Implementation of Naive Bayes for Optimizing Asset Condition Classification in a Web-Based Information System
DOI:
https://doi.org/10.33050/686nnx47Kata Kunci:
Naive Bayes, Information Systems, Classification, Data Management, FeasibilityAbstrak
Improving the quality of work performance is an essential aspect for employees at the Office of Investment and Integrated One-Stop Services of Central Sulawesi Province. Many challenges remain in managing asset data, especially because the recording and monitoring processes are still performed manually. This manual approach often leads to inconsistencies, inefficiencies, and difficulties in determining asset eligibility. Therefore, an information system capable of supporting accurate and efficient data management is highly needed. The main objective of this study is to apply the Naive Bayes algorithm to classify asset conditions in a web-based system, enabling faster decision-making and improving the accuracy of asset feasibility assessments within government institutions. The dataset used in this study consists of three key attributes asset functionality, asset age, and physical condition. These attributes serve as indicators for classification using the Naïve Bayes probabilistic approach. The developed web-based application was evaluated through black-box testing to ensure that all system functions performed according to expectations and produced consistent outputs. Black-box testing results show that the system successfully provides correct outputs for each test scenario, verifying that the classification and data management processes operate properly. The application is able to classify assets into feasible or non-feasible categories based on calculated probabilities. Findings indicate that implementing the Naïve Bayes algorithm significantly improves the efficiency of asset data processing and enhances data management quality. The system also supports more objective decision-making regarding asset feasibility. This study demonstrates that probabilistic classification can be effectively integrated into governmental asset management systems to optimize operational performance.
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