Tracking and Validation System of Proposal Document in Manado State Polytechnic Based on Machine Learning with Random Forest Algorithm

DOI

10.2991/978-94-6463-926-1_73How to use a DOI?

Keywords

AI; Machine Learning; Proposal Validation; Random Forest

Abstract

This system is an innovative solution to overcome problems in the process of submitting proposals for procurement of activities. To prevent and reduce errors that often occur in proposal writing, such as inappropriate formatting, insufficient document completeness, and non-compliance with administrative standards, this research developed a Machine Learning-based system using the Random Forest algorithm. This system is capable of automatically validating proposal formats and providing real-time feedback to users. Data was collected from proposal datasets, data processing, feature extraction, model training, and system testing. The dataset contains approved and rejected proposals in recent years. The Random Forest model was chosen because it is able to handle many features and has high accuracy. This system will analyze proposals based on completeness of documents, structure, and suitability of content to established standards. The result of this research is a web-based system that is capable of automatic validation of proposals uploaded by users. This system can detect whether a proposal complies with the specified format or still requires revision before being submitted. If the proposal does not meet the predetermined standards, the system will provide automatic feedback regarding the parts that need improvement, so that users can immediately make revisions without having to wait for manual evaluation. This system is expected to speed up proposal validation at the Manado State Polytechnic, reduce the burden on verifiers, and increase administrative efficiency. Apart from that, this system can be a reference for other educational institutions in developing AI-based document validation.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Selvy Kalele
AU  - Alfrets Wauran
AU  - Franky Manppo
AU  - Susy Marentek
PY  - 2025
DA  - 2025/12/31
TI  - Tracking and Validation System of Proposal Document in Manado State Polytechnic Based on Machine Learning with Random Forest Algorithm
BT  - Proceedings of the International Conference on Applied Science and Technology on Engineering Science 2025 (iCAST-ES 2025)
PB  - Atlantis Press
SP  - 649
EP  - 657
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-926-1_73
DO  - 10.2991/978-94-6463-926-1_73
ID  - Kalele2025
ER  -