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All Journal Science in Information Technology Letters Bulletin of Social Informatics Theory and Application
Simbolon, Triyanti
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Computer Science & IT Social Sciences

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Misuse of Facebook user data Hernández , Leonel; Juniarti Fatimah, Tri; Fautngilyanan, Petrus Jack; Hamid, Sayed Rahman; Pramudhita, Setya Rahadi; Simbolon, Triyanti
Bulletin of Social Informatics Theory and Application Vol. 5 No. 1 (2021)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v5i1.411

Abstract

Nowadays, we live when technological developments have developed rapidly, especially in the communications and information sector. One of the triggers is social media, including Facebook. In 2010, there were around 24 million Facebook users in Indonesia. In building social relationships, of course, personal information is needed from someone. The same is the case when using social media. When you want to create a Facebook account, users will be asked to provide various personal data. This functions so that users can recognize one another. Besides that, it can also help Facebook in securing user accounts. But by sharing personal data will undoubtedly pose a risk of being misused. Apart from data leaks due to Facebook's shortcomings, this is also inseparable from users' negligence in safeguarding their data. This research explores the unawareness of Facebook users in protecting their data. This research will show the consequences of a lack of attention in maintaining personal data and show how to protect personal data to increase the awareness of Facebook users in safeguarding their data.
Text classification of traditional and national songs using naïve bayes algorithm Simbolon, Triyanti; Wibawa, Aji Prasetya; Zaeni, Ilham Ari Elbaith; Ismail, Amelia Ritahani
Science in Information Technology Letters Vol 3, No 2 (2022): November 2022
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v3i2.1215

Abstract

In this research, we investigate the effectiveness of the multinomial Naïve Bayes algorithm in the context of text classification, with a particular focus on distinguishing between folk songs and national songs. The rationale for choosing the Naïve Bayes method lies in its unique ability to evaluate word frequencies not only within individual documents but across the entire dataset, leading to significant improvements in accuracy and stability. Our dataset includes 480 folk songs and 90 national songs, categorized into six distinct scenarios, encompassing two, four, and 31 labels, with and without the application of Synthetic Minority Over-sampling Technique (SMOTE). The research journey involves several essential stages, beginning with pre-processing tasks such as case folding, punctuation removal, tokenization, and TF-IDF transformation. Subsequently, the text classification is executed using the multinomial Naïve Bayes algorithm, followed by rigorous testing through k-fold cross-validation and SMOTE resampling techniques. Notably, our findings reveal that the most favorable scenario unfolds when SMOTE is applied to two labels, resulting in a remarkable accuracy rate of 93.75%. These findings underscore the prowess of the multinomial Naïve Bayes algorithm in effectively classifying small data label categories.
Co-Authors Aji Prasetya Wibawa Fautngilyanan, Petrus Jack Hamid, Sayed Rahman Hernández , Leonel Ismail, Amelia Ritahani Juniarti Fatimah, Tri Pramudhita, Setya Rahadi Zaeni, Ilham Ari Elbaith