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All Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) IKRAM: Jurnal Ilmu Komputer Al Muslim
Feru Adiningrat
STMIK Pranata Indonesia, Bekasi
Computer Science & IT Decision Sciences, Operations Research & Management

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Implementasi Migrasi Sistem ke Azure Cloud: Tantangan dan Solusi di PT New Primagama Abdul Rohim; Feru Adiningrat; Novi Wulandari
IKRAM: Jurnal Ilmu Komputer Al Muslim Vol. 4 No. 1 (2025): IKRAM: Jurnal Ilmu Komputer Al Muslim
Publisher : STMIK Al Muslim

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Abstract

Migrasi sistem ke platform cloud telah menjadi langkah strategis bagi banyak perusahaan untuk meningkatkan efisiensi operasional dan skalabilitas teknologi informasi. Studi ini membahas implementasi migrasi sistem ke Azure Cloud pada PT New Primagama, dengan fokus pada identifikasi tantangan utama yang dihadapi dan solusi yang diterapkan untuk mengatasinya. Proses migrasi mencakup evaluasi kebutuhan bisnis, perencanaan arsitektur cloud, dan implementasi teknologi secara bertahap. Analisis menunjukkan bahwa Azure Cloud tidak hanya memberikan peningkatan dalam kinerja sistem dan keamanan data, tetapi juga menghadirkan fleksibilitas dan pengurangan biaya operasional jangka panjang. Temuan studi ini memberikan wawasan bagi organisasi lain yang mempertimbangkan transisi ke platform cloud untuk mendukung transformasi digital mereka.
Model Prediksi Penyakit Jantung dengan Penanganan Outlier Menggunakan Interquartile Range dan Extreme Gradient Boosting Lukman Azhari; Novi Wulandari; Feru Adiningrat; Allan Desi Alexander
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): January 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6390

Abstract

Heart disease remains one of the leading causes of death worldwide, with increasing prevalence rates, including in Indonesia. Delayed detection and diagnosis are the main challenges in treating this disease, as most cases are only identified after patients experience serious symptoms or heart attacks. Medical data often containing outliers and noise adds to the complexity of developing accurate predictive models. This study aims to develop a heart disease prediction model using a combination of the Interquartile Range (IQR) method for outlier handling and the Extreme Gradient Boosting (XGBoost) algorithm for predictive modeling. The IQR method is applied at the pre-processing stage to identify and eliminate outliers robustly without reducing data integrity, while XGBoost is used to build an efficient prediction model through an ensemble learning approach. The results showed significant improvements in model performance, with accuracy increasing from 75.41% to 89.47% and AUC-ROC from 0.8615 to 0.9450. The model demonstrates balanced predictive capabilities with precision of 95.24% and recall of 80.00% for cases without disease, and precision of 86.11% and recall of 96.88% for cases with disease. The developed model makes significant contributions by improving data quality through robust outlier handling using the IQR method, building a more accurate prediction model by leveraging the advantages of the XGBoost algorithm in the ensemble learning approach.
Co-Authors Abdul Rohim Alexander, Allan D Lukman Azhari Novi Wulandari Novi Wulandari