Deteksi Dini Pneumonia Berbasis Gejala Klinis Menggunakan Random Forest: Early Detection of Pneumonia Based on Clinical Symptoms Using Random Forest

Ridha Mukhtar; Achmad Saleh; Rusdah Rusdah

doi:10.57152/malcom.v6i4.2796

Authors

Ridha Mukhtar Universitas Sapta Mandiri
Achmad Saleh Universitas Sapta Mandiri
Rusdah Rusdah Universitas Budi Luhur

DOI:

https://doi.org/10.57152/malcom.v6i4.2796

Keywords:

Deteksi Dini, Gejala Klinis, Machine Learning, Pneumonia, Random Forest

Abstract

Pneumonia merupakan salah satu penyakit pernapasan akut dengan tingkat morbiditas dan mortalitas yang tinggi, terutama pada anak-anak dan lansia. Keterbatasan fasilitas radiologi dan laboratorium di beberapa wilayah menyebabkan proses diagnosis dini pneumonia menjadi kurang optimal. Penelitian ini bertujuan untuk membangun model deteksi dini pneumonia berbasis gejala klinis menggunakan algoritma Random Forest. Metode penelitian mengacu pada kerangka kerja CRISP-DM yang meliputi tahap business understanding, data understanding, data preparation, modelling, evaluation, dan deployment. Dataset penelitian diperoleh dari rekam medis RSUD H. Damanhuri Barabai dengan atribut berupa jenis kelamin, usia, tinggi badan, berat badan, suhu tubuh, batuk, dahak, nyeri dada, sesak napas, laju pernapasan, dan muntah. Evaluasi model dilakukan menggunakan confusion matrix dan 10-fold Cross-validation. Hasil penelitian menunjukkan bahwa algoritma Random Forest menghasilkan performa terbaik dengan nilai akurasi sebesar 89,82%, recall 95,24%, precision 90,53%, dan AUC 0,9469. Hasil ini menunjukkan bahwa model mampu mendeteksi pneumonia secara efektif dan berpotensi menjadi alat bantu pengambilan keputusan medis, khususnya pada fasilitas kesehatan dengan keterbatasan sarana diagnostik.

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References

WHO, “Pneumonia in children,” Nov. 2022. [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/pneumonia

Kemenkes, “Pedoman Nasional Pelayanan Kedokteran Tata Laksana Pneumonia Pada Dewasa,” Kementeri. Kesehat. RI, pp. 1–85, 2023, [Online]. Available: jdih.kemkes.go.id

World Health Organization, Guideline on Management of Pneumonia and Diarrhoea in Children up to 10 Years of Age. 2024. [Online]. Available: https://iris.who.int/handle/10665/381761

F. Faisal, Irwandi, R. Aprilia, Suharni, and Efriza, “Tinjauan Literatur: Faktor Risiko dan Epidemiologi Pneumonia pada Balita,” Sci. J., vol. 3, no. 3, pp. 166–173, 2024, doi: 10.56260/sciena.v3i3.144.

S. Usabili and U. Indahyanti, “Pemodelan Deteksi Dini Diabetes Mellitus menggunakan Pendekatan Ensemble Learning,” Manaj. Pelayanan Kesehat., vol. 1, no. 1, p. 12, Jan. 2024, doi: 10.47134/mpk.v1i1.2924.

C. Y. Effah et al., “Machine learning-assisted prediction of pneumonia based on non-invasive measures,” Front. Public Heal., vol. 10, 2022, doi: 10.3389/fpubh.2022.938801.

T. Z. Yehuala, B. M. Fente, S. M. Wubante, and N. M. Derseh, “Exploring machine learning algorithms to predict acute respiratory tract infection and identify its determinants among children under five in Sub-Saharan Africa,” Front. Pediatr., vol. 12, no. November, 2024, doi: 10.3389/fped.2024.1388820.

A. W. Demsash et al., “Data?driven machine learning Data-driven machine learning algorithm model for pneumonia prediction and determinant factor stratification among children aged 6–23 months in Ethiopia (BMC Infectious Diseases, (2025), 25, 1, (647), 10.1186/s12879-025-10916-4),” BMC Infect. Dis., vol. 25, no. 1, 2025, doi: 10.1186/s12879-025-11137-5.

T. Frondelius et al., “Early prediction of ventilator-associated pneumonia with machine learning models: A systematic review and meta-analysis of prediction model performance?,” Eur. J. Intern. Med., vol. 121, pp. 76–87, Mar. 2024, doi: 10.1016/j.ejim.2023.11.009.

