PENERAPAN ALGORITMA K-NEAREST NEIGHBOR PADA KLASIFIKASI PETIR NBE

FEBRIAN, RHEVDA and Sidik, Muhammad Abu Bakar (2025) PENERAPAN ALGORITMA K-NEAREST NEIGHBOR PADA KLASIFIKASI PETIR NBE. Undergraduate thesis, Sriwijaya University.

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Abstract

This research aims to classify lightning recording data obtained in the Palembang region in January 2020 using the K-Nearest Neighbor (KNN) algorithm implemented in the Python programming language. The raw data, initially in psdata format, was converted to Comma Separated Values (CSV) format to facilitate analysis. From a total of 12,908 lightning recordings, the classification process successfully identified 550 data points as Negative Narrow Bipolar Events (NNBE) and 2,314 as Positive Narrow Bipolar Events (PNBE), with the remaining 10,044 categorized as Non-NBE. Characteristic analysis revealed that NNBE had an average pulse duration of 32.43 ± 9.54 µs and a rise time of 4.97 ± 4.65 µs. Meanwhile, PNBE exhibited an average pulse duration of 35.1 ± 10.72 µs with a rise time of 5.08 ± 6 µs. Model validation using a confusion matrix showed an overall accuracy of 96%, with a precision of 93% and a recall of 98% for the NNBE class, and a recall of 91% for the PNBE class. These results indicate that the KNN method is effective for NBE lightning classification, despite a slight weakness in distinguishing between the PNBE and Non-NBE classes. This study contributes to the automation of lightning classification using a machine learning approach and provides a foundation for developing more precise extreme weather detection systems.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Klasifikasi Petir, Narrow Bipolar Events (NBE), K-Nearest Neighbor (KNN), Python, Machine Learning, Karakteristik Petir
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK4125-4399 Electric lighting
Divisions:	03-Faculty of Engineering > 20201-Electrical Engineering (S1)
Depositing User:	Rhevda Febrian
Date Deposited:	22 Jul 2025 07:54
Last Modified:	22 Jul 2025 07:54
URI:	http://repository.unsri.ac.id/id/eprint/179672

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