Abstract
Suicide is currently a serious problem in higher education, especially among university students, and special approaches and attention are required to prevent it. With today's advances in technology, emotion analysis techniques can be an effective way to understand students' feelings and thoughts that may lead to suicidal behavior or indicate a risk of suicide. For this study, we scraped the data for his 1,151 tweets on Twitter and cleaned it up to 817. Of these, there are 745 negative tweets and 72 positive tweets. Additionally, the data is implemented in an algorithm that performs a data split of 80:20 with an accuracy of 90,24%. That's the "depression" that often appears when visualizing Lata data. Especially in Indonesia, there are many suicides due to depression. The purpose of this study is to understand the factors associated with student suicide and to determine the effectiveness and accuracy of this algorithm. Additionally, this study is expected to provide insights into educational and mental health settings to improve prevention strategies and more effective approaches
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