Data Mining Prediksi Jumlah Kematian Berjenis Kelamin Perempuan Berdasarkan Usia Dengan Menggunakan Metode Regresi Linear Sederhana

This study aims to analyze the relationship between the number of female deaths and age. It uses mortality data collected from relevant agencies and applies simple linear regression to model the relationship between these two variables. The results show a positive association: as age increases, the risk of death among females also rises. The derived simple linear regression model can be used to predict the number of female deaths based on age. This research contributes to understanding age‑related patterns of female mortality and can inform policymakers in designing health programs and policies targeted at the most at‑risk female age groups. However, the study is limited by its use of data from a single year and one region; future research with a broader dataset and more sophisticated analytical methods is needed to achieve a more comprehensive understanding of factors influencing female mortality by age.

Authors

Publication Details

Journal
Jurnal Riset Informatika dan Teknologi Informasi
Published
2024-08-30
DOI
https://doi.org/10.58776/jriti.v2i1.132
Primary Topic
Data Mining and Machine Learning Applications
Type
article
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article

Data Mining Prediksi Jumlah Kematian Berjenis Kelamin Perempuan Berdasarkan Usia Dengan Menggunakan Metode Regresi Linear Sederhana

Syahrizal Raksa negara
Jurnal Riset Informatika dan Teknologi Informasi
Data Mining and Machine Learning Applications
article

Data Mining Prediksi Jumlah Kematian Berjenis Kelamin Perempuan Berdasarkan Usia Dengan Menggunakan Metode Regresi Linear Sederhana

Syahrizal Raksa negara
article en

Abstract

This study aims to analyze the relationship between the number of female deaths and age. It uses mortality data collected from relevant agencies and applies simple linear regression to model the relationship between these two variables. The results show a positive association: as age increases, the risk of death among females also rises. The derived simple linear regression model can be used to predict the number of female deaths based on age. This research contributes to understanding age‑related patterns of female mortality and can inform policymakers in designing health programs and policies targeted at the most at‑risk female age groups. However, the study is limited by its use of data from a single year and one region; future research with a broader dataset and more sophisticated analytical methods is needed to achieve a more comprehensive understanding of factors influencing female mortality by age.

Jurnal Riset Informatika dan Teknologi InformasiVol. 2(1)
Openalex Percentile: Top 50%
Data Mining and Machine Learning Applications
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