Penerapan Algoritma K-Nearest Neighbors (K-NN) dan Naïve Bayes untuk Menentukan Pemilihan Penerima Bantuan Sosial Berdasarkan Ekonomi Masyarakat (Jurnal Sistem Informasi dan Ilmu Komputer, 2025)
One approach the government employs to decorate public welfare, mainly among low-income families, is through social help initiatives. however, the subjectivity inside the choice process regularly ends in mistargeting all through implementation. This observe objectives to apply the ok-Nearest Neighbor (ok-NN) and Naive Bayes algorithms inside a decision support device to perceive eligible recipients based on community statistics. The ok-NN algorithm determines similarity by calculating the Euclidean distance among new and current facts, whilst the Naive Bayes set of rules utilizes a probabilistic method based at the likelihood of attribute incidence inside each elegance. Key criteria considered consist of household income, employment kind, number of dependents, housing conditions, and asset possession. Experimental consequences reveal that each algorithms are powerful in as it should be classifying eligibility for help, with k-NN barely outperforming Naive Bayes. therefore, the combination of these algorithms can support stakeholders in making extra goal and efficient selections regarding the distribution of social useful resource.