Analysis of the feasibility level of IT device using K-Means cluster and C4.5 classification: English

Fachriqi Naldes; S. Sumijan; Syafri Arlis

doi:10.35134/komtekinfo.v13i1.673

Authors

Fachriqi Naldes Universitas Putra Indonesia "YPTK" Padang
S. Sumijan Universitas Putra Indonesia YPTK https://orcid.org/0000-0002-9932-4325
Syafri Arlis Universitas Putra Indonesia YPTK https://orcid.org/0000-0003-3279-4637

DOI:

https://doi.org/10.35134/komtekinfo.v13i1.673

Keywords:

feasibility level, IT Device, K-Means Cluster, C4.5 classification

Abstract

The availability of reliable laptops is essential for ensuring smooth business operations; however, decisions regarding device upgrades and replacements in many organizations still rely primarily on device age and subjective user perceptions. This practice often leads to inconsistent IT asset lifecycle decisions, increased security risks, and inefficient cost management. This study proposes a classification model to recommend laptop feasibility levels, namely usable, requires upgrade, and requires replacement, based on a combination of technical specifications and operating system characteristics. K-Means clustering is applied to group laptops into three feasibility categories using processor type, release year, RAM capacity, storage type, and operating system attributes that have undergone performance score–based ordinal encoding and Min–Max normalization. Subsequently, the C4.5 algorithm is employed to construct a decision tree using the K-Means cluster labels as target classes, producing interpretable if–then rules that describe device feasibility patterns. The dataset is obtained from the IT device inventory of PT Semen Indonesia, consisting of 1,905 laptop records, which after data cleaning result in 85 unique specification combinations for analysis. The clustering process classifies 47 laptops as usable, 22 as requiring upgrades, and 16 as requiring replacement. The C4.5 algorithm model achieves accuracy, precision, recall, and F1-score values of 100% on the test data, indicating its ability to effectively replicate the feasibility patterns generated by K-Means algorithm. These findings demonstrate that the proposed approach provides a data-driven framework for supporting upgrade and replacement decisions, contributing to more efficient and measurable IT asset lifecycle management.

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