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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal Masyarakat Madani Indonesia
ABDUL SALAM AT-TAQWA
Universitas Negeri Makassar
Humanities Computer Science & IT Education Engineering Public Health Social Sciences

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EVALUATION OF MACHINE LEARNING AND DEEP LEARNING ALGORITHMS WITH FEATURE SCALING AND K-FOLD CROSS-VALIDATION FOR DIABETES CLASSIFICATION ANGGA KURNIAWAN; MAWARDI KUDIN; ABDUL SALAM AT-TAQWA
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7005

Abstract

Diabetes is a growing global health challenge that requires advanced approaches for early detection and prevention. Previous research has often been limited to evaluation using a single data split, which can potentially yield unreliable model performance estimates. This study addresses that limitation by conducting a comprehensive and rigorous evaluation of eight machine learning algorithms—including classical models, ensemble methods, and Multi-Layer Perceptron (MLP)—using the Pima Indians Diabetes dataset. The applied methodology includes data preprocessing, systematic hyperparameter optimization, and, most importantly, robust performance validation through Multi K-Fold Cross-Validation (K=5,10,15,20). Initial results showed perfect accuracy (100%) for the K-Nearest Neighbors (KNN) model; however, this finding was proven to be an artifact of a fortunate data split (lucky split) after undergoing cross-validation procedures. The more reliable validation results instead revealed the exceptional superiority of the Multi-Layer Perceptron (MLP) model, which achieved 94.96% accuracy with high stability (standard deviation 0.0356) in 20-fold cross-validation. Meanwhile, classical models such as Logistic Regression and Support Vector Machine (SVM) demonstrated high robustness and consistency. These findings significantly contribute to the field of health informatics by emphasizing the importance of rigorous validation methodology and identifying MLP as a highly strong predictive model candidate for diabetes detection. For practical application, this study recommends MLP along with stable classical models as the foundation for developing reliable clinical decision support systems, with the note that further external validation testing is necessary.
Meningkatkan Literasi Digital Siswa SMA/SMK melalui Kurikulum Koding Berbasis Project-Based Learning di Sulawesi Selatan Angga Kurniawan; Mawardi Kudin; Abdul Salam At-Taqwa; Nisrina Hanifa Setiono
Jurnal Masyarakat Madani Indonesia Vol. 5 No. 2 (2026): Mei
Publisher : Alesha Media Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59025/q3fcw725

Abstract

Program pengabdian ini bertujuan meningkatkan literasi digital siswa SMA/SMK di Sulawesi Selatan melalui kurikulum koding berbasis Project-Based Learning (PjBL) selama 16 sesi. Mitra menghadapi tiga masalah utama: belum ada pembelajaran koding sistematis, rendahnya kemampuan computational thinking, dan kesiapan guru terbatas dalam PjBL. Program dilaksanakan daring via Zoom, melibatkan 86 siswa dari tiga sekolah (SMAN 3 Bone, SMAN 12 Bone, SMAN 9 Bulukumba). Metode PjBL memandu peserta membuat aplikasi (kalkulator, kasir) berbasis Python/web. Hasil evaluasi menunjukkan peningkatan rata-rata kognitif sebesar 40.2 poin (84%) dari pre-test ke post-test, dengan 82.5% peserta menghasilkan proyek akhir berkategori "Memuaskan" hingga "Sangat Memuaskan". Rata-rata kehadiran 85,7%. Program ini terbukti efektif meningkatkan literasi digital dan keterampilan koding aplikatif, serta menghasilkan model replikasi untuk pendidikan literasi digital terukur di daerah lain.
Co-Authors Angga Kurniawan Mawardi Kudin Nisrina Hanifa Setiono