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All Journal Abdimas Umtas : Jurnal Pengabdian kepada Masyarakat Jurnal Ilmiah Ilmu Terapan Universitas Jambi
Rahmat Ramadan
Universitas Bina Darma
Religion Agriculture, Biological Sciences & Forestry Arts Education Engineering Industrial & Manufacturing Engineering Social Sciences

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Counseling Employee Assistance Program (EAP) – SUPPORT To Improve Employee Mental Health at PT X Palembang Dwi Hurriyati; Rahmat Ramadan
ABDIMAS: Jurnal Pengabdian Masyarakat Vol. 6 No. 2 (2023): ABDIMAS UMTAS: Jurnal Pengabdian Kepada Masyarakat
Publisher : LPPM Universitas Muhammadiyah Tasikmalaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35568/abdimas.v6i2.3236

Abstract

This community service activity aims to increase employee and community knowledge about the importance of the Employee Assistance Program (EAP) for employees designed by the company to improve employee mental health productivity. This service program is expected to help employees to prevent, identify and resolve employee problems, especially mental health, which impacts employee performance. This service activity is carried out at PT. X Palembang. The method of implementing community service activities is carried out to solve problems that exist among employees of PT. X Palembang related to mental health problems in the form of counseling efforts on the application and use of EAP program services at PT. X Palembang. The results of this community service activity are in the form of companion services for employees of PT. X Palembang to conduct coaching and counseling sessions to help solve employee problems to improve employee mental health.
COMPARING DEEP LEARNING AND MACHINE LEARNING FOR DETECTING FAKE NEWS ON SOCIAL MEDIA Ria Andryani; Dedek Julian; Rezki Syaputra; Ahmad Syazili; Ahmad Rusli; Rahmat Ramadan; Edi Surya Negara
Jurnal Ilmiah Ilmu Terapan Universitas Jambi Vol. 9 No. 3 (2025): Volume 9, Nomor 3, September 2025
Publisher : LPPM Universitas Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/jiituj.v9i3.46370

Abstract

One of the critical issues resulting from the increasing penetration of social media is the spread of fake news. This can damage public information and influence mass opinion, leading to conflict. To overcome this problem, machine learning and deep learning-based approaches have been continuously developed to detect fake news on various social media platforms automatically. This article aims to compare the effectiveness of these two approaches in detecting fake news. The methods used include the implementation of traditional machine learning algorithms, such as Support Vector Machines (SVM) and Random Forest, as well as deep learning-based approaches, including Long Short-Term Memory and Self-Organizing Maps. Datasets containing real and fake news from various social media sources are used to train and evaluate these models. Model performance is measured based on accuracy, precision, recall, and F1-score. This study aims to determine which approach is more effective and identify challenges in implementing these algorithms in a dynamic social media environment. The results obtained show that the Random Forest algorithm achieves an accuracy level of 100%, surpassing other algorithms, including Long Short-Term Memory with an F-1 Score of 97%, Self-Organizing Map with an F-1 Score of 96%, and Support Vector Machine with an F-1 Score of 92%.
Co-Authors Ahmad Rusli Ahmad Syazili Andryani, Ria Dedek Julian Dwi Hurriyati Edi Surya Negara Rezki Syaputra