ANALISIS KINERJA ALGORITMA NAIVE BAYES DAN SUPPORT VECTOR MACHINE DALAM KLASIFIKASI BERITA OTOMATIS WEB TRUSTED NEWS

HIDAYAT, FIRMAN (2024) ANALISIS KINERJA ALGORITMA NAIVE BAYES DAN SUPPORT VECTOR MACHINE DALAM KLASIFIKASI BERITA OTOMATIS WEB TRUSTED NEWS. S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

This study aims to analyze the Naïve Bayes and Support Vector Machine Algorithms (SVM) algorithm for classifying news on the Trusted News website. Training data is obtained from Trusted News Web. An experimental approach is employed to test the performance of the Naïve Bayes and Support Vector Machine Algorithms algorithm in news categorization systems. Text preprocessing involves tokenization, stemming, lemmatization, and the use of the Term Frequency-Inverse Document Frequency (TF-IDF) method for evaluating relevant words. The test results indicate that Naïve Bayes provides better accuracy compared to SVM, with Naïve Bayes reaching a highest accuracy of 88.5%, while SVM's highest accuracy is 79%. Epoch-wise analysis shows consistent performance for Naïve Bayes, whereas Support Vector Machine Algorithms experiences fluctuations. The conclusion of this research affirms that Naïve Bayes can be an effective choice for classifying news on the Trusted News website, contributing to enhancing information security and public trust. Keywords: Naïve Bayes, Support Vector Machine, SVM, Machine Learning. Penelitian ini bertujuan untuk menganalisis algoritma Naïve Bayes dan Support Vector Machine (SVM) dalam klasifikasi berita pada situs web Trusted News. Data pelatihan diperoleh dari situs web Trusted News. Pendekatan eksperimental digunakan untuk menguji kinerja algoritma Naïve Bayes dan Support Vector Machine dalam sistem kategorisasi berita. Pra-pemrosesan teks melibatkan tokenisasi, stemming, lemmatisasi, dan penggunaan metode Term Frequency-Inverse Document Frequency (TF-IDF) untuk mengevaluasi kata-kata yang relevan. Hasil pengujian menunjukkan bahwa Naïve Bayes memberikan akurasi yang lebih baik dibandingkan dengan SVM, dengan Naïve Bayes mencapai akurasi tertinggi sebesar 88,5%, sedangkan akurasi tertinggi SVM adalah 79%. Analisis per epoch menunjukkan kinerja yang konsisten untuk Naïve Bayes, sementara algoritma Support Vector Machine mengalami fluktuasi. Kesimpulan dari penelitian ini menegaskan bahwa Naïve Bayes dapat menjadi pilihan yang efektif untuk mengklasifikasikan berita pada situs web Trusted News, berkontribusi pada peningkatan keamanan informasi dan kepercayaan masyarakat. Kata Kunci: Naïve Bayes, Support Vector Machine, SVM, Machine Learning.

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