PREDIKSI PENDONOR DARAH DENGAN METODE REGRESI LINIER DI UNIT DONOR DARAH (UDD) PALANG MERAH INDONESIA KABUPATEN BANYUMAS

Cahyani, Viki (2020) PREDIKSI PENDONOR DARAH DENGAN METODE REGRESI LINIER DI UNIT DONOR DARAH (UDD) PALANG MERAH INDONESIA KABUPATEN BANYUMAS. Other thesis, Universitas Amikom Purwokerto.

[img] Text
Cover.pdf

Download (768kB)
[img] Text
DAFTAR ISI.pdf

Download (664kB)
[img] Text
ABSTRAK.pdf

Download (447kB)
[img] Image
BAB I.pdf
Restricted to Registered users only

Download (949kB)
[img] Image
BAB II.pdf
Restricted to Registered users only

Download (588kB)
[img] Image
BAB III.pdf
Restricted to Registered users only

Download (778kB)
[img] Image
BAB IV.pdf
Restricted to Registered users only

Download (3MB)
[img] Image
BAB V.pdf
Restricted to Registered users only

Download (448kB)
[img] Image
DAFTAR PUSTAKA.pdf
Restricted to Registered users only

Download (524kB)
[img] Text
LAMPIRAN.pdf
Restricted to Repository staff only

Download (2MB)

Abstract

The Indonesian Red Cross (PMI) of Banyumas Regency has humanitarian social tasks such as disaster management, community services, first aid training, and helping provide blood donations. As the population grows, so does the need for blood each year, but often there is a lack of blood and difficulty in planning and supplying blood. This needs to be anticipated by the Blood Donor Unit of the Banyumas Regency PMI to reduce the amount of blood shortage, one of which is by predicting or forecasting the number of donors to find out the number of donors in the following year. The purpose of this study is to find out the prediction results of the number of donors in the Blood Donation Unit of the Banyumas Regency PMI in 2020. In a study conducted to predict the number of donors, researchers used a linear regression method. Based on the calculations that have been done the linear regression method can produce predictions with 40 linear regression prediction models, namely with a very good category MAPE value totaling 19 prediction models or 47.5%, with a good category MAPE value totaling 9 prediction models or 22.5 %, with a sufficient MAPE category score of 10 prediction models or 25%, with a bad category MAPE score of 2 prediction models or 5%.

Item Type: Thesis (Other)
Additional Information: Dosen Pembimbing: Tri Astuti, S.Kom., M.Eng.
Uncontrolled Keywords: Prediction, Blood Donors, Data Mining, Linear Regression
Subjects: N Fine Arts > N Visual arts (General) For photography, see TR
R Medicine > R Medicine (General)
T Technology > T Technology (General)
Divisions: Fakultas Ilmu Komputer > Informatika
Depositing User: UPT Perpustakaan Pusat Universitas Amikom Purwokerto
Date Deposited: 07 Jan 2021 02:42
Last Modified: 07 Jan 2021 02:42
URI: http://eprints.amikompurwokerto.ac.id/id/eprint/498

Actions (login required)

View Item View Item