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ERGONOMIC POSTURE CLASSIFICATION OF BENCH WORK UTILIZING MUSCLE DATA: A CASE STUDY IN EDUCATIONAL WORKSHOP Putri, Farika Tono; Purwati, Wiwik; Margana; Supriyo; Prawibowo, Hartanto; Pasmanasari, Elta Diah; Ismail, Rifky; Kadavi, Fadhil Muhammad; Muryanto
Journal of Mechanical Engineering and Applied Technology Vol. 3 No. 2 (2025): VOLUME 3 ISSUE 2 YEAR 2025 (JULY 2025)
Publisher : Politeknik Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32497/jmeat.v3i2.6674

Abstract

Occupational musculoskeletal disorders (MSDs) often result from prolonged non-ergonomic postures, especially in educational and industrial bench work activities. This study presents an approach to classify ergonomic and non-ergonomic working postures using surface electromyography (sEMG) signals and machine learning. sEMG data were recorded from four upper limb muscles during simulated bench work conditions. Time-domain and frequency-domain features were extracted from segmented EMG signals using sliding windows. Dimensionality reduction was performed using Principal Component Analysis (PCA), and classification was carried out using logistic regression. The proposed system achieved an overall classification accuracy of 75% in distinguishing ergonomic and non-ergonomic postures. Visualization using PCA and Linear Discriminant Analysis (LDA) showed clear class separation, validating the discriminatory power of the extracted features. While the small sample size and class imbalance were identified as limitations, the study demonstrates that a simple and interpretable model like Logistic Regression, when combined with proper feature engineering, can yield promising results.This work contributes to the development of low-cost, efficient, and interpretable ergonomic assessment tools. It is particularly relevant for vocational and educational environments where real-time posture monitoring and early prevention of MSDs are essential. Future research should focus on expanding the dataset, exploring deep learning methods, and implementing real-time wearable systems.
Utilization of Potassium Carbonate-Ethylene Glycol as Deep Eutectic Solvent to Delignification Oil Palm Empty Fruit Bunch for Furfural and Ethanol Production Muryanto; Amelia, Fanny; Nurbayti, Siti; Sudiyani, Yanni; Gozan, Misri
Jurnal Sains Materi Indonesia Vol. 25 No. 2 (2024): Jurnal Sains dan Materi Indonesia
Publisher : BRIN Publishing (Penerbit BRIN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jsmi.2024.1260

Abstract

Pretreatment using Deep Eutectic Solvent (DES) was done to reduce the lignin content in oil palm empty fruit bunch (OPEFB). DES solution was prepared by combining Potassium Carbonate (K2CO3) as hydrogen bond acceptors (HBA) and Ethylene Glycol (EG) as hydrogen bond donors (HBD). This study aimed to obtain optimum conditions in the pretreatment process and determine the levels of lignin, cellulose, hemicellulose, and glucose in OPEFB. The pretreatment stage was performed at different temperature parameters, 100, 120, and 150°C, for 60 minutes to produce optimum conditions. Biomass pretreated with DES was hydrolyzed by cellulase complex to obtain glycose and xylose. The results showed that the pretreatment optimum condition value achieved with DES K2CO3:EG (1:10) at 150°C for 60 minutes was able to degrade lignin by 46,06%, hemicellulose by 4,08%, increase cellulose by 60,21%. The glucose and xylose content reached 58.48 g/l and 26.60 g/l, respectively. This sugar has potential for ethanol production from glucose and furfural production from xylose.