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All Journal International Journal of Engineering, Science and Information Technology IRA Jurnal Pengabdian Kepada Masyarakat
Sutrisno, Nos
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Sosialisasi Pemberdayaan UMKM dengan Pendekatan Metode Taguchi pada Perbaikan Kualitas Kerupuk Lipat Haniza, Haniza; Silviana, Nukhe Andri; Sutrisno, Nos; Munte, Sirmas; Nainggolan, Lorena
IRA Jurnal Pengabdian Kepada Masyarakat (IRAJPKM) Vol 2 No 3 (2024): Desember
Publisher : CV. IRA PUBLISHING

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56862/irajpkm.v2i3.172

Abstract

The tri dharma of higher education is a task and function for every lecturer that must be carried out every semester, and one of these activities is carrying out community service. The Industrial Engineering study program had the opportunity to implement it with MSMEs in making folded crackers in Sentis village, Percut Sei Tuan subdistrict. Based on the data, it was found that the quality of crackers was damaged by 21% per month, causing a decrease in income. This service aims to educate entrepreneurs to improve product quality so that cracker sales can increase and reduce damaged products using the Taguchi method.  From the experimental results, it was found that quality improvements in the form of taste, durability and crispness of crackers could be improved through 3 stages, namely the drying factor for 7 hours (A1), the optimal baking factor for 13 minutes (B1), and the optimal frying factor for 2 minutes (C2). The results of this experiment succeeded in improving the quality of crackers, in line with consumer expectations, and it is hoped that they will be able to restore and increase sales which previously experienced a decline.
Predicting Burnout in Start-Up Environments: A Multivariate Risk Scoring Approach for Early Managerial Intervention Sutrisno, Nos; Elveny, Maricha; Lubis, Andre Hasudungan; Syah, Rahmad; Hartono, Hartono; Krisdayanti, Sabina
International Journal of Engineering, Science and Information Technology Vol 5, No 4 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i4.1663

Abstract

Start-up organisations operate under fast timelines, lean staffing, and constantly shifting priorities, exposing employees to chronic workload pressure and emotional strain. Unmanaged burnout in these settings threatens individual well-being, talent retention, and long-term execution capacity. This study proposes a multivariate burnout risk scoring approach that aims to identify and prioritise employees at elevated risk before full deterioration occurs, enabling early managerial intervention rather than reactive recovery. The proposed pipeline integrates principal component analysis (PCA), Random Forest, and Support Vector Machine (SVM). PCA is first applied to reduce redundancy across workplace indicators, yielding five principal components (PC1–PC5) that together explain 88% of the total variance in self-reported stress level, job satisfaction, emotional exhaustion, work-life balance, performance, and social interaction. These components are then used as predictors in two supervised classification models, Random Forest and SVM, to estimate the likelihood that each employee belongs to a high-burnout-risk class. The Random Forest model achieved an accuracy of 88%, and the SVM model achieved an accuracy of 86%, demonstrating strong predictive capability in distinguishing higher-risk employees from lower-risk employees. The resulting predicted probability is interpreted as an individualised burnout risk score, which can be mapped to action categories such as workload redistribution, role clarification, targeted supervisory check-ins, or temporary protection from critical-path tasks. In this way, the framework operationalises burnout prediction not only as a detection task but also as an actionable decision-support signal for leaders. The study therefore offers both a quantitative method for forecasting burnout in start-up environments and a practical structure for translating prediction into preventive intervention.
Co-Authors Andre Hasudungan Lubis Elveny, Maricha Haniza Hartono Hartono Krisdayanti, Sabina Nainggolan, Lorena Rahmad Syah, Rahmad Sirmas Munte