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All Journal Syntax Literate: Jurnal Ilmiah Indonesia JMM (Jurnal Masyarakat Mandiri) Journal of Soft Computing Exploration Jurnal Informatika: Jurnal Pengembangan IT
Haqimi, Nur Azizul
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Humanities Computer Science & IT Education Environmental Science Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Journal : Jurnal Informatika: Jurnal Pengembangan IT

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Analisis Spam Komentar Instagram menggunakan Support Vector Machine dengan Variasi Hyperparameter Haqimi, Nur Azizul; Roshinta, Trisna Ari
Jurnal Informatika: Jurnal Pengembangan IT Vol 9, No 3 (2024)
Publisher : Politeknik Harapan Bersama

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Abstract

Instagram (IG) is a web and mobile-based social media application where users can share photos or videos with the available features. These features include captions, tagging, adding locations where photos or videos were taken, editing and filtering photos or videos before they are uploaded from the smartphone application and certain tags so that the photos can be seen by many people. Instagram as social media is not only a medium for communication but also for developing brands and selling products. Spam that often appears in spam comments is a barrier to getting appropriate information. When identifying spam and non-spam comments, a challenging problem is that the number of spam comments is less than non-spam comments, thus causing an imbalanced dataset problem. Imbalanced data sets can affect the performance of classification algorithms. Support Vector Machine (SVM) to classify comments between two classes (spam or nonspam) which is the maximum distance between the hyperplane and the closest item from both classes. Analysis of related research that has been carried out with feature variations states that the addition of 90 different features to the data used to increase classification accuracy on imbalanced data.  Other related research discusses Complementary Naïve Bayes which can be used to balance dataset classes. This research describes the selection of Support Vector Machine hyperparameters, especially for unbalanced data where the level of similarity is almost the same, so hyperparameter experiments are needed for the best accuracy
Co-Authors Khoirunisa, Rifa Kusuma, Rendra Tri Masbahah Masbahah, Masbahah Rachman, Yusuf Fadlila Riatma, Darmawan Lahru Roshinta, Trisna Ari Sani, Ahmad Faisal