THE COMPARISON OF CLASSIFICATION ALGORITHMS K-NEAREST NEIGHBORS (KNN) AND SUPPORT VECTOR MACHINE (SVM) TO PREDICT THE UNEMPLOYMENT RATE IN LAMPUNG PROVINCE

Angga Witata, Ganes (2024) THE COMPARISON OF CLASSIFICATION ALGORITHMS K-NEAREST NEIGHBORS (KNN) AND SUPPORT VECTOR MACHINE (SVM) TO PREDICT THE UNEMPLOYMENT RATE IN LAMPUNG PROVINCE. Masters thesis, Institut Informatika dan Bisnis Darmajaya.

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Abstract

By: GANES ANGGA WITATA This research aims to carry out a comparison between two classification algorithms, namely K-Nearest Neighbors (KNN) and Support Vector Machine (SVM), in the context of predicting unemployment rates in Lampung Province. Unemployment is a significant socio-economic issue and requires a comprehensive and effective approach for understanding and predicting these patterns. This research aims to provide in-depth insight into the relative performance of both algorithms in overcoming the challenges of unemployment prediction. The experimental methodology involved training and testing KNN models and SVM using historical data to measure the prediction accuracy and general capabilities of the model. The results of this comparison were expected to provide a better view of the advantages and disadvantages of each algorithm, as well as providing recommendations regarding optimal implementation in the context of unemployment prediction. Through this research contribution, it is hoped that it can provide a strong foundation to select the most appropriate algorithm to support government efforts and other stakeholders in managing and reducing unemployment levels in Lampung Province. Keywords: Unemployment, Classification, KNN, SVM, Lampung

Item Type: Thesis (Masters)
Subjects: Ilmu Komputer
eTheses
Divisions: Skripsi/TA & PKPM/KP - Fakultas Ilmu Komputer > Teknik Informatika
Depositing User: Ganes Angga Witata
Date Deposited: 06 Jun 2024 01:40
Last Modified: 06 Jun 2024 01:40
URI: http://repo.darmajaya.ac.id/id/eprint/16345

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