Internet Bandwidth Forecasting by Using Fuzzy Time Series in Zainal Abidin General Hospital, Indonesia

Khalid Rianda; Muhd Iqbal; Muslim Amiren; Maulyanda Maulyanda; Afdhaluzzikri Afdhaluzzikri; Intan Syahrini; Siti Rusdiana; Abdul Fikri; Irvanizam Irvanizam

doi:10.60084/ijds.v4i1.428

Authors

Khalid Rianda Department of Informatics, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
Muhd Iqbal Department of Informatics, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
Muslim Amiren Department of Informatics, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
Maulyanda Maulyanda Department of Informatics, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
Afdhaluzzikri Afdhaluzzikri Department of Informatics, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
Intan Syahrini Department of Mathematics, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
Siti Rusdiana Department of Mathematics, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
Abdul Fikri Center for Southeast Asian Studies, Kyoto University, 46 Yoshidashimoadachicho, Sakyo Ward, Kyoto, 606-8304, Japan
Irvanizam Irvanizam Department of Informatics, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia

DOI:

https://doi.org/10.60084/ijds.v4i1.428

Keywords:

Bandwidth, Fuzzy Times Series, Forecasting, Internet, MAPE

Abstract

Data bandwidth capacity is a critical component of internet infrastructure management, directly impacting network efficiency and operational costs. Accurate measurement and forecasting of bandwidth requirements are essential to optimize resource allocation. This study utilizes a Fuzzy Time Series (FTS) approach for bandwidth forecasting, leveraging its ability to capture complex patterns from historical data without requiring the rigid statistical assumptions of classical forecasting methods. A forecasting model was developed and implemented to predict data bandwidth requirements at the Zainal Abidin General Hospital (RSUZA). Utilizing historical data collected from February 1, 2019, to April 29, 2019, the model's performance was evaluated using the Mean Absolute Percentage Error (MAPE). The proposed method achieved a MAPE of 6.45%, demonstrating high accuracy and falling into the "highly accurate" category.

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