Agung Suharyanto, Kraugusteeliana Kraugusteeliana, Yuniningsih Yuniningsih, Danu Eko Agustinova, Ari Ramdani, Robbi Rahim
This study examines the application of the k-Nearest Neighbor (k-NN) algorithm for predicting IT project outcomes to support data-driven decision-making in IT governance. The algorithm was applied to a dataset of historical projects from a mid-sized technology company. Despite limitations like sensitivity to parameter tuning, the simplicity and interpretability of k-NN demonstrate its potential as an IT governance decision tool. However, the single case study design restricts generalizability. Further research should explore ensemble approaches to improve robustness, compare k-NN with other methods, and assess its effectiveness across diverse organizational contexts. © 2024, Success Culture Press. All rights reserved.
Universitas Medan Area, Medan, Indonesia; Universitas Pembangunan Nasional Veteran Jakarta, Jakarta, Indonesia; Universitas Pembangunan Nasional "Veteran" Jawa Timur, Surabaya, Indonesia; Universitas Negeri Yogyakarta, Yogyakarta, Indonesia; Sekolah Tinggi Ilmu Administrasi YPPT Tasikmalaya, Tasikmalaya, Indonesia; Sekolah Tinggi Ilmu Manajemen Sukma, Medan, Indonesia
