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All Journal JUSIFO : Jurnal Sistem Informasi Sistemasi: Jurnal Sistem Informasi
Harjanto, Aris Tri Joko
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Computer Science & IT Electrical & Electronics Engineering

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Prediction of Whole Blood Stock Using Single Exponential Smoothing: A Case Study at the Indonesian Red Cross, Semarang Hayyannabil, Adha Wiyan; Harjanto, Aris Tri Joko; Herlambang, Bambang Agus
JUSIFO : Jurnal Sistem Informasi Vol 10 No 1 (2024): JUSIFO (Jurnal Sistem Informasi) | June 2024
Publisher : Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/jusifo.v10i1.22368

Abstract

The Indonesian Red Cross, as a provider of public health services in the city of Semarang, experienced a shortage of whole blood supply in April 2023 due to high demand. In response to this issue, an effective prediction system is needed. This research aims to apply the single exponential smoothing method to predict blood needs at the Indonesian Red Cross in Semarang. The study uses data from 2022 to April 2024 as historical data to forecast blood requirements. The research findings indicate a continuous increase in the demand for whole blood availability. This study provides a foundation for developing a better prediction system with more complex data patterns.
Implementation of the K-Means Algorithm for Clustering Zoning of Natural Disaster-Prone Areas in Pati Regency Arifin, Arifin; Herlambang, Bambang Agus; Harjanto, Aris Tri Joko
Sistemasi: Jurnal Sistem Informasi Vol 15, No 3 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i3.5970

Abstract

Based on data from the Central Bureau of Statistics (BPS) of Pati Regency, during the 2018–2022 period the region frequently experienced natural disasters, particularly floods and landslides, across many areas.The high frequency of these disasters and the lack of proper mapping of affected areas present challenges that need to be addressed effectively. By utilizing technology and the K-Means clustering method, this study proposes an alternative solution to identify and map areas that are vulnerable to natural disasters. The results of the analysis indicate that Pati Regency can be divided into three clusters: Cluster 1 represents highly disaster-prone areas, accounting for 42.86% (9 regions); Cluster 2 represents disaster-prone areas, accounting for 19.04% (4 regions); and Cluster 3 represents areas with low disaster vulnerability, accounting for 38.1% (8 regions). The visualization results are presented as a classification map of regions based on their disaster vulnerability levels. This map can serve as a reference for local governments and relevant institutions in formulating more targeted and effective disaster mitigation policies.
Co-Authors Arifin Arifin Bambang Agus Herlambang, Bambang Hayyannabil, Adha Wiyan