IDENTIFIKASI PELECEHAN SEKSUAL PADA INSTAGRAM DENGAN METODE SUPPORT VECTOR MACHINE

AKBAR, OTTO TIRTAYASA and Utami, Alvi Syahrini and Rizqie, M. Qurhanul (2022) IDENTIFIKASI PELECEHAN SEKSUAL PADA INSTAGRAM DENGAN METODE SUPPORT VECTOR MACHINE. Undergraduate thesis, Sriwijaya University.

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Abstract

The existence of social media makes many people upload their experiences, either in the form of writing or multimedia. The reactions from these uploads also vary, some respond well in the form of praise, suggestions and positive comments, some respond badly such as giving reproaches, insults and even harassing sentences. This study aims to identify whether a sentence on Instagram includes sexual harassment or not using the Support Vector Machine (SVM) classification method. The SVM method was chosen because this method is often used for classification and has good performance results. In its implementation, the data is pre-processed to make the data cleaner and more structured, then feature extraction is carried out using Term Frequency-Inverse Document Frequency (TF-IDF) and classification is carried out using the SVM method with a linear kernel. Testing this research using the K-Fold Cross Validation method, the data, in total 200 Instagram comments containing 100 harassment comments and 100 non-harassment comments, is divided into 90% training data and 10% test data with a K value of 10. After 10 tests, the SVM classification method with linear kernel produces an accuracy value of 71%, a precision value of 93%, a recall value of 65% and an f-1 value of 76% with an average computation time of 0.035 seconds.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Pelecehan seksual, Instagram, Media Sosial, Support Vector Machine (SVM)
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation. T Technology > T Technology (General) > T175-178 Industrial research. Research and development > T175 General works
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Otto Tirtayasa Akbar
Date Deposited:	27 Jan 2023 05:17
Last Modified:	27 Jan 2023 05:17
URI:	http://repository.unsri.ac.id/id/eprint/88069

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