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All Journal JUITA : Jurnal Informatika
Putu Sumardika Eka Putra
Institut Teknologi dan Bisnis STIKOM Bali
Computer Science & IT

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Voting Scheme Nearest Neighbors by Difference Distance Metrics Measurement Gede Angga Pradipta; Made Liandana; Putu Desiana Wulaning Ayu; Dandy Pramana Hostiadi; Putu Sumardika Eka Putra
JUITA : Jurnal Informatika JUITA Vol. 11 No. 2, November 2023
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v11i2.19298

Abstract

K-Nearest Neighbor (KNN) is a widely used method for both classification and regression cases. This algorithm, known for its simplicity and effectiveness, relies primarily on the Euclidean formula for distance metrics. Therefore, this study aimed to develop a voting model where observations were made using different distance calculation formulas. The nearest neighbors algorithm was divided based on differences in distance measurements, with each resulting model contributing a vote to determine the final class. Consequently, three methods were proposed, namely k-nearest neighbors (KNN), Local Mean-based KNN, and Distance-Weighted neighbor (DWKNN), with an inclusion of a voting scheme. The robustness of these models was tested using umbilical cord data characterized by imbalance and small dataset size. The results showed that the proposed voting model for nearest neighbors consistently improved performance by an average of 1-2% across accuracy, precision, recall, and F1 score when compared to the conventional non-voting method.
Co-Authors Dandy Pramana Hostiadi Gede Angga Pradipta Made Liandana, Made Putu Desiana Wulaning Ayu