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All Journal Jurnal Ilmiah MITSU (Media Informasi Teknik Sipil Universitas Wiraraja) Sistemasi: Jurnal Sistem Informasi
Lestari, Ninik
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Environmental Science Transportation

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Comparison of Machine Learning Models for Predicting Lung Cancer Severity Lestari, Ninik; Susanto, Erliyan Redy
Sistemasi: Jurnal Sistem Informasi Vol 14, No 6 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i6.5258

Abstract

This study aims to compare the performance of four machine learning algorithms Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR), and K-Nearest Neighbors (KNN) in predicting lung cancer severity based on patient medical data. The dataset includes clinical information with the target variable categorized into three severity levels: low, medium, and high. Experiments were conducted using an 80:20 train-test split without feature scaling. The results show that RF achieved 100% accuracy, LR 99%, KNN 82%, and SVM 43%. The superior performance of Random Forest can be attributed to its ensemble of decision trees, which mitigates overfitting in medium-dimensional numerical features, whereas SVM (kernel = RBF, C = 1.0, gamma = "scale") failed to adapt due to the absence of scaling and hyperparameter tuning. Recall, precision, and F1-score further confirm the dominance of RF and LR. This study provides insights into the effectiveness of machine learning algorithms in lung cancer diagnosis and highlights the contribution of a multi-algorithm approach. The findings recommend using RF as the primary model and LR as a complementary control within clinical decision support systems, enabling physicians to make earlier, more personalized treatment decisions and ultimately improve lung cancer patient prognosis.
The PREDIKSI KONDISI KERUSAKAN JALAN BERDASARKAN NILAI IRI MENGGUNAKAN APLIKASI ROADROID (Studi Kasus: Ruas Jalan Kelas III dengan Volume Lalu Lintas Rendah di Kabupaten Lumajang) Maliq, Tatang Maulana; Hasanuddin, Akhmad; ., Syaripin; Lestari, Ninik
Jurnal Ilmiah MITSU (Media Informasi Teknik Sipil Universitas Wiraraja) Vol 14 No 1 (2026): Jurnal Ilmiah MITSU
Publisher : Fakultas Teknik, Universitas Wiraraja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24929/ft.v14i1.4346

Abstract

This study aims to predict road damage conditions based on the International Roughness Index (IRI) value using the Roadroid application on class III roads with low traffic volume in Lumajang Regency. This study is important because the road section has not undergone maintenance since 2018, thus potentially experiencing accelerated degradation of pavement performance. IRI data for 2023 was obtained through a field survey using the Android-based Roadroid application and compared with IRI data for 2018 measured using the National Association of Australian State Road Authorities (NAASRA) tool to determine the rate of increase in damage and predict conditions for the next five years. The results showed that the average IRI value in 2023 was in the severely damaged category with a value between 12.5–20.5 m/km. The relationship model between IRI and Average Daily Traffic (ADR) of heavy vehicles produced the equation y = 0.2889x + 1.2993 (R² = 0.935), which indicates a significant effect of heavy vehicle volume on increasing road surface roughness. Predictive simulations indicate that several road segments are expected to reach a state of minor damage within the next 2–4 years, even with overlays. The novelty of this research lies in the integration of historical and survey data with the Roadroid application for predictive modeling of road damage on low-traffic sections as a basis for preventive maintenance planning.
Co-Authors ., Syaripin Akhmad Hasanuddin Maliq, Tatang Maulana Susanto, Erliyan Redy