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All Journal Pascal: Journal of Computer Science and Informatics
Lubis, Erliani Syahputri
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Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Library & Information Science Social Sciences

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Journal : Pascal: Journal of Computer Science and Informatics

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Application of Natural Language Processing Based on Machine Learning and IoT Data Pratiwi, Adellia; Lubis, Erliani Syahputri; Rangkuti, Fiqri Hidayat; Suyudi, M. Karim; Jefry, Togap Aland
Pascal: Journal of Computer Science and Informatics Vol. 3 No. 01 (2025): Pascal: Journal of Computer Science and Informatics
Publisher : Devitara Innovations

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Abstract

The development of the Internet of Things (IoT) and Natural Language Processing (NLP) has opened new opportunities to build intelligent monitoring systems capable of processing multiformat data simultaneously. This study aims to apply machine learning–based NLP methods to analyze IoT data in order to improve the accuracy of real-time environmental condition detection. The dataset used consists of temperature and humidity parameters collected from IoT sensors, as well as textual data in the form of environmental condition reports. The textual data are processed through tokenization, lowercasing, stopword removal, stemming, and lemmatization, followed by feature extraction using Term Frequency–Inverse Document Frequency (TF-IDF). The Naive Bayes algorithm is employed to classify conditions into Normal, Warning, and Critical based on a combination of sensor data and textual features. The experimental results show that integrating NLP with IoT data increases classification accuracy from 82% (using sensor data alone) to 91% and enables automatic, real-time condition detection. This study demonstrates that multiformat data integration through NLP and machine learning can enhance the effectiveness of intelligent monitoring systems and can be implemented in environmental, industrial, healthcare, and security domains, thereby making a significant contribution to data-driven decision-making.
Co-Authors Jefry, Togap Aland Pratiwi, Adellia Rangkuti, Fiqri Hidayat Suyudi, M. Karim