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All Journal Jurnal Teknik Jurnal Informatika dan Teknik Elektro Terapan JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) Among : Jurnal Pengabdian Masyarakat Journal of Information System and Technology (JOINT) Jutech: Jurnal Teknologi Informasi JUSTIFY : Jurnal Sistem Informasi Ibrahimy Jurnal Aplikasi Sistem Informasi dan Elektronika Kohesi: Jurnal Sains dan Teknologi International Applied Science Journal Of Humanities Community Empowerment Jurnal Penelitian Teknologi Informasi dan Sains ABDIMASTEK
Fitriyah, Nur Qodariyah
Universitas Muhammadiyah Jember
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Perbandingan Algoritma K-Nearest Neighbors dan Naïve Bayes dalam Penentuan Penerima Bantuan di Desa Banyuputih Kidul Thoriq Wahyu Hidayatullah; Ulya Anisatur Rosyidah; Nur Qodariyah Fitriyah
Jurnal Penelitian Teknologi Informasi dan Sains Vol. 4 No. 1 (2026): : JURNAL PENELITIAN TEKNOLOGI INFORMASI DAN SAINS (JPTIS)
Publisher : Institut Teknologi dan Bisnis (ITB) Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54066/jptis.v4i1.3733

Abstract

The distribution of social assistance represents a key government strategy to enhance the welfare of low-income communities. Nevertheless, its implementation frequently faces challenges related to inaccurate targeting, often caused by uneven data collection and subjective decision-making processes in identifying eligible beneficiaries. This study aims to compare the performance of the K-Nearest Neighbors (KNN) and Naïve Bayes algorithms in determining eligibility for social assistance recipients in Banyuputih Kidul Village. Both models were evaluated using a confusion matrix with performance indicators including accuracy, precision, recall, and F1-score. The findings reveal that the KNN algorithm outperformed Naïve Bayes in identifying recipients of the PKH social assistance program, achieving an evaluation score of 99%, compared to 86% for Naïve Bayes. These results indicate that KNN provides higher predictive reliability for eligibility classification. This research is expected to support the development of an objective, data-driven decision support system that can assist village governments in distributing social assistance more accurately and transparently.
Co-Authors Abdul Azis agus milu susetyo Ari Eko Wardoyo Daryanto Dinata, Noeril Agian Septa Erwanto , Tedi Fitriyah, Aurelia Agustin Fitriyah, Bait Syaiful Rijal Fitriyah, Cetta Ugama Putra Ginanjar Abdurrahman Hasbullah Hasbullah Lintang Setyo Kurniawati Mochammad Zainur Rahman A. Mohammad Hasan Oktavianto, Hardian Putra, Dua Rizki Dharma Rahman, Miftahur Rintyarna , Bagus Setya Sulistyo, Henny Wahyu Sutikno Sutikno Sutikno Sutikno Thoriq Wahyu Hidayatullah Ulya Anisatur R. Ulya Anisatur Rosyidah Widiyanto, Alfian Wijaya, Guruh Yusril Izzi Arlisa Amiri Zaenab Kurnia Zainul Arifin Zakiyyah, Amalina Maryam