Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal TIN: TERAPAN INFORMATIKA NUSANTARA Bulletin of Computer Science Research
Denny Ganjar Purnama
Universitas Pembangunan Jaya, Tangerang Selatan
Aerospace Engineering Arts Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Implementasi Business Intelligence Untuk Analisis Data Tingkat Kerawanan Kebakaran Berbasis Wilayah Javier Alvino Alfian; Denny Ganjar Purnama
Bulletin of Computer Science Research Vol. 6 No. 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i3.1060

Abstract

This study aims to implement a Business Intelligence (BI) approach to analyze fire risk levels based on regional characteristics using open government data from Satu Data Jakarta. The dataset consists of 5,471 records for the period 2024–2025, including hazard, vulnerability, and capacity indicators at the neighborhood (RW) level. The methodology involves Extract, Transform, Load (ETL) using Python, data warehouse design with a star schema in PostgreSQL, OLAP-based analysis using SQL queries, and visualization through a web-based dashboard. The results indicate a significant increase in the proportion of high-risk areas from 16.62% in 2024 to 33.42% in 2025. However, this increase does not fully reflect actual changes in field conditions and may also be influenced by data distribution and the underlying risk classification system. Furthermore, the analysis reveals that fire risk is not evenly distributed but concentrated in specific regions, particularly in several districts of East Jakarta and South Jakarta, highlighting the importance of spatial-based approaches in determining mitigation priorities. This study utilizes pre-defined risk categories provided by the data source without performing predictive modeling or reclassification. The BI implementation not only integrates disparate data but also uncovers distribution patterns, risk trends, and regional priorities more systematically compared to conventional descriptive analysis. The findings contribute to supporting data-driven decision-making, especially in identifying priority areas for fire risk mitigation.
Analisis Prediktif Faktor Kematian Balita menggunakan Logistic Regression, Random Forest, dan XGBoost Aqila Kharismawardani; Denny Ganjar Purnama
TIN: Terapan Informatika Nusantara Vol 6 No 6 (2025): November 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i6.8594

Abstract

The Under-Five Mortality Rate (UFMR) is a crucial issue in Indonesia that requires data-driven interventions. This study aims to develop a predictive model to identify the most influential risk factors for under-five mortality in Bandung City and to compare the performance of three machine learning algorithms. This research utilizes secondary data from the Bandung City Open Data portal for the period 2019-2021. The method employed is a comparative analysis of Logistic Regression, Random Forest, and XGBoost. To address the significant class imbalance in the data, the Synthetic Minority Over-sampling Technique (SMOTE) was applied to the training data. The evaluation results show that all three models achieve high accuracy, however, performance on the minority calss (mortality cases) remains challenging, indicated by low F1-scores (0.12 for Random Forest and 0.17 for XGBoost). Nonetheless, the feature importance analysis from the Random Forest model successfully identified 'other causes' (penyebab_LAIN-LAIN), 'fever' (penyebab_DEMAM), and the availability of healthcare professionals (PERAWAT, BIDAN) as the most significant predictors. This study highlights the insight from feature importance in identifying risk factors in imbalanced medical data, providing a basis for more targeted health policy recommendations.
Co-Authors Aqila Kharismawardani Javier Alvino Alfian