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All Journal Jurnal Riset Multidisiplin Edukasi Ar-Rasyid: Jurnal Publikasi Penelitian Ilmiah
Johan Abisay Tambunan
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Religion Humanities Economics, Econometrics & Finance Education Social Sciences Other

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Perancangan Scema Data Warehouse Studi Kasus Penyakit Diabetes Melitus Ardi Riyadi; Johan Abisay Tambunan; Andri Wijaya
Ar-Rasyid: Jurnal Publikasi Penelitian Ilmiah Vol. 1 No. 6 (2025): Ar-Rasyid: Jurnal Publikasi Penelitian Ilmiah (Bulan Desember 2025)
Publisher : PT. Saha Kreasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64788/ar-rasyid.v1i6.230

Abstract

Diabetes Mellitus is a chronic disease with an increasing global prevalence, requiring integrated and data-driven health data management. However, healthcare institutions often face challenges in managing patient data that are distributed across multiple formats and fragmented storage systems (data silos), which hinders strategic analysis and effective decision-making. This study aims to design and implement a Data Warehouse to integrate diabetes patient data as a foundation for clinical and managerial decision support. The research methodology applies a multidimensional schema design using the Star Schema approach, consisting of one fact table and three main dimension tables: demographics, lifestyle, and medical conditions. The Extract, Transform, and Load (ETL) process was implemented using SQL Server Integration Services (SSIS) to cleanse and centralize data from operational sources. The dataset used in this study consists of 10,000 anonymized patient records that have undergone data profiling and data cleansing processes. The results indicate that the developed Data Warehouse is capable of integrating data consistently and supporting multidimensional analysis. Data visualization using Tableau Public reveals a correlation between Body Mass Index (BMI) and diabetes status, where patients diagnosed with diabetes exhibit a higher average BMI compared to non-diabetic patients. This implementation improves data access efficiency and facilitates the identification of health risk patterns. Therefore, the proposed Data Warehouse can serve as a foundation for a healthcare analytics system that supports data-driven strategies for diabetes prevention and management.
Perbandingan Akurasi Support Vector Machine dan Random Forest pada Prediksi Diabetes Melitus Ardi Riyadi; Johan Abisay Tambunan; Andri Wijaya
Jurnal Riset Multidisiplin Edukasi Vol. 2 No. 12 (2025): Jurnal Riset Multidisiplin Edukasi (Edisi Desember 2025)
Publisher : PT. Hasba Edukasi Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71282/jurmie.v2i12.1452

Abstract

Diabetes Mellitus (DM) is a chronic metabolic disease that poses a global health threat, with its prevalence increasing every year. Early detection through the application of Data Mining techniques is crucial to prevent severe complications and to support medical practitioners in making faster clinical decisions. This study aims to compare the performance of two popular machine learning algorithms, namely Support Vector Machine (SVM) and Random Forest, in predicting diabetes risk. Unlike previous studies that often utilize complex feature optimization techniques or oversampling methods (such as SMOTE), this research focuses on evaluating baseline performance to observe each algorithm’s pure capability on the standard Pima Indians Diabetes dataset, which consists of 10,004 medical records with 22 clinical attributes. The experiments were conducted using RapidMiner with a 10-Fold Cross-Validation approach to ensure valid and reliable evaluation results. The findings show that the Random Forest algorithm achieved superior performance with an accuracy of 82.19%, while SVM obtained an accuracy of 79.40%. These results confirm that the ensemble learning approach of Random Forest provides better stability in handling clinical data with high variability compared to single-hyperplane methods such as SVM under default parameters. This study is expected to serve as a foundational benchmark for further development of diabetes prediction models in the future.
Co-Authors Andri Wijaya Ardi Riyadi