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All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Audi Nuermey Hanafi
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Klasifikasi Body Shaming Berbahasa Indonesia pada Komentar Instagram dengan Pembobotan TF-IDF-C menggunakan Metode Modified K-Nearest Neighbor (MK-NN) Audi Nuermey Hanafi; Muhammad Tanzil Furqon; Marji Marji
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 2 (2021): Februari 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Body shaming is the act of denouncing one's physical appearance or even oneself. Some of the negative effects of body shaming include the victim feeling embarrassed, lack of confidence, and depression. The most terrible impact that can be caused by body shaming is suicide. Body shaming is often found on social media, especially Instagram. The classification system does a body shaming great job of classifying Instagram comments regarding body shaming more efficiently. This classification system body shaming has several stages. The first stage is data collection in the form of Instagram comments. This study collected 230 comments data. The second stage is text processing. Furthermore, the third stage is word weighting using the TF-IDF-C method. After obtaining the weight of each word, then the classification stage uses the method Modified K-Nearest Neighbor (MK-NN). The final result in the form of category classification of test data would enter the category of body shaming or not the body shaming. Based on the test results, the best average accuracy value is 0.530 or 53%. This proves that the TF-IDF-C weighting method and the MK-NN classification method produces the highest level of accuracy when using 60 features and K = 1. However, the level of accuracy is not good in classifying body shaming in Instagram comments.
Co-Authors Marji Marji Muhammad Tanzil Furqon