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All Journal Techno.Com: Jurnal Teknologi Informasi JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING
Putra, Bernardus Septian Cahya
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Computer Science & IT Engineering

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Efektivitas Algoritma Random Forest, XGBoost, dan Logistic Regression dalam Prediksi Penyakit Paru-paru Putra, Bernardus Septian Cahya; Tahyudin, Imam; Kusuma, Bagus Adhi; Isnaini, Khairunnisak Nur
Techno.Com Vol. 23 No. 4 (2024): November 2024
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v23i4.11705

Abstract

Penyakit paru-paru, seperti pneumonia dan kanker paru-paru, menjadi masalah kesehatan global dengan tingkat kematian tinggi, terutama dipengaruhi oleh polusi udara, infeksi, dan kebiasaan merokok. Pencegahan dan deteksi dini sangat penting dalam mengurangi dampaknya. Algoritma yang digunakan dalam penelitian ini meliputi Random Forest, XGBoost, dan Logistic Regression. Tujuannya yaitu untuk membandingkan performa tiga algoritma machine learning dalam mengklasifikasikan penyakit paru-paru menggunakan metrik evaluasi seperti, akurasi, presisi, recall, dan F1-score.  Setelah hyperparameter tuning, XGBoost menunjukkan hasil terbaik dengan akurasi 94,44%, presisi 94,98%, recall 94,44%, dan F1-score 94,41%, menunjukkan keseimbangan optimal antara presisi dan recall. Random Forest juga memberikan hasil yang sebanding dengan XGBoost dengan akurasi dan presisi yang tinggi. Sementara itu, Logistic Regression menunjukkan keterbatasan dalam menangani data yang kompleks, dengan performa yang lebih rendah pada seluruh metrik evaluasi. Penelitian ini menunjukkan bahwa algoritma berbasis pohon keputusan seperti XGBoost dan Random Forest lebih unggul untuk klasifikasi penyakit paru-paru, menjadikannya metode yang lebih andal untuk mendukung deteksi dini penyakit ini.   Kata kunci: Hyperparameter Tuning, Logistic Regression, Penyakit Paru-paru, Random Forest, XGBoost.
Performance Evaluation of CNN-LSTM and CNN-FNN Combinations for Pneumonia Classification Using Chest X-ray Images Putra, Bernardus Septian Cahya; Tahyudin, Imam
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 8 No. 2 (2025): Issues January 2025
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v8i2.13503

Abstract

Pneumonia is one of the deadliest infectious diseases worldwide, particularly affecting children under five years old and the elderly, with a significant mortality rate annually. This disease is caused by bacterial, viral, or fungal infections, leading to inflammation in the air sacs (alveoli) of the lungs, which disrupts respiratory function. A major challenge in diagnosing pneumonia lies in the reliance on radiological expertise to interpret chest X-ray images, a process that is time-consuming and prone to errors in interpretation. This study aims to compare the performance of deep learning models, specifically the combination of Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) and CNN with Feedforward Neural Networks (FNN), in classifying pneumonia based on chest X-ray images. The results indicate that the CNN & LSTM model achieved an accuracy of 96.59%, a loss of 9.95%, precision of 96%, recall of 95%, and F1-score of 96%, slightly outperforming the CNN & FNN model, which achieved an accuracy of 96.13%, a loss of 12.16%, precision of 96%, recall of 94%, and F1-score of 95%. The advantage of CNN & LSTM lies in its ability to capture sequential patterns through LSTM, making it more effective in detecting positive pneumonia cases. In conclusion, the CNN & LSTM model outperforms the CNN & FNN model in accuracy, recall, and F1-score, making it a more reliable choice for automatic pneumonia classification. The findings suggest the potential use of deep learning models, particularly CNN & LSTM, to support medical professionals and the public in quickly and accurately detecting pneumonia through chest X-ray images analysis
Co-Authors Imam Tahyudin Isnaini, Khairunnisak Nur Kusuma, Bagus Adhi