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All Journal TATAL JOURNAL OF APPLIED INFORMATICS AND COMPUTING
Laily Hermawanti
Jurusan Teknik Informatika, Universitas Sultan Fatah Jl. Diponegoro 1A, Jogoloyo – Demak, Indonesia
Computer Science & IT Education

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PENERAPAN ALGORITMA NAÏVE BAYES UNTUK DETEKSI BAKTERI E-COLI Hermawanti, Laily
TATAL Vol 8, No 1 (2012): TATAL VOL. 8 NO.1 SEPTEMBER 2012
Publisher : TATAL

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Abstract

Bakteri e-coli merupakan bakteri mikroskopik yang memiliki ukuran sangat kecil dan hanya bisa dilihat dengan mikroskop. Penelitian ini menggunakan algoritma Naïve Bayes untuk mendeteksi bakteri ecoli. Penelitian ini menghasilkan nilai akurasi untuk algoritma klasifikasi Naive Bayes senilai 98.18% dan nilai Area Under Curve (AUC) untuk algoritma Naive Bayes senilai 0.871, sehingga penelitian ini dalam mendeteksi bakteri e-coli menhasilkan hasil yang akurat.Kata kunci : Bakteri e-coli, algoritma Naïve Bayes
PENERAPAN ALGORITMA KLASIFIKASI C4.5 UNTUK DIAGNOSIS PENYAKIT KANKER PAYUDARA Hermawanti, Laily
TATAL Vol 7, No 2 (2012): TATAL VOL. 7 NO.2 MARET 2012
Publisher : TATAL

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Abstract

Penyakit kanker payudara merupakan . Penelitian ini menggunakan algoritma C4.5 untuk mendiagnosis penyakit kanker payudara. Penelitian ini menghasilkan nilai akurasi untuk algoritma klasifikasi C4.5 senilai 94.56% dan nilai Area Under Curve (AUC) untuk algoritma Naive Bayes senilai 0.941, sehingga penelitian ini dalam mendiagnosis penyakit kanker payudara menghasilkan hasil yang akurat.Kata Kunci: Penyakit kanker payudara, algoritma C4.5
Interpretable Machine Learning with SHAP and XGBoost for Lung Cancer Prediction Insights Kurniawan, Taufik; Hermawanti, Laily; Safriandono, Achmad Nuruddin
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8395

Abstract

Lung cancer remains one of the leading causes of death worldwide, and early detection through accurate and reliable methods is essential to improve patient prognosis. This study proposes a lung cancer classification model that integrates XGBoost with SHapley Additive exPlanations (SHAP) and Random Over Sampling (ROS) techniques to address the data imbalance problem. Using hyperparameter optimization through Optuna, the resulting model demonstrated superior performance, with an average accuracy of 96.84%, precision of 99.23%, recall of 94.51%, F1-score of 96.74%, specificity of 99.17%, and AUC of 96.84% in a 10-fold cross-validation evaluation. SHAP analysis provided significant interpretability, identifying key features such as gender, smoking habits, and physical signs of yellow fingers as the factors that most influence the model's predictions. The results of this study indicate that the proposed model is not only accurate, but also interpretable, making a significant contribution to supporting better clinical decision making in lung cancer diagnosis.
Co-Authors Achmad Nuruddin Safriandono Taufik Kurniawan