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All Journal Jupiter Jurnal INKOM PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal technoscientia Jurnal Ilmiah Matrik METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Jurnal Sisfokom (Sistem Informasi dan Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Journal of Information Systems and Informatics Indonesian Journal of Electrical Engineering and Computer Science Jurnal Teknologi Informatika dan Komputer JURNAL TEKNOLOGI TECHNOSCIENTIA Journal of Software Engineering Ampera Jurnal Pengabdian Masyarakat Information Technology (JPM ITech) Jurnal Ilmiah Ilmu Terapan Universitas Jambi SmartComp
Andryani, Ria
Universitas Binadarma
Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mechanical Engineering

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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%.
EARLY DETECTION OF ACADEMIC DEPRESSION USING SMARTPHONE-BASED MACHINE LEARNING MODELS Edi Surya Negara; Latius Hermawan; Hastari Mayrita; Desy Arisandy; Mohamad Farozi; Rahmat Ramadan; Sunda Ariana; Ria Andryani
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.v9i3.46375

Abstract

Mental health in developing countries is a common and complex problem. The problem continues to increase and is closely related to low self-confidence, negative interpersonal relationships, and academic depression. This can affect students' ability to complete academic assignments on a university scale. An AI-based early detection application can potentially improve mental health services related to treatment access. This system can help identify users who may be depressed based on the language used, especially for those who are reluctant to seek professional solutions due to the negative stigma of mental health. This study uses a qualitative descriptive method involving observation, in-depth analysis of group conversations, and early detection of academic depression by identifying conversation patterns between students and counselors as the basis for developing a smartphone-based application. This study produced a dataset of 395 depression-level data entries used as training data to develop a machine-learning model. A prototype of an academic depression detection application has been developed as a mobile-based application.
Co-Authors Ahmad Haidar Mirza Ahmad Rusli Ahmad Syazili Aris Budiman Dedek Julian Dedy Syamsuar Deka Ria Agustin Della Resilia Dendi Triadi Desy Arisandy Dinda Lusita Fristiani Anissa Edi Surya Negara Hastari Mayrita Kurniawan Kurniawan Latius Hermawan Lela Triana Mareta Yuliani Mery Sintia Mohamad Farozi Mutiara Pasha Nadela Annggela Obi Mardalena Prihambodo Hendro Saksono Rahmat Ramadan Rahmat Ramadan Reta Dwi Putri Rezki Syaputra Riyan Amanda Robudin Sunda Ariana, Sunda Yepi Kusmeta Yuni Amrina