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All Journal JOMLAI: Journal of Machine Learning and Artificial Intelligence Jurnal Inovasi Artificial Intelligence & Komputasional Nusantara
Ratih Manalu
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Computer Science & IT Education Engineering Mathematics

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Detection of Mental Health Tendencies Using Naïve Bayes Based on Social Media Activity Jeremi Sibarani; Ratih Manalu; Dongan Parulian Hutasoit; Wilman Arif Telaumbanua; Victor Asido Elyakim P
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 4 No. 2 (2025): Juni 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v4i2.5959

Abstract

The development of social media has had a significant impact on individual mental health. This study aims to detect mental health trends based on user activity on social media using the Naïve Bayes algorithm. The data used is sourced from the Kaggle platform and collected through web scraping techniques with keywords related to mental health and social media activity. The analysis process includes data preprocessing, classification using Naïve Bayes, and evaluation of model performance by dividing training and test data at a ratio of 60:40, 70:30, and 80:20. The results showed that the Naïve Bayes method was able to classify mental health tendencies with the highest accuracy of 75.17% at a ratio of 60:40. Precision and recall were higher for the “Troubled” category compared to the “Good” category, showing the effectiveness of the model in detecting indications of mental disorders. However, there is still a prediction imbalance that affects the overall accuracy. These findings suggest that the Naïve Bayes algorithm can be a tool in social media-based mental health early detection, which can be used by health practitioners and researchers to design more appropriate intervention strategies.
Analisis Sistem Keamanan Pada Sistem Operasi Windows Dengan Metode Clean Instal Febiola, Adinda; Ratih Manalu; Retno Ajeng Kartika Said; Indra Gunawan; Sumarno, Sumarno; Heru Satria Tambunan
Jurnal Inovasi Artificial Intelligence & Komputasional Nusantara Vol. 1 No. 1 (2024): Volume 1 No 1 Tahun 2024
Publisher : PT Siantar Codes Academy Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.260396/0n1zyh66

Abstract

Saat ini informasi berkembang sangat pesat dan semakin canggih. Semakin canggihnya perangkat teknologi informasi berarti semakin banyak pula masyarakat yang menggunakannya. Salah satunya adalah perkembangan computer. Sistem operasi windows adalah salah satu sistem operasi yang paling banyak digunakan di seluruh dunia dan merupakan target utama serangan cyber.  Metode penelitian yang dilakukan terdiri dari dua tahap yaitu pengamatan langsung (observasi) dan studi Pustaka. Kerentanan pada system keamanan windows yang dapat dieksploitasi menggunakan metode dan Solusi clean install direkomendasikan dalam beberapa cara. Kerentanan desain fitur keamanan yaitu windows smart app control dan smart screen mengandung cacat desain yang memungkinkan akses awal tanpa peringatan keamanan untuk mengatasi permasalahan tersebut, setiap system keamanan harus mempunyai metode algoritmik tersendiri untuk mengatasi permasalahan yang ada. Setiap system operasi mempunyai permasalahannya masing masing. Setiap system operasi menggunakan algoritma yang berbeda tergantung pada fitur fiturnya, yang membuat fitur fitur tersebut sulit diretas, disusupi, atau dirusak oleh berbagai ancaman.
Co-Authors Dongan Parulian Hutasoit Febiola, Adinda Heru Satria Tambunan Indra Gunawan Jeremi Sibarani Retno Ajeng Kartika Said Sumarno . Victor Asido Elyakim P Wilman Arif Telaumbanua