I. A. Firdaus, “Deteksi Infeksi Mycoplasma Pneumoniae Pneumonia Menggunakan Komparasi Algoritma Klasifikasi Machine Learning,” 2022. doi: 10.31328/jointecs.v7i1.3242.

S. Mahmuda, “Implementasi Metode Random Forest pada Kategori Konten Kanal Youtube,” J. Jendela Mat., vol. 2, no. 01, pp. 21–31, 2024, doi: 10.57008/jjm.v2i01.633.

M. L. Amodio, “Effects of seasonal variation on MAP response of fresh-cut artichokes,” in Acta Horticulturae, 2024, pp. 175–176. doi: 10.17660/ActaHortic.2024.1386.24.

A. M. Shimaoka, R. C. Ferreira, and A. Goldman, “The evolution of CRISP-DM for Data Science: Methods, Processes and Frameworks,” SBC Rev. Comput. Sci., vol. 4, no. 1, pp. 28–43, 2024, doi: 10.5753/reviews.2024.3757.

M. Bouam et al., “Delayed diagnosis of pneumonia in the emergency department: factors associated and prognosis,” Front. Med., vol. 10, no. May, 2023, doi: 10.3389/fmed.2023.1042704.

S. H. Widiastuti, Purwanto, A. Wibowo, and R. W. Wisnuwardani, “Impact of Filter-Based Feature Selection on Prediction Model Performance for Early Detection of Stunting,” 2024 9th Int. Conf. Informatics Comput. ICIC 2024, pp. 1–7, 2024, doi: 10.1109/ICIC64337.2024.10956483.

D. Anggreani, Hamdani, and Nurmisba, “Grid Search Hyperparameter Analysis in Optimizing The Decision Tree Method for Diabetes Prediction,” Indones. J. Data Sci., vol. 5, no. 3, pp. 190–197, 2024, doi: 10.56705/ijodas.v5i3.190.

A. Hamid, L. Harmaji, and D. Mahdiana, “Deteksi Dini Hipertensi Pada Perusahaan Jasa Keamanan Menggunakan Metode Decision Tree,” J. Media Inform. Budidarma, vol. 8, no. 2, p. 900, 2024, doi: 10.30865/mib.v8i2.7513.

A. Saleh et al., “Early Detection of Dengue Hemorrhagic Fever Using Patient Medical Data with Ensemble Learning Methods,” vol. 8, no. 3, pp. 635–645, 2025, doi: http://dx.doi.org/10.24014/ijaidm.v8i3.38088.

A. C. Mawarni, R. Rusdah, L. L. Hin, and D. Anubhakti, “Deteksi Dini Gejala Awal Penyakit Diabetes Menggunakan Algoritma Random Forest,” IDEALIS Indones. J. Inf. Syst., vol. 6, no. 2, pp. 165–171, Jul. 2023, doi: 10.36080/idealis.v6i2.3018.

Ary Prandika Siregar, Dwi Priyadi Purba, Jojor Putri Pasaribu, and Khairul Reza Bakara, “Implementasi Algoritma Random Forest Dalam Klasifikasi Diagnosis Penyakit Stroke,” J. Penelit. Rumpun Ilmu Tek., vol. 2, no. 4, pp. 155–164, 2023, doi: 10.55606/juprit.v2i4.3039.

A. Wibowo, A. F. N. Masruriyah, and S. Rahmawati, “Refining Diabetes Diagnosis Models: The Impact of SMOTE on SVM, Logistic Regression, and Naïve Bayes for Imbalanced Datasets,” J. Electron. Electromed. Eng. Med. Informatics, vol. 7, no. 1, pp. 197–207, 2025, doi: 10.35882/jeeemi.v7i1.596.

N. Elsayed, S. A. Elaleem, and M. Marie, “Improving Prediction Accuracy using Random Forest Algorithm,” Int. J. Adv. Comput. Sci. Appl., vol. 15, no. 4, pp. 436–441, 2024, doi: 10.14569/IJACSA.2024.0150445.

D. Org Nur-Uz-Zaman, R. Ara, and M. Hossain, “A review on pneumonia in children: Clear insights,” Mediterr. J. Pharm. Pharm. Sci., vol. 4, no. 4, p. 57, 2024, doi: https://doi.org/10.5281/zenodo.14279043.

Y. Liang et al., “Early prediction of ventilator-associated pneumonia in critical care patients: a machine learning model,” BMC Pulm. Med., vol. 22, no. 1, pp. 1–9, 2022, doi: 10.1186/s12890-022-02031-w.

Y. C. Liu et al., “Evaluation of the Need for Intensive Care in Children with Pneumonia: Machine Learning Approach,” JMIR Med. Informatics, vol. 10, no. 1, pp. 1–11, 2022, doi: 10.2196/28934.