A Decision Guidance for Solving Success Rate Political Campaign Using Distance Weighted kNN in Nassi-Shneiderman Framework

Abstract

Campaigns on online social media are becoming intriguing because all parties can compete with each other. Various content can be created to support or defeat one another. Interaction is important to control the battlefield in the face of disinformation. The campaign team must be able to increase interaction and reporting it as a form of achievement. The reality is that the campaign team has not been able to provide a measurable report on the success rate of political campaign interaction on social media. This is because there is no standardization in measuring these interactions. This fact inspires to build a model for measuring campaign interaction on social media. This model will be able to help the campaign team. This study uses a dataset from a governor election campaign in Indonesia which is then tested on a presidential election campaign dataset in America. The model was built using machine learning with the k classification method Nearest Neighbor (kNN) and Distance Weight kNN (DWkNN). The research stages were arranged using the Nassi-Shneiderman Framework. This framework describes the stages and comparison of the use of the KNN and the DWkNN from the training to testing stages in an easy to understand manner. The kNN and DWkNN training stages showed excellent accuracy results of nearly 100%. Furthermore, at the testing stage using the crossvalidation method with a variety of fold 5 to 20 variations showed excellent results, namely 99.89% on the governor election dataset and a range of 98% on the American presidential election dataset. This classification model has been tested using several datasets from several candidate social media accounts. We also created a dataset campaign interaction. This study shows that the proposed model can outperform previous studies. So, that it provides novelty in the form of a campaign interaction guide model for the campaign team.

Prediksi Ketepatan Waktu Studi Mahasiswa Bidik Misi Menggunakan K-Nearest Neighbour

“…Penelitian sebelumya pernah dilakukan oleh Aryo Nugroho pada tahun 2021 dengan judul A Decision Guidance for Solving Success Rate Political Campaign Using Distance Weighted kNN in Nassi-Shneiderman Framework [8]. Data diolah dengan pemodelan yang dibangun menggunakan machine learning dengan metode klasifikasi k Nearest Neighbor (kNN) dan Distance Weighted kNN (kNN).…”

Pebandingan Performa Naïve Bayes dan KNN pada Klasifikasi Teks Sentimen Jasa Ekspedisi

“…Sentimen akan diproses untuk menghasilkan informasi atau pola yang dapat diubah sebagai ilmu pengetahuan baru [2]. Tujuan dari pengklasifikasian sentimen adalah untuk menemukan kecendurangan pola opini dari ulasan pengguna untuk menganalisa dan menemukan masalah [3]. Analisis sentimen adalah bidang studi yang digunakan untuk menganalisa pendapat yang dicurahkan seseorang dengan tersurat terkait topik masalah tertentu [4].…”