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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal Sistem Cerdas DiJITAC : Digital Journal of Information Technology and Communication Jurnal Masyarakat Madani Indonesia
Mawardi Kudin
Universitas Sulawesi Barat
Religion Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Public Health Social Sciences

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Analysis of Diabetes Classification Performance Improvement Using Ensemble Bagging and K-Fold Mawardi Kudin; Abd Salam At Taqwa; Angga Kurniawan; Chairi Nur Insani
Jurnal Sistem Cerdas Vol. 9 No. 1 (2026)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v9i1.630

Abstract

Diabetes mellitus represents a long-term metabolic disorder whose global incidence continues to rise, making precise early identification essential to minimize severe complications. Machine learning techniques have been extensively utilized for diabetes classification; however, single-model approaches often suffer from performance constraints, such as susceptibility to overfitting and high variability in prediction outcomes. To address these challenges, this research introduces a bagging-based ensemble learning strategy integrated with K-Fold Cross Validation to enhance both predictive accuracy and model robustness. The study employs the Pima Indians Diabetes Dataset, which contains 768 patient records described by eight clinical features and one outcome variable. Eight classification methods—Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Decision Tree, Random Forest, Naïve Bayes, Gradient Boosting, and XGBoost—were assessed individually and within the proposed ensemble framework. Model effectiveness was measured using accuracy, precision, recall, and F1-score derived from the confusion matrix. The findings indicate that the ensemble bagging approach generally strengthens model stability and yields improvements in accuracy and precision across most algorithms. Notably, K-Nearest Neighbors and XGBoost demonstrated the most stable gains following ensemble integration. Nevertheless, enhancements in precision were frequently associated with a reduction in recall, reflecting a trade-off in identifying positive cases. In summary, the integration of bagging and K-Fold Cross Validation provides a more resilient and dependable classification model, offering strong potential for supporting clinical decision-making in early diabetes detection.
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.
ERP TERINTEGRASI PADA KOPERASI SYARIAH UNTUK OPTIMALISASI PENILAIAN KELAYAKAN KREDIT, KEPATUHAN SYARIAH, DAN MANAJEMEN RISIKO BERBASIS SLIK OJK Sufyaldy; Suherman; Muhammad Shaleh; Karkhi Mirza Kavari; Hasnawati; Mawardi Kudin
DiJITAC : Digital Journal of Information Technology and Communication DiJITAC, Vol 6 No.2, April 2026
Publisher : Universitas Islam Negeri Sultan Aji Muhammad Idris Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21093/fhz0az18

Abstract

Lembaga Keuangan Mikro Syariah (LKMS), khususnya Koperasi Simpan Pinjam dan Pembiayaan Syariah (KSPPS) atau Baitul Maal wat Tamwil (BMT), memiliki peran strategis dalam mendorong inklusi keuangan bagi sektor Usaha Mikro, Kecil, dan Menengah (UMKM) di Indonesia. Namun, eksistensi dan operasional entitas ini kerap dihadapkan pada kendala fragmentasi sistem informasi, tingginya angka pembiayaan bermasalah (Non-Performing Financing/NPF), serta ketidakpatuhan terhadap standar pelaporan akuntansi dan regulasi pengawasan. Penelitian ini bertujuan untuk merancang, mengembangkan, dan mengevaluasi sebuah platform sistem informasi terintegrasi berbasis Enterprise Resource Planning (ERP) khusus untuk KSPPS. Pengembangan sistem ini mengadopsi kerangka kerja Design Science Research Methodology (DSRM) yang disinergikan dengan pendekatan User-Centered Design (UCD). Platform ERP yang dibangun menggunakan lingkungan kerja ERP (modul tipd_koperasi) dengan arsitektur Model-View-Controller (MVC) yang menjembatani dualisme fungsi BMT, yakni fungsi komersial (Tamwil) dan fungsi sosial (Maal). Hasil implementasi menunjukkan bahwa sistem mampu mengotomatisasi siklus akuntansi syariah berdasarkan standar PSAK 106, menghitung rasio tingkat kesehatan koperasi sesuai regulasi Kementerian Koperasi dan UKM, serta menyediakan instrumen mitigasi risiko pembiayaan melalui algoritma credit scoring internal yang terintegrasi secara konseptual dengan Sistem Layanan Informasi Keuangan (SLIK) Otoritas Jasa Keuangan (OJK). Intervensi teknologi ini memberikan implikasi manajerial berupa efisiensi operasional, eliminasi bias subjektif dalam persetujuan kredit, dan peningkatan transparansi tata kelola lembaga keuangan mikro syariah.
Co-Authors Abd Salam At Taqwa ABDUL SALAM AT-TAQWA Angga Kurniawan Chairi Nur Insani Hasnawati Karkhi Mirza Kavari Muhammad Shaleh Nisrina Hanifa Setiono Sufyaldy Suherman