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All Journal Jurnal Optimasi Sistem Industri Jurnal INTECH Teknik Industri Universitas Serang Raya
Auditya Purwandini Sutarto
Universitas Qomaruddin
Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Industrial & Manufacturing Engineering Mechanical Engineering Transportation

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Evaluasi Prevalensi Keluhan Otot Rangka dan Tingkat Produktivitas Subyektif pada Karyawan Marketing Online Auditya Purwandini Sutarto; Nailul Izzah; Zahrotul Farda
Jurnal INTECH Teknik Industri Universitas Serang Raya Vol. 8 No. 2 (2022): Desember
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/intech.v8i2.5011

Abstract

Karakteristik pekerja marketing online dapat dikategorikan sebagai office work yang berhubungan erat dengan interaksi antara pekerja dengan komponen dalam stasiun kerja. Pekerja menghabiskan sebagian besar waktu dalam posisi duduk di depan komputer yang berkaitan dengan risiko munculnya keluhan otot rangka atau musculoskeletal disorders (MSDs). Penelitian ini be­rtujuan mengetahui tingkat keluhan nyeri otot, penilaian faktor risiko postur kerja, dan tingkat produktivitas individu secara subyektif. Penelitian ini bersifat deskriptif cross-sectional dengan subyek 30 pekerja sales online (total sampling). Pengukuran tingkat keluhan MSDs secara subyektif dilakukan dengan kuesioner Nordic Body Map, penilaian faktor risiko postur kerja meng­gunakan Rapid Office Strain Assessment (ROSA), dan tingkat produktivitas individu dengan Individual Work Performance Questionnaire (IWPQ). Hasil penelitian menunjukkan 90% pekerja mengeluhkan gangguan pada paling sedikit satu anggota tubuh baik pada satu tahun atau tujuh hari terakhir. Rataan skor ROSA untuk seluruh responden sebesar 7,97 yang mengindikasikan perlu perbaikan segera. Tingkat laporan diri produktivitas individu secara umum cukup baik. Namun demikian, pekerja yang mengalami keluhan di bagian tubuh tertentu cenderung melaporkan tingkat produktivitas lebih rendah dibandingkan mereka yang tidak mengalami. Usulan perbaikan yang dapat dilakukan mencakup perbaikan fasilitas kerja dan organisasi kerja seperti pengaturan waktu istirahat, peregangan, dan ergonomi partisipatori.
Physiological Signals as Predictors of Mental Workload: Evaluating Single Classifier and Ensemble Learning Models Nailul Izzah; Auditya Purwandini Sutarto; Ade Hendi; Maslakhatul Ainiyah; Muhammad Nubli bin Abdul Wahab
Jurnal Optimasi Sistem Industri Vol. 22 No. 2 (2023): Published in December 2023
Publisher : The Industrial Engineering Department of Engineering Faculty at Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/josi.v22.n2.p81-98.2023

Abstract

With a growing emphasis on cognitive processing in occupational tasks and the prevalence of wearable sensing devices, understanding and managing mental workload has broad implications for safety, efficiency, and well-being. This study aims to develop machine learning (ML) models for predicting mental workload using Heart Rate Variability (HRV) as a representation of the Autonomic Nervous System (ANS) physiological signals. A laboratory experiment, involving 34 participants, was conducted to collect datasets. All participants were measured during baseline, two cognitive tests, and recovery, which were further separated into binary classes (rest vs workload). A comprehensive evaluation was conducted on several ML algorithms, including both single (Support Vector Machine/SVM and Naïve Bayes) and ensemble learning (Gradient Boost and AdaBoost) classifiers and incorporating selected features and validation approaches. The findings indicate that most HRV features differ significantly during periods of mental workload compared to rest phases. The SVM classifier with knowledge domain selection and leave-one-out cross-validation technique is the best model (68.385). These findings highlight the potential to predict mental workload through interpretable features and individualized approaches even with a relatively simple model. The study contributes not only to the creation of a new dataset for specific populations (such as Indonesia) but also to the potential implications for maintaining human cognitive capabilities. It represents a further step toward the development of a mental workload recognition system, with the potential to improve decision-making where cognitive readiness is limited and human error is increased.
Co-Authors Ade Hendi Maslakhatul Ainiyah Muhammad Nubli bin Abdul Wahab Nailul Izzah Zahrotul Farda