Please use this identifier to cite or link to this item:
https://repository.unej.ac.id/xmlui/handle/123456789/123431
Full metadata record
DC Field | Value | Language |
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dc.contributor.author | QUR’ANI, Fajri Sahrul | - |
dc.date.accessioned | 2024-08-12T02:58:27Z | - |
dc.date.available | 2024-08-12T02:58:27Z | - |
dc.date.issued | 2024-01-24 | - |
dc.identifier.nim | 202410101046 | en_US |
dc.identifier.uri | https://repository.unej.ac.id/xmlui/handle/123456789/123431 | - |
dc.description.abstract | RSGM Jember State University is a hospital that provides services and treatment with specialisation in dental and oral diseases. It has 7 clinics with different specialities. The management requires forecasting data to carry out planning in management in the future. Forecasting is done using the prophet timeseries forecasting method with accuracy testing using Mean Absolute Percentage Error (MAPE), this study uses data on the number of RSGM patients in each clinic with a time span of January 2015 to November 2023. The results showed that the best forecasting model produced was using the prophet model with additional components. With the results of accuracy testing using MAPE obtained at the dental conservation clinic with the highest accuracy test result of 98.68%, and the oral disease clinic with the lowest accuracy test result of 88.89%. For forecasting in 2023, it can be seen that forecasting with the prophet model produces the best numbers with only 2 clinics producing accuracy rates below 90%. | en_US |
dc.language.iso | other | en_US |
dc.publisher | Fakultas Ilmu Komputer | en_US |
dc.subject | Forecasting | en_US |
dc.subject | Prophet Models | en_US |
dc.subject | Timeseries | en_US |
dc.title | Peramalan Jumlah Pasien RSGM Universitas Jember Menggunakan Metode Prophet | en_US |
dc.type | Skripsi | en_US |
dc.identifier.prodi | Sistem Informasi | en_US |
dc.identifier.pembimbing1 | Priza Pandunata S.Kom., M.Sc. | en_US |
dc.identifier.pembimbing2 | Muhamad Arief Hidayat S.Kom,.M.Kom. | en_US |
dc.identifier.validator | reva | en_US |
Appears in Collections: | UT-Faculty of Computer Science |
Files in This Item:
File | Description | Size | Format | |
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doc.pdf Until 2029-01-25 | 2.21 MB | Adobe PDF | View/Open Request a copy |
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