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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Ulead : Jurnal E-pengabdian
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Computer Science & IT

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Comparison of Xgboost, Random Forest and Logistic Regression Algorithms in Stroke Disease Classification Sitompul, Lia Relita; Nababan, Adli Abdillah; Manihuruk, Mey Lestari; Ponsen, Wildan Andika; Supriyandi
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14794

Abstract

Stroke remains a critical global health concern, ranking as the second leading cause of mortality and third cause of disability worldwide. Early detection and accurate classification of stroke risk could significantly improve patient outcomes through timely interventions. This research evaluates and compares the performance of three machine learning algorithms—XGBoost, Random Forest, and Logistic Regression—for stroke disease classification using a dataset of 5,110 patient records with 12 attributes including demographic, lifestyle, and health factors. Due to significant data imbalance between stroke and non-stroke cases, Synthetic Minority Over-sampling Technique (SMOTE) was applied to enhance model performance. Comprehensive evaluation metrics including accuracy, precision, recall, and F1-score were utilized to assess each algorithm's effectiveness. Results demonstrate that XGBoost achieved superior performance with 95% accuracy, followed by Random Forest at 94% and Logistic Regression at 82%. Feature importance analysis identified age, average blood glucose level, and history of heart disease as the most significant predictors for stroke diagnosis. This study contributes to the advancement of clinical decision support systems by highlighting the effectiveness of ensemble learning approaches for stroke prediction, potentially enabling earlier interventions and improved patient management. These findings suggest that integration of machine learning tools in clinical settings could enhance stroke risk assessment, though further validation with diverse patient populations is recommended for broader implementation.
Membangun Literasi Kewirausahaan Pada Siswa SMA Swasta Eka Prasetya Medan Siagian, Lasma; Tambunan, Indah Doa; Supriyandi
ULEAD : Jurnal E-Pengabdian Volume 4 Nomor 1 Juli 2024
Publisher : Fakultas Ilmu Komputer, Universitas Katolik Santo Thomas Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54367/ulead.v4i1.3974

Abstract

Pengabdian ini bertujuan untuk membangun literasi kewirausahaan pada siswa SMA Swasta Eka Prasetya Medan, dilatarbelakangi oleh rendahnya tingkat literasi kewirausahaan di kalangan siswa yang dapat menghambat kemampuan mereka untuk berinovasi dan bersaing di dunia kerja. Urgensi pengabdian ini terletak pada pentingnya mempersiapkan generasi muda dengan keterampilan kewirausahaan sejak dini untuk menghadapi tantangan ekonomi global yang semakin kompleks. Metode penelitian yang digunakan adalah metode kualitatif dengan pendekatan studi kasus, di mana data dikumpulkan melalui observasi, wawancara mendalam, dan analisis dokumen terkait program kewirausahaan yang diterapkan di sekolah. Hasil pengabdian menunjukkan bahwa program literasi kewirausahaan yang diterapkan secara signifikan meningkatkan pemahaman siswa tentang konsep kewirausahaan dan keterampilan praktis mereka dalam merancang serta menjalankan proyek bisnis sederhana, selain meningkatkan motivasi dan kreativitas siswa. Integrasi program literasi kewirausahaan dalam kurikulum sekolah menengah atas memberikan dampak positif terhadap keterampilan dan kesiapan siswa dalam memasuki dunia kerja.
Co-Authors Manihuruk, Mey Lestari Nababan, Adli Abdillah Ponsen, Wildan Andika siagian, lasma Sitompul, Lia Relita Tambunan, Indah Doa