Implementasi Algoritma Random Forest untuk Penentuan Relawan Pendonor Darah Potensial pada Palang Merah Indonesia (PMI) Kabupaten Kudus

Muhammad Maulana Abdurrohman; Fajar Nugraha; Noor Latifah

doi:10.70340/jirsi.v5i2.347

Authors

Muhammad Maulana Abdurrohman Universitas Muria Kudus https://orcid.org/0009-0007-0943-0612
Fajar Nugraha Universitas Muria Kudus
Noor Latifah Universitas Muria Kudus

DOI:

https://doi.org/10.70340/jirsi.v5i2.347

Keywords:

Random Forest, Blood Donation, PMI, Machine Learning, Geographic Information System

Abstract

Palang Merah Indonesia (PMI) Kudus Regency faces an imbalance of blood stock across blood types, frequently causing difficulties in meeting blood demand during emergencies. Manual donor data management has been the primary factor behind slow donor recruitment processes. This study develops a machine learning-based prediction system using the Random Forest algorithm to identify blood donor volunteers with a high likelihood of donating again. The system is built as a web application using the CodeIgniter framework and Python, integrated with a Geographic Information System (GIS) via Leaflet.js for spatial visualization of donor distribution. System development follows the CRISP-DM approach, using historical donor data from PMI Kudus Regency as model training material. Evaluation results demonstrate strong model performance with an accuracy of 87.45% and a ROC-AUC of 0.912, while a cross-validation ROC-AUC of 0.897 confirms model consistency without overfitting. The system generates a ranked list of potential donors based on the highest probability values, organized by sub-district and blood type, thereby accelerating PMI officers' response in fulfilling blood demand.

Downloads

Download data is not yet available.

References

A. M. Jannah, A. A. D. Nur, and D. Dahniar, “Gambaran Ketersediaan Stok Komponen Darah Thrombocyte Concentrate di UDD PMI Provinsi Sulawesi Selatan Tahun 2024,” J. Penelit. Multidisiplin Bangsa, vol. 2, no. 5, pp. 918–925, 2025.

P. Batarius, A. A. J. Sinlae, and others, “Prediction of blood demand and supply: double exponential smoothing method approach,” J-Icon J. Komput. Dan Inform., vol. 12, no. 1, pp. 1–9, 2024.

Y. I. Saidi, Y. Aneta, and R. Tohopi, “Kualitas Pelayanan Di Unit Transfusi Darah Palang Merah Indonesia (Pmi) Kota Gorontalo,” J. Adm. Manaj. Sumber Daya Mns. Dan Ilmu Sos., vol. 3, no. 3, pp. 140–153, 2024.

N. N. Amalina, D. T. Liputra, and R. M. Heryanto, “Analisis Penyebab Risiko pada Rantai Pasok Darah di Masa Pandemi COVID-19 Menggunakan Model Supply Chain Operations Reference (SCOR) dan Failure Modes and Effects Analysis (FMEA),” JISI J. Integr. Sist. Ind., vol. 11, no. 1, pp. 65–76, 2024.

S. S. Elfaretta, A. A. Arifiyanti, and A. S. Fitri, “Klasifikasi Calon Pendonor Darah Potensial Menggunakan Algoritma Decision Tree Di Utd Pmi Kota Surabaya,” J. Inform. dan Tek. Elektro Terap., vol. 12, no. 3, 2024.

M. Azwar, L. Widyawati, R. Azhar, K. Kartarina, T. Tanwir, and A. S. Anas, “Deteksi Malware Pada Perangkat Android Menggunakan Ensemble Learning,” J. Teknol. Inf. dan Multimed., vol. 7, no. 3, pp. 408–419, 2025.

F. Rozi, “Perancangan Sistem Penyediaan Stok Darah Dalam Blood Supply Chain Management Berbasis Blockchain Pada PMI Sleman Yogyakarta,” Universitas Islam Indonesia, 2024.

A. K. Putri and D. I. Nur, “Penggunaan bahasa python untuk analisis dan visualisasi data penduduk di Desa Sumberjo, Nganjuk,” Karya J. Pengabdi. Kpd. Masy., vol. 3, no. 3, pp. 206–217, 2023.

N. A. Elmaulana, L. P. Ghanistyana, N. Aryanti, R. Taufik, and S. R. Jelita, “Strategi Cyber PR PMI Kota Tangerang Dalam Kegiatan Donor Darah,” J. Din. Ilmu Komun., vol. 11, no. 1, pp. 1–12, 2025.

L. A. Kusumawardani, Y. Amalia, S. Widuri, P. A. D. Reswari, and C. P. Kwarta, “Prediksi Kelayakan Donor Berdasarkan Riwayat Kesehatan Berbasis Algoritma Machine Learning,” J. Ilm. J-HESTECH, vol. 8, no. 2.

S. Sutisna and M. N. Yuniar, “Klasifikasi kualitas air bersih menggunakan METODE Naive baiyes,” J. Sains dan Teknol., vol. 5, no. 1, pp. 243–246, 2023.

I. M. Hamdani, N. Nurhidayat, A. Karman, A. H. Julyaningsih, and others, “Edukasi dan Pelatihan Data Science dan Data Preprocessing,” Intisari J. Inov. Pengabdi. Masy., vol. 2, no. 1, pp. 19–26, 2024.

M. Guntara and F. D. Astuti, “Komparasi Kinerja Label-Encoding dengan One-Hot-Encoding pada Algoritma K-Nearest Neighbor menggunakan Himpunan Data Campuran,” JIKO (Jurnal Inform. dan Komputer), vol. 9, no. 2, pp. 352–360, 2025.

A. F. Nugraha, R. F. A. Aziza, and Y. Pristyanto, “Penerapan metode Stacking dan Random Forest untuk Meningkatkan Kinerja Klasifikasi pada Proses Deteksi Web Phishing,” J. Infomedia, vol. 7, no. 1, p. 39, 2022.

M. R. Hanan, L. Muflikhah, and F. A. Bachtiar, “Prediksi Nefropati Menggunakan Algoritma Random Forest,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 9, no. 5, 2025.