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All Journal bit-Tech Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Maheswari, Desinta
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Computer Science & IT Decision Sciences, Operations Research & Management

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Implementasi Algoritma Round Robin dan Priority Pada Sistem Antrian Rumah Sakit Widiarto, Wisnu; Maheswari, Desinta; Sari, Dewi Puspita; Arianto, Kezia Jazzlyn
JURNAL FASILKOM Vol. 14 No. 2 (2024): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v14i2.7334

Abstract

Dalam sistem antrian rumah sakit, perencanaan proses yang efektif dan efisien sangat penting untuk mengurangi waktu tunggu pasien dan meningkatkan kualitas pelayanan medis. Penelitian ini mengimplementasikan algoritma Round-Robin dan Prioritas untuk mengatur antrian pasien dalam suatu rumah sakit. Algoritma Round-Robin memberikan setiap pasien waktu eksekusi yang adil berdasarkan jumlah waktu yang telah ditentukan, sedangkan algoritma Prioritas mengatur waktu berjalan berdasarkan tingkat prioritas pasien. Penelitian ini menganalisis kinerja kedua algoritma menggunakan simulasi data pasien seperti waktu kedatangan, waktu konsultasi, dan tingkat prioritas. Hasil simulasi menunjukkan bahwa meskipun algoritma Round-Robin berhasil mendistribusikan waktu konsultasi secara merata, namun hal ini dapat meningkatkan waktu tunggu pasien sakit kritis. Sebaliknya, algoritma Prioritas mengurangi waktu tunggu untuk pasien dengan prioritas tinggi namun dapat menyebabkan penundaan bagi pasien dengan prioritas rendah.
Comparison of ARCH and GARCH Models for Ethereum Return Volatility Makarim, Rizqi Akbar; Maheswari, Desinta; Pramustiwi, Aqila Dina; Rahmawati, Kartika Ayu; Ghaisani, Salma Fatila
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3363

Abstract

The volatility of cryptocurrency markets has increased substantially in recent years, particularly for Ethereum (ETH), which exhibits fat-tailed distributions and persistent volatility clustering that traditional linear models are unable to capture. This study aims to analyze and model the volatility of ETH/USD using high-frequency hourly data to determine the most appropriate volatility model for describing Ethereum’s intraday market dynamics. The dataset consists of 8,760 hourly closing prices from October 31, 2024 to October 31, 2025, obtained through the CryptoCompare API. The methodological framework includes data preprocessing, log-return transformation, stationarity analysis using the Augmented Dickey–Fuller test, detection of heteroskedasticity via the ARCH–LM test, and estimation of several ARCH and GARCH model specifications. The results show that ETH/USD returns are stationary, non-normally distributed, and exhibit clear volatility clustering. Among the ARCH models, only ARCH(1) adequately captures short-term fluctuations, while ARCH(2) provides no additional benefit. In contrast, GARCH models demonstrate superior performance in capturing both short-term shocks and long-term persistence. Based on AIC, BIC, and log-likelihood values, GARCH(1,2) emerges as the best-performing model, offering the highest flexibility in representing Ethereum’s persistent and reactive volatility patterns. These findings confirm that ETH/USD volatility is predictable and can be modeled statistically. Future research may incorporate asymmetric GARCH extensions or external explanatory variables to improve predictive performance.
Co-Authors Arianto, Kezia Jazzlyn Dewi Puspita Sari Ghaisani, Salma Fatila Makarim, Rizqi Akbar Pramustiwi, Aqila Dina Rahmawati, Kartika Ayu Wisnu Widiarto