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All Journal Jurnal Ilmu Komputer dan Informatika Brilliance: Research of Artificial Intelligence
Caroline, Fionna
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Computer Science & IT Decision Sciences, Operations Research & Management Mathematics Other

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Analisis Sentimen Masyarakat terhadap Kasus Korupsi PT. Timah Menggunakan Metode Support Vector Machine Caroline, Fionna; Budi, Raden George Samuel; Rivan, Muhammad Ezar Al
Jurnal Ilmu Komputer dan Informatika Vol 4 No 1 (2024): JIKI - Juni 2024
Publisher : CV Firmos

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54082/jiki.141

Abstract

Korupsi adalah penyalahgunaan jabatan publik untuk keuntungan pribadi yang dimana korupsi ini dapat memberikan kerugian besar bagi negara maupun masyarakat. Topik yang dipilih untuk penelitian ini adalah kasus korupsi PT. Timah yang sedang hangat dibicarakan dikarenakan kerugian negara yang mencapai 271 T. Untuk membantu analisis dalam penelitian ini, dibangunlah sebuah sistem yang dapat mendeteksi sentimen publik yang sudah dikumpulkan dari platform Youtube dengan metode Support Vector Machine. Model yang sudah dilatih dengan dataset akan diseimbangkan dengan SMOTE karena tidak meratanya kelas klasifikasi. Model klasifikasi yang telah dibangun dengan support vektor machine mendapatkan hasil presisi pada sentimen negatif 91% dan sentimen positif 44%, recall pada sentimen negatif 96% dan sentimen positif 22%, F1-Score pada sentimen negatif 93% dan sentimen positif 30%, serta jumlah sample pada kelas sentimen negatif 140 dan kelas sentimen positif 18.
Comparison of XGBoost and LightGBM Algorithms in Predicting Heart Disease Caroline, Fionna; Rachmat, Nur
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.7505

Abstract

Heart disease remains a leading cause of mortality worldwide, underscoring the need for early and accurate diagnosis to reduce complications and improve patient outcomes. Recent advances in machine learning have enabled the development of predictive models that assist healthcare professionals in disease detection using patient medical records. This study aims to develop and compare the performance of Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) for heart disease prediction. The dataset used in this research was obtained from the UCI Machine Learning Repository and consists of 303 patient records with binary class labels indicating the presence or absence of heart disease. Data preprocessing involved feature standardization using StandardScaler and handling class imbalance through the Synthetic Minority Over-sampling Technique (SMOTE). Model evaluation was conducted using Stratified K-Fold Cross Validation with K values of 3, 5, and 7 to ensure robust and unbiased performance assessment. Hyperparameter optimization was carried out using RandomizedSearchCV to efficiently identify optimal model configurations. Experimental results indicate that both XGBoost and LightGBM achieved strong classification performance, with accuracy exceeding 80% and AUC values above 0.89. LightGBM demonstrated slightly superior performance in terms of average accuracy, F1-score, and stability across folds, while XGBoost achieved higher precision, reflecting better control of false positives. Overall, both algorithms are effective for heart disease prediction, supporting the potential of machine learning in early disease detection and clinical decision-support systems.
Co-Authors Budi, Raden George Samuel M Ezar Al Rivan Nur Rachmat