Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH)
Rachmad Sanuri
Sekolah Tinggi Manajemen Informatika dan Komputer El Rahma Yogyakarta, Yogyakarta
Computer Science & IT Decision Sciences, Operations Research & Management

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Implementasi Algoritma Decision Tree dan Ensemble Learning Berdasarkan Spesifikasiperangkat Keras Untuk Klasifikasi Harga Laptop Lian Galang Prayoga; Rachmad Sanuri
Journal of Information System Research (JOSH) Vol 7 No 4 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i4.10250

Abstract

The complexity of hardware specifications in laptops often makes it difficult for consumers to estimate price suitability in the digital market. This study aims to implement a laptop price classification system by comparing the performance of the baseline Decision Tree algorithm and the Ensemble Learning method (Random Forest) utilizing the Knowledge Discovery in Databases (KDD) approach. Contrary to the initial hypothesis that the ensemble model would provide significant performance improvements, the evaluation results revealed a paradoxical finding. Both classification models produced identically exact performance with an accuracy rate of 73.39% and an F1-Score of 73.41%. Technical analysis indicates that this anomaly is caused by the narrow dimension of deterministic features in the dataset, where RAM capacity and CPU architecture attributes dictate the decision boundaries absolutely, rendering the addition of hundreds of decision trees in the Random Forest computationally redundant. The results of this study contribute theoretical insights regarding the efficiency limitations of ensemble algorithms on low-dimensional datasets, while proving that a single Decision Tree is optimal and computationally efficient enough to be implemented as an inference engine in laptop price recommendation systems.
Co-Authors Lian Galang Prayoga