Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Arianto, Kezia Jazzlyn
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
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.
Prediksi Harga Penutupan Saham Gojek-Tokopedia Menggunakan Model Hybrid GARCH-LSTM Pramudito, Farhan Wegig; Arianto, Kezia Jazzlyn; Thoyib, Najma Humairoh; Arvintyani, Risquina Angelica; Herlambang, Yudhistira Jalu; Zuhdi, Syaifudin
Journal of Information System Research (JOSH) Vol 7 No 2 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i2.8911

Abstract

This study proposes the application of a hybrid GARCH–LSTM model to predict GoTo stock prices in the context of Indonesia's rapidly growing digital economy. GoTo stock prices are characterized by high volatility and a non-linear time series pattern, making them difficult to model using conventional approaches. Daily closing price data from 2022 to November 2025 are transformed into logarithmic returns to meet the stationarity assumption. The GARCH(1,1) model is used to estimate conditional volatility, which represents short-term risk dynamics and the volatility clustering phenomenon. Furthermore, historical returns and conditional volatility are used as additional features in the LSTM model to predict the next period's stock returns, which are then converted back into closing price predictions. The estimation results show that all GARCH parameters are statistically significant, indicating the persistence of volatility in GoTo stock data. Evaluation of the performance of the hybrid model on the test data produces an RMSE value of 3.126, an MAE of 2.245, and a coefficient of determination (R²) of 0.899, indicating that the model is able to represent stock price movement patterns well. These findings indicate that the hybrid GARCH–LSTM approach is effective in modeling stock price dynamics under highly volatile market conditions.
Co-Authors Arvintyani, Risquina Angelica Dewi Puspita Sari Herlambang, Yudhistira Jalu Maheswari, Desinta Pramudito, Farhan Wegig Thoyib, Najma Humairoh Wisnu Widiarto Zuhdi, Syaifudin