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All Journal SITEKIN: Jurnal Sains, Teknologi dan Industri Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi Jurnal Sains dan Informatika Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Sistem Komputer dan Informatika (JSON)
Fitri Wulandari
Fakultas Sains Dan Teknologi Universitas Islam Negeri (UIN) Sultan Syarif Kasim Riau
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Neuroscience Transportation Other

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Penerapan Algoritma Naïve Bayes Terhadap Klasifikasi Penerima Bantuan Program Keluarga Harapan (PKH) Amelia Irsyada; Elin Haerani; Muhammad Irsyad; Fitri Wulandari; Liza Afriyanti
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 2 (2023): Desember 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i2.7203

Abstract

Poverty in Indonesia is one of the complex social issues. As a manifestation of the government's concern about poverty in the country, various assistance programs have been established to target the impoverished population. One such program aimed at alleviating poverty in Indonesia is the Family Hope Program (Program Keluarga Harapan or PKH). PKH is a conditional cash transfer program provided to the impoverished community. The manual selection process for aid recipients is considered less than ideal, leading to issues of improper distribution. In this study, the Naïve Bayes algorithm is applied to classify PKH aid recipients in the Bungaraya Subdistrict, Siak Regency, as part of the government's efforts to tackle poverty. The dataset used consists of 560 records, including data on existing PKH aid recipients and potential recipients from various villages in the Bungaraya Subdistrict for the year 2022. The attributes considered in this research include age, income, number of dependents, dependents attending school, dependents with disabilities, housing status, floor type, and wall type. The highest accuracy obtained through calculations on Google Colab is 99% for an 80:20 ratio, while the accuracy obtained using RapidMiner is 94%.
Co-Authors Alwis Nazir Amany Akhyar Amelia Irsyada Dimas Rifqi Ekaryanto Elin Haerani Elin Haerani Elin Haerani Elin Haerani Elin Haerani Farhan Adib Surya Febi Nur Salisah Fitri Insani Gusti, Siska Kurnia Liza Afriyanti Muhammad Affandes Muhammad Irsyad Muhammad Ridho Amrullah Nurul Ikhsan Reski Mai Candra Rice Novita Rinto Setiawan Siti Ramadhani Siti Ramadhani Siti Ramadhani Surya Andika Yusra