada data uji 15 langkah ke depan, dan rata-rata MAPE 20,177% dari lima percobaan set.seed terbaik, yang termasuk kategori prediksi baik. Hasil ini menegaskan bahwa model ARMA–GARCH mampu menangkap pola harga dan volatilitas Bitcoin secara stabil serta layak digunakan untuk peramalan jangka pendek dan pendukung pengelolaan risiko pada aset kripto yang bergejolak. Kata kunci: ARMA-GARCH, volatilitas, Bitcoin
dc.description.abstractThis study aims to identify the use of the ARMA–GARCH model for predicting Bitcoin volatility and to evaluate its accuracy using the MAPE metric. Daily Bitcoin prices from January 1, 2023 to December 31, 2023 were preprocessed through onetime differencing, a translation to ensure positive values, and a Box–Cox transformation. Based on ACF–PACF analysis and AIC comparison, the selected mean model is ARMA(0,0). The Ljung–Box test indicates uncorrelated residuals, while the ARCH–LM test confirms conditional heteroskedasticity, leading volatility to be modeled using GARCH(0,1). The evaluation shows a train MAPE of 20.223%, a 15-step-ahead test MAPE of 22.930%, and an average MAPE of 20.177% across the five best set.seed runs, classified as good prediction accuracy. These findings demonstrate that the ARMA–GARCH model effectively captures Bitcoin price dynamics and volatility, making it suitable for short-term forecasting and supporting risk management in highly volatile crypto markets. Keywords: ARMA-GARCH, volatility, Bitcoin
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dc.language.isoid
dc.publisherIPB Universityid
dc.titlePeramalan Harga Bitcoin menggunakan Model ARMA-GARCHid
dc.title.alternative
dc.typeSkripsi
dc.subject.keywordbitcoinid
dc.subject.keywordARMA-GARCHid


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