Perbandingan Algoritma Machine Learning untuk Klasifikasi Risiko Penyakit Paru Berdasarkan Data Diagnostik Pasien

Alwan Kapi Muntaha, Kevin Dwi Satria, Desiana Nuranudin Putri, Sumanto Sumanto, Roida Pakpahan, Imam Budiawan

Abstract

Abstrak : 

Penyakit paru-paru termasuk salah satu faktor utama penyebab tingginya angka kematian di seluruh dunia. Kondisi ini terjadi karena penyakit paru-paru sering kali sulit terdeteksi pada tahap awal akibat gejalanya yang tidak spesifik. Perkembangan teknologi machine learning memberikan peluang untuk membantu proses diagnosis secara otomatis dengan memanfaatkan data diagnostik pasien. Penelitian ini bertujuan untuk mengklasifikasikan risiko penyakit paru menggunakan berbagai algoritma machine learning pada aplikasi Orange3, serta menentukan model dengan akurasi terbaik. Dataset yang digunakan terdiri dari 5.000 data pasien dengan 18 atribut yang mencakup faktor demografis, gaya hidup, riwayat medis, dan kondisi klinis seperti kadar oksigen, tingkat stres, dan kebiasaan merokok. Lima algoritma diuji, yaitu iDecision Tree, Naïve Bayes, Support Vector Machine (SVM), k-Nearest Neighbor (kNN), dan Neural Network. Hasil pengujian menunjukkan bahwa Neural Network menghasilkan nilai akurasi tertinggi sebesar 89,15%, diikuti oleh Decision Tree (85,12%) dan Naïve Bayes (83,63%). Temuan ini membuktikan bahwa Neural Network lebih unggul dalam mengenali pola kompleks antarvariabel dan mampu memberikan prediksi yang lebih akurat. Dengan demikian, penelitian ini menegaskan potensi penerapan machine learning berbasis data diagnostik non-citra sebagai sistem pendukung keputusan untuk diagnosis dini penyakit paru.

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Abstract : 

Lung disease is a major contributing factor to high mortality rates worldwide. This is because lung disease is often difficult to detect in its early stages due to its nonspecific symptoms. The development of machine learning technology provides an opportunity to assist the automated diagnosis process by utilizing patient diagnostic data. This study aims to classify the risk of lung disease using various machine learning algorithms in the Orange3 application, and determine the model with the best accuracy. The dataset used consists of 5,000 patient data with 18 attributes covering demographic factors, lifestyle, medical history, and clinical conditions such as oxygen levels, stress levels, and smoking habits. Five algorithms were tested: Decision Tree, Naïve Bayes, Support Vector Machine (SVM), k-Nearest Neighbor (kNN), and Neural Network. The test results showed that Neural Network produced the highest accuracy value of 89.15%, followed by Decision Tree (85.12%) and Naïve Bayes (83.63%). These findings prove that Neural Network is superior in recognizing complex patterns between variables and is able to provide more accurate predictions. Thus, this study confirms the potential of applying machine learning based on non-image diagnostic data as a decision support system for early diagnosis of lung disease.

Keywords

lung diseases; machine learning; classification; neural network; decision tree; naïve bayes; SVM; kNN; Orange3; decision support

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References

[1] World Health Organization. (2023, June.26) Lung Cancer [online]. Available: https://www.who.int/news-room/fact-sheets/detail/lung-cancer

[2] D. Aprilianto and E. Rizal, “Klasifikasi Penyakit Kanker Paru Menggunakan Algoritma Random Forest Berbasis Streamlit,” vol. 9, p. 2025, doi: 10.47002/metik.v9i2.1076.

[3] A. Sholiha and Z. Fatah, “Klasifikasi Penyakit Paru-Paru Menggunakan Data Mining Decision Tree,” 2025.

[4] B. Riyadi, D. Saputra, and A. A. Sari, “Penerapan Algoritma Naïve Bayes Untuk Prediksi Penyakit Paru (Studi Kasus : Rumah Sakit Umum Daerah Dokter Soeratno Gemolong),” Seminar Nasional Teknologi Informasi dan Bisnis (SENATIB), p. 2025.

[5] Y. Amadeus Ishak and M. Dolok Lauro, “Jurnal Ilmu Komputer dan Sistem Informasi Prediksi Pasien Kanker Paru-Paru Menggunakan Metode K-Nearest Neighbors, Decision Tree, Dan Naive Bayes.”

[6] M. Javid and H. Xie, “Deep Learning Models for Classification of Lung Diseases,” European Journal of Theoretical and Applied Sciences, vol. 2, no. 2, pp. 858–868, Mar. 2024, doi: 10.59324/ejtas.2024.2(2).77.

[7] E. E. Khaing, T. Z. Aung, E. Ei, and K. E. Tone, “Modeling Deep Feature for Lung Disease Classification in Chest X-ray Images 34 Modeling Deep Feature for Lung Disease Classification in Chest X-ray Images.” [Online]. Available: http://jit.it.msu.ac.th

[8] M. Jasmine Pemeena Priyadarsini et al., “Lung Diseases Detection Using Various Deep Learning Algorithms,” J Healthc Eng, vol. 2023, 2023, doi: 10.1155/2023/3563696.

[9] P. Syifa, Safwandi, and Z. Fitri, “Sistem Pakar Diagnosis Penyakit Paru Menggunakan Metode Convolutional Neural Network Dan Rule Based System,” Rabit : Jurnal Teknologi dan Sistem Informasi Univrab, vol. 10, no. 2, pp. 1380–1392, Jul. 2025, doi: 10.36341/rabit.v10i2.6548.

[10] A. Milionita, B. Mayor, C. E. Mongi, W. Christo, and D. Weku, “IJIDS Sistem Deteksi Penyakit Pneumonia Menggunakan Algoritma Klasifikasi Convolutional Neural Network dan Support Vector Machine Dengan Citra Chest X-Ray,” 2022. [Online]. Available: https://ejournal.unsrat.ac.id/v3/index.php/IJIDS/index

[11] P. Rosyani et al., “Klasifikasi Penyakit Paru-Paru Berbasis Pengolahan Citra X-Ray Menggunakan Convolutional Neural Network.” [Online]. Available: https://jurnalmahasiswa.com/index.php/biikma

[12] A. Ilmiah Aplikasi Teknologi et al., “JURNAL APTEK Klasifikasi Kanker Paru-Paru berdasarkan Data Citra CT-Scan: Berbasis Gray Level Co-Occurrence Matrix dan Extreme Learning Machine”, doi: 10.30606/aptek.v17i2.3553.

[13] A. D. Azzumzumi, M. Hanafi, and W. M. P. Dhuhita, “Klasifikasi Penyakit Paru-Paru Berdasarkan Peningkatan Kualitas Kontras dan EfficientNet Menggunakan Gambar X-Ray,” Teknika, vol. 13, no. 2, pp. 293–300, Jul. 2024, doi: 10.34148/teknika.v13i2.881.

[14] L. Cong, Y. Chen, X. He, M. KuErBan, C. Wu, and L. Chen, “Diagnosis accuracy of machine learning for idiopathic pulmonary fibrosis: a systematic review and meta-analysis,” Eur J Med Res, vol. 30, no. 1, Apr. 2025, doi: 10.1186/s40001-025-02501-x.

[15] J. P. Garcia-Mendez et al., “Machine Learning for Automated Classification of Abnormal Lung Sounds Obtained from Public Databases: A Systematic Review,” Oct. 01, 2023, Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/bioengineering10101155.

DOI: https://doi.org/10.20961/ijai.v10i1.110867

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