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All Journal SKANIKA: Sistem Komputer dan Teknik Informatika Masyarakat Berkarya: Jurnal Pengabdian dan Perubahan Sosial
Annisa Maulana Majid
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Engineering Law, Crime, Criminology & Criminal Justice Public Health

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Pelatihan Pembinaan UMKM Berbasis Teknologi untuk Meningkatkan SDM Pelaku Usaha Ayam Balung Empuken Citra Anisa Tika Putri; Annisa Maulana Majid; Dwi Astuti; Fahrurozi Muarief
Masyarakat Berkarya : Jurnal Pengabdian dan Perubahan Sosial Vol. 2 No. 2 (2025): Mei : Masyarakat Berkarya : Jurnal Pengabdian dan Perubahan Sosial
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/karya.v2i2.1369

Abstract

As the backbone of the global economy, Micro, Small, and Medium Enterprises (MSMEs) have a very important role. One of the success factors for SMEs is the effective management of Human Resources (HR). HR management is not only about recruiting and retaining employees, but it also involves strategies that blend human potential with business goals. Additionally, managing an employee team can be a challenge that requires the right skills and strategies. From sourcing top talent to designing effective training programs, everything needs to be well organized to support business growth and sustainability. The Community Service Activity of the Faculty of Economics and Business, Universitas Pelita Bangsa will be carried out online and offline (hybrid), located at the Alam Raya Residence Cluster Block B2 No 12A, Sukasari, Serang Baru, Bekasi Regency and also via Zoom.
Komparasi Algoritma Klasifikasi Machine Learning Dengan Penerapan Metode Ensemble Stacking untuk Menganalisa Sentimen terhadap Kesehatan Mental Annisa Maulana Majid; Karina Imelda; Ismasari Nawangsih
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 8 No 2 (2025): Jurnal SKANIKA Juli 2025
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v8i2.3561

Abstract

Mental health often goes undetected due to the absence of physical symptoms, which hinders timely and appropriate intervention. Many individuals choose to express their emotions on social media rather than access professional services. However, the use of social media can potentially worsen mental health conditions and even impact physical well-being. Therefore, early detection through the analysis of digital data, particularly social media posts, using machine learning approaches is essential. Previous research on mental health sentiment analysis has utilized classification algorithms, but accuracy improvement remains necessary. This study compares single classification algorithms and applies an ensemble stacking method that combines multiple classifiers as base learners and a meta-learner. The results show that the stacking method achieves a higher accuracy of 88.13%.
Co-Authors Citra Anisa Tika Putri Dwi ASTUTI Fahrurozi Muarief Karina Imelda Nawangsih, Ismasari