Implementasi Algoritma Naive Bayes Classifier Dalam Menentukan Tingkat Kecanduan Terhadap Penggunaan Instagram Dan Tiktok (Studi Kasus: It Telkom Purwokerto)

Indri, Monica Cristiani Silalahi (2022) Implementasi Algoritma Naive Bayes Classifier Dalam Menentukan Tingkat Kecanduan Terhadap Penggunaan Instagram Dan Tiktok (Studi Kasus: It Telkom Purwokerto). Undergraduate Thesis thesis, Institut Teknologi Telkom Purwokerto.

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Abstract

Social media can be accessed by various groups of people, one of which is students. Social media can have both positive and negative impacts. The high intensity of social media use will have an addictive effect due to the pleasure and available social media facilities. Therefore, there is a need for an analysis that can determine the level of student addiction in the use of social media, one of which is the IT Telkom Purwokerto student. The algorithm used in this research is the Naive Bayes Classifier algorithm. This research was made to determine the level of addiction to social media instagram and tiktok based on the criteria of low addiction level, moderate addiction level, and severe addiction level. The dataset in this research amounted to 100 data. Naive Bayes classifier testing is done using a multi class confusion matrix. From the test results it was found that the naive bayes classifier algorithm on the instagram dataset has an accuracy of 83% and tiktok is 90% f-1 score for instagram is 80% and tiktok is 86%. In the nave Bayes classifier analysis of 100 students, it was found that on the use of Instagram there were 58 students with mild addiction levels, 30 students with moderate addiction levels and 12 students with severe addiction levels. In the use of tiktok there were 52 students with mild addiction levels and 30 with moderate addiction levels and 18 with severe addiction levels. Keywords: Social Media, Social Media Addiction, Students, Naive Bayes Classifier, Multi Class Confusion Matrix

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