Implementasi KNN dan Decision Tree Untuk Klasifikasi Kelayakan Bantuan Disabilitas DKI Jakarta - Nathan Ramadhani; Dwi Wahyu Maulana; Muhammad Rizky Fahreza (CICES (Cyberpreneurship Innovative and Creative Exact and Social Science), 2024)
The purpose of this study is to identify whether recipients of disability assistance are eligible to receive aid based on the Governor of DKI Jakarta Regulation Number 44 of 2022 concerning the Provision of Social Assistance for Protection. This study uses a qualitative approach with Python and takes a real case study using a dataset from the DKI Jakarta Social Service in 2020. The study involves two machine learning algorithms: Decision Tree to classify eligibility and K-Nearest Neighbors (KNN) to visualize the recipients of assistance who fall into the eligible and non-eligible groups. The large number of recorded disabled individuals has created challenges for the DKI Jakarta Provincial Social Service in identifying them. The high population, along with limited resources and funds, presents a significant challenge for the Social Service to ensure that assistance is distributed accurately and effectively. One of the efforts that the DKI Jakarta Provincial Social Service can undertake is to issue the Jakarta Disability Card (KPDJ). The results of this study show that the classification with the Decision Tree algorithm achieved an accuracy score of 0.78, indicating that the developed system is capable of providing recommendations with a high level of accuracy in determining aid recipients, thereby improving efficiency in the decision-making process related to aid distribution.