Klasifikasi Pneumonia dari Citra X-Ray Menggunakan Deep Learning dengan Metode Ensemble Stacking: Classification of Pneumonia from X-Ray Images Using Deep Learning with the Ensemble Stacking Method

Bagas Renovan Situmorang; Surya Alfredo Simanjuntak; Lasia Tomi Giantama Sembiring; Asian Rambe; Abdi Dharma

doi:10.57152/malcom.v6i3.2715

Authors

Bagas Renovan Situmorang Universitas Prima Indonesia
Surya Alfredo Simanjuntak Universitas Prima Indonesia
Lasia Tomi Giantama Sembiring Universitas Prima Indonesia
Asian Rambe Universitas Prima Indonesia
Abdi Dharma Universitas Prima Indonesia

DOI:

https://doi.org/10.57152/malcom.v6i3.2715

Keywords:

Chest X-ray, Convolutional Neural Network, Deep Learning, Ensemble Stacking, Klasifikasi Pneumonia

Abstract

Pneumonia merupakan salah satu penyebab utama kematian di dunia, terutama pada anak-anak dan lansia. Deteksi dini melalui citra rontgen dada sangat penting untuk membantu proses diagnosis dan menentukan pengobatan yang tepat. Namun, pembacaan gambar rontgen secara manual membutuhkan keahlian khusus dan berisiko menyebabkan kesalahan. Oleh karena itu, penelitian ini bertujuan untuk mengembangkan sistem klasifikasi pneumonia berbasis Deep Learning menggunakan metode Convolutional Neural Network (CNN) yang dikombinasikan dengan teknik ensemble learning. Penelitian ini menggunakan tiga model CNN, yaitu DenseNet161, ConvNeXt-Tiny, dan DenseNet201. Dataset berasal dari Kaggle dengan total 5.856 citra rontgen yang dibagi menjadi dua kelas, yaitu Normal dan Pneumonia. Proses penelitian mengikuti tahapan CRISP-DM, mulai dari pemahaman masalah hingga evaluasi model. Hasil pengujian menunjukkan bahwa DenseNet161 sebagai model tunggal terbaik dengan akurasi 65,85%. Setelah model digabungkan menggunakan ensemble stacking dengan regresi logistik sebagai meta-learner, akurasi meningkat menjadi 84,86% dengan recall 90,72%. Hasil ini menunjukkan bahwa pendekatan stacking ensemble mampu meningkatkan kinerja klasifikasi secara signifikan dibandingkan dengan model tunggal serta berpotensi membantu tenaga medis dalam mendiagnosis pneumonia. Kontribusi utama penelitian ini adalah menghasilkan sistem klasifikasi yang lebih andal dengan recall tinggi, sehingga dapat meminimalkan risiko false negative dan mendukung pengambilan keputusan medis untuk deteksi dini pneumonia yang lebih akurat dan efisien.

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