Classification of Remission Data for Prisoners in Tangerang Class IIA Correctional Institution using K-Nearest Neighbor Algorithm - Refa Maulana Abdillah; Halim Agung; Syaipul Ramdhan (Universitas Islam Raden Rahmat, Malang, 2025)
The classification of remission eligibility for prisoners is a critical issue in correctional institutions, as it directly impacts prison management and the rehabilitation process. Special remission is a reduction of sentence granted to prisoners based on specific criteria, including religious status and the type of remission granted. This research aims to address the challenge of classifying special remission data for prisoners at the Class IIA Tangerang Correctional Facility using the K-Nearest Neighbor (KNN) algorithm. The dataset used in this study includes four indicators: Length of Sentence, Remaining Sentence, Crime Type, and Risk Dimension, which are analyzed to predict the remission status to be granted. The KNN model, with a parameter of k=1, achieved an accuracy of 93.94%. However, the model struggled to accurately classify the