KLASIFIKASI JUDUL BERITA BAHASA INDONESIA MENGGUNAKAN METODE SELEKSI FITUR CHI-SQUARE DAN ALGORITMA SUPPORT VECTOR MACHINE (SVM).

AQILLAH, NAZIFAH SUCI and Utami, Alvi Syahrini and Rachmatullah, Muhammad Naufal (2024) KLASIFIKASI JUDUL BERITA BAHASA INDONESIA MENGGUNAKAN METODE SELEKSI FITUR CHI-SQUARE DAN ALGORITMA SUPPORT VECTOR MACHINE (SVM). Undergraduate thesis, Sriwijaya University.

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Abstract

News is an important source of information in society, and classification of news headlines is a challenge for organizing this information. This research aims to develop software that is able to classify Indonesian news headlines into 3 categories using the Support Vector Machine method with Chi-Square Feature Selection. The SVM method is known as an effective classification algorithm, while chi-square feature selection is used to select important words. in news headlines that differentiate between categories. These selected words are then represented using TF-IDF weighting and used as features to train the SVM model. The data used is a collection of Indonesian language news titles in the categories EDU, FINANCE, SPORT. The research stages include text pre-processing, feature extraction, TFIDF weighting, SVM model training, and classification performance evaluation. The results of classification research using the value C=10 show that by applying a combination of chi-square feature selection with the SVM algorithm, the level of classification accuracy of Indonesian news titles decreases by around 3% compared to without feature selection. This research shows that chi-square feature selection with a linear kernel combined with the SVM algorithm is less effective for classification results. Keywords: Support Vector Machine, Chi-Square, classification accuracy, News Title

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Support Vector Machine, Chi-Square, akurasi klasifikasi, Judul Berita
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Nazifah Suci Aqillah
Date Deposited:	17 May 2024 03:46
Last Modified:	17 May 2024 03:46
URI:	http://repository.unsri.ac.id/id/eprint/144210

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