INVOTEK: Jurnal Inovasi Vokasional dan Teknologi

Application of Job Safety Analysis (JSA) to Mitigate Ergonomic Risks in Visual Communication Design Vocational High School

Dwi Septiani — 2026-04-11

This study aimed to identify ergonomic hazards and assess occupational health risk levels among Vocational High School students in the Visual Communication Design program. Using a qualitative approach with 32 Grade XII participants, data were collected through participatory observation of 10 critical work steps and workstation measurements. The analysis, mapped into a Job Safety Analysis (JSA) matrix, revealed significant facility dimensional gaps that forced postural compensation. Results indicated that 60% of work steps were classified as High Risk, with the highest risk scores (R=15) identified in repetitive mouse usage and long-duration static work. These risks were driven by an "almost certain" frequency of exposure, confirming the existence of chronic "silent hazards" within computer laboratories. While limited to a case study in Tangerang City, the findings implied that vocational environments harbour cumulative musculoskeletal risks often overlooked compared to acute safety issues. Consequently, the study recommended administrative controls, specifically disciplined micro-breaks and the 20-20-20 visual rule as the most effective and economical mitigation strategies compared to overhauling physical facilities. This research emphasized the urgency of establishing a healthy work culture for the future creative workforce. Its novelty lay in adapting JSA methodology to detect preventive health risks in non-technical Vocational High School settings, distinct from the conventional focus on physical safety in heavy industries.

Effect of Temperature and Catalyst Concentration on the Quality of Biodiesel Oil Extracted From Black Soldier Fly Larvae (Hermetia Illucens)

Dafit Ari Prasetyo — 2026-04-12

Biodiesel is a renewable and environmentally friendly energy source that can be produced from lipid-rich materials through the transesterification process. Black soldier fly (BSF) larvae (Hermetia Illucens) contain approximately 30–40% fat, making them a potential alternative feedstock for biodiesel production. This study aimed to evaluate the effect of reaction temperature and KOH catalyst concentration on the yield and quality of biodiesel derived from BSF larvae oil. Oil extraction was conducted using the Soxhlet method with n-hexane, where 15 g of dried larvae powder extracted for 240 minutes produced the highest oil yield of 97% (w/w). The extracted oil was then converted into biodiesel through transesterification at two temperatures (65°C and 70°C) and two catalyst concentrations (1% and 2% w/v KOH). Biodiesel quality was analyzed based on SNI 7182:2015. The highest biodiesel yield, 60.19% (v/v), was obtained at 70°C and 1% KOH (A2B1). The resulting biodiesel met major SNI requirements, including density (869.59 kg/m³), kinematic viscosity (4.75 cSt), cetane number (44.25), acid number (0.32 mg KOH/g), iodine number (16.8 g-I₂/100 g), and FAME content (97.45%). These findings confirm that BSF larvae oil is a viable non-conventional feedstock for biodiesel production. This study is limited by the narrow range of reaction variables tested, suggesting that broader optimization—such as variations in alcohol ratio, reaction time, and pretreatment—may further improve yields. Practically, biodiesel production from BSF larvae supports waste valorization because larvae can be cultivated using organic waste streams. Socially, this approach reduces dependence on edible vegetable oils and supports circular economy initiatives. The originality of this research lies in its systematic evaluation of transesterification conditions for BSF-derived oil, providing new insights into the development of insect-based biodiesel technology.

Numerical Structural Response Analysis of Savonius Wind Turbine Blades with Geometric Variation under Aerodynamic Loads

Aldo Alfattah Sudjono — 2026-05-09

Rising demand for electrical energy has accelerated the development of renewable energy technologies, including wind turbines designed to operate at low wind speeds. The savonius vertical-axis wind turbine is a promising option due to its simple configuration and insensitivity to wind direction. Nevertheless, the structural reliability of its blades is strongly affected by their geometry and the material selected. This study focuses on evaluating the structural response of savonius turbine blades under aerodynamic loading using the finite element analysis (FEA) method. Simulations were carried out in ANSYS Workbench R2 2025, considering geometric variations in blade thickness (1.2 mm, 1.5 mm, and 2 mm), aspect ratio (0.8, 1.0, and 1.3), and arc angle (120°, 150°, and 180°). The blades were modeled using AISI 304 stainless steel. The analysis examined von mises equivalent stress, total deformation, and safety factor. The analysis results show that all geometric variations are structurally safe with a safety factor value of 15. The highest maximum von mises stress of 0.081 MPa occurs at a thickness of 1.2 mm, while the lowest value of 0.017 MPa is obtained at an arc angle of 180°. The maximum deformation of 3.38 × 10⁻³ mm occurred at an arc angle of 150°, while the lowest deformation of 6.77 × 10⁻⁴ mm was obtained at an arc angle of 180°, which indicates the most structurally stable configuration.

Grouping of Research Interests of Final Project of Computer Science Students at UINSU Using the Fuzzy C-Means Approach

Harun Al Rasyid — 2026-05-17

Determining research topics for final projects that align with students' competencies and interests remains a challenge in academic management because the process is often influenced by subjective factors. This situation can lead to various impacts, such as inappropriate research topics, delays in study completion, and imbalances in the guidance workload among supervisors. This study aims to cluster the research interests of students preparing their final projects in the Computer Science Study Program at the State Islamic University of North Sumatra (UINSU) using the Fuzzy C-Means (FCM) algorithm. The data used were obtained from a student interest questionnaire from the 2020–2022 intake and historical data on final project titles. Furthermore, the text data underwent preprocessing and was converted into numerical form using the Term Frequency–Inverse Document Frequency (TF-IDF) method. The FCM algorithm was then used to form research interest clusters with fuzzy membership degrees. Based on the results of the cluster quality evaluation, it was found that the most optimal number of clusters was six, with a Silhouette Index value of 0.6311 and a Davies–Bouldin Index of 0.5505, which indicates that the cluster structure formed is classified as good. The clustering results indicated that student interests were dominated by Software Engineering and Artificial Intelligence, with a fairly high degree of overlap. This study combines student interest questionnaire data and historical final project title data, represented using TF-IDF and clustered using the Fuzzy C-Means algorithm to map multidimensional research interests. The results suggest that this approach provides a more objective basis for identifying students’ research tendencies and can support topic recommendation systems and academic supervision planning.