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All Journal Buletin Ilmiah Sarjana Teknik Elektro Journal of Current Studies in SDGs Journal of Current Studies in SDGs
Fendi Achmad
State University of Surabaya
Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Environmental Science Social Sciences Other

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Advancing SDG 4 through Contextual Robotics Learning Devices to Enhance Technical and Problem-Solving Skills in Vocational Education Muhamad Zuhrie; Agus Wiyono; Fendi Achmad; Dwi Aqidah; Afrizal Luthfi Eka Arnatha; Muhammad Nawwarudin Nawwarudin
Journal of Current Studies in SDGs Vol. 2 No. 4 (2026): December
Publisher : Sekolah Tinggi Agama Islam Sabilul Muttaqin Mojokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63230/jocsis.2.4.235

Abstract

Objective: To development of a comprehensive robotics learning device package for vocational engineering education based on Contextual Teaching and Learning (CTL) principles. This research contributes to Sustainable Development Goal 4 (SDG 4) by promoting quality education through innovative, contextual, and competency-based learning approaches that support technical skill development and problem-solving abilities. Method: The research employed a Research and Development (R&D) methodology guided by the 4-D model (Define, Design, Develop, Disseminate) and the ADDIE framework. The developed learning package consists of six components: Mechanical Robot Module, Electronics Robot Module, Software Robot Module, Student Worksheets (LKPD), Learning Evaluation Instruments, and Robotics Practicum Guide. Validation was conducted by experts, followed by initial student trials to evaluate feasibility and learning responses. Results:  Expert validation across seven aspects resulted in an average feasibility score of 86.6%, categorized as Very Feasible. Initial student trials demonstrated positive responses, showing improvements in learning engagement, module attractiveness, instructional interactivity, and robotics skills through contextual project-based problem-solving activities. The developed learning package achieved an overall research achievement rate of 90.6%. Novelty: The study provides a novel contribution by integrating CTL principles into a comprehensive robotics learning device package that connects theoretical knowledge with real-world industrial applications. The findings highlight how contextual technology-based learning supports SDG 4 by strengthening quality vocational education, enhancing student competencies, and preparing learners for industry-oriented challenges.
Advancing SDG 4 through Contextual Robotics Learning Devices to Enhance Technical and Problem-Solving Skills in Vocational Education Muhamad Zuhrie; Agus Wