Klasifikasi berita hoaks dengan metode svm dan logistic regression

Hanitio, Richard (2023) Klasifikasi berita hoaks dengan metode svm dan logistic regression. Bachelor thesis, Universitas Pelita Harapan.

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Abstract

Media digital telah menjadi wadah bagi masyarakat untuk memperoleh informasi secara cepat pada zaman digital seperti sekarang ini. Melalui media digital, berita hoaks dapat dengan cepat dan mudah menyebar ke masyarakat yang menyebabkan keresahan, konflik dan kerugian pada masyarakat. Oleh karena itu, diperlukan upaya untuk mendeteksi dan mengklasifikasi berita hoaks dengan akurat. Penelitian ini bertujuan untuk membuat suatu model klasifikasi menggunakan metode SVM dan Logistic Regression. Dataset yang digunakan dalam penelitian ini terdiri dari 5882 berita, dengan 2893 berita hoaks dan 3039 berita faktual. Dataset melalui tahapan preprocessing, transformasi TF-IDF, melatih model Logistic Regression untuk mencari probabilitas klasifikasi awal, dan melatih model SVM kernel Linear untuk mencari klasifikasi akhirnya. Model tersebut kemudian dicoba pada data uji. Hasilnya adalah model tersebut memiliki akurasi sebesar 96.4%, presisi sebesar 96.7%, sensitivitas sebesar 95.7%, dan F1-score sebesar 96.2%. Hasil ini menunjukkan bahwa metode SVM dan Logistic Regression efektif dalam mengklasifikasikan berita hoaks. / Digital media has become a platform for people to obtain information quickly in this digital age. Through digital media, fake news can be quickly and easily spread to the public, causing anxiety, conflict and harm to the public. Therefore, there is a need to detect and classify hoax news accurately. This research aims to create a classification model using SVM and Logistic Regression methods. The dataset used in this research consists of 5882 news, with 2893 fake news and 3039 real news. The dataset goes through preprocessing, TF-IDF transformation, training the Logistic Regression model to find the initial classification probability, and training the linear kernel SVM model to find the final classification. The model was then tested on the tested data. The results showed that the model has an accuracy of 96.4%, a precision of 96.7%, a sensitivity of 95.7%, and an F1-score range of 96.2%. These results show that the SVM and Logistic Regression methods are effective in classifying fake news.

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