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All Journal Al-Ittihad: Jurnal Pemikiran dan Hukum Islam Tech : Journal of Engineering Science
Herlinawati
Politeknik Negeri Banjarmasin
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Peranan Hadis dalam Perkembangan Ilmu Pengetahuan dan Peradaban Herlinawati
Al-Ittihad: Jurnal Pemikiran dan Hukum Islam Vol 2 No 2 (2016): Juli-Desember
Publisher : Sekolah Tinggi Islam Syariah (STIS) Al-Ittihad Bima

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61817/ittihad.v2i2.21

Abstract

Perkembangan ilmu pengetahuan dan peradaban menuntut adanya rambu-rambu yang menjadi landasannya. Hadis sebagai sumber ajaran Islam yang ke dua setelah al-Qur’an, memperkenalkan prinsip-prinsip atau rambu-rambu yang menjadi dasar perilaku berbudaya, selain itu juga memuat tentang teori ilmu pengetahuan dan peradaban. Oleh karenanya, selain al-Qur’an, hadis bisa dijadikan landasan dalam perkembangan ilmu pengetahuan dan peradaban. Hadis bisa menghantarkan manusia dari pemahaman yang dangkal dan primitif menuju pemahaman yang luas dan mendalam mengenai alam dan kehidupan, yang dikenal dengan istilah al-fiqh al-hadlari (fiqh peradaban). Selain itu juga memuat ajaran tentang al-wa’yu al-hadlari (kesadaran peradaban). Bercermin dari kejayaan masa lalu. Ilmu pengetahuan dan peradaban akan berkembang pesat apabila umat Islam memperhatikan Sunnatullah serta memelihara hukum sebab akibat.
Impact of Feature Selection on the Performance of KNN and SVM in Heart Disease Prediction Dhiyaussalam; M. Helmy Noor; Herlinawati; Isna Wardiah
Tech : Journal of Engineering Science Vol 1 No 1 (2025): Pengembangan dan Penerapan Solusi Rekayasa untuk Tantangan Lingkungan, Industri,
Publisher : Yayasan Penelitian dan Pengabdian Masyarakat Sisi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69836/tech.v1i1.353

Abstract

Feature selection plays a vital role in enhancing the performance of machine learning models by eliminating irrelevant or redundant attributes. This study investigates the impact of feature selection on the classification accuracy of K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) in predicting heart disease. Using the UCI Heart Disease data, which initially includes 13 input features, feature importance scores were calculated using a Random Forest model. A threshold-based method was then applied to identify and retain the most informative features. Through iterative testing of importance thresholds, a value of 0.03 yielded the best results, reducing the feature set from 13 to 9 attributes. Classification models were trained and evaluated using full and reduced feature sets. Performance was assessed using accuracy, precision, recall, and F1-score and validated with 5-fold cross-validation. The results demonstrate significant performance gains after feature selection. The KNN classifier improved accuracy from 83% to 92%, with notable gains in recall and F1-score for the positive class. Similarly, SVM achieved 92% accuracy, with improved precision and overall performance stability. These findings suggest that data-driven feature reduction simplifies the model and enhances its predictive power. This study systematically compares feature selection effects on two distinct machine learning algorithms and offers practical insights for optimizing medical prediction models in clinical decision support systems.
Co-Authors Dhiyaussalam M. Helmy Noor Wardiah, Isna