Pengaplikasian Data Mining Penentuan Penerimaan Bantuan Sosial Masyarakat Dengan Prosedur Algoritma Naive Bayes

Social assistance is an important aspect of government and non-government programs that can help on a large scale for the community so that the impact is to lighten life in the short term, but social assistance has several criteria such as income, social conditions, family status and the impact of the economic situation. . Knowing the criteria for social assistance is done by applying data mining to social assistance using the Naive Bayes algorithm procedure which produces accuracy calculations from 100 testing data, obtained good values, namely accuracy of 95.00%, precision of 92.31%, and recall of 97.95%.

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Publication Details

Journal
Merkurius Jurnal Riset Sistem Informasi dan Teknik Informatika
Published
2024-10-02
DOI
https://doi.org/10.61132/merkurius.v2i6.392
Primary Topic
Data Mining and Machine Learning Applications
Type
article
Field-Weighted Citation Impact
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article

Pengaplikasian Data Mining Penentuan Penerimaan Bantuan Sosial Masyarakat Dengan Prosedur Algoritma Naive Bayes

Nurfalah Nurfalah, Rouli Doharma
Merkurius Jurnal Riset Sistem Informasi dan Teknik Informatika
Data Mining and Machine Learning Applications
article

Pengaplikasian Data Mining Penentuan Penerimaan Bantuan Sosial Masyarakat Dengan Prosedur Algoritma Naive Bayes

Nurfalah Nurfalah, Rouli Doharma
article en

Abstract

Social assistance is an important aspect of government and non-government programs that can help on a large scale for the community so that the impact is to lighten life in the short term, but social assistance has several criteria such as income, social conditions, family status and the impact of the economic situation. . Knowing the criteria for social assistance is done by applying data mining to social assistance using the Naive Bayes algorithm procedure which produces accuracy calculations from 100 testing data, obtained good values, namely accuracy of 95.00%, precision of 92.31%, and recall of 97.95%.

Merkurius Jurnal Riset Sistem Informasi dan Teknik InformatikaVol. 2(6)
Gunadarma University (ID)
Reduced inequalities
Openalex Percentile: Top 72%
Data Mining and Machine Learning Applications
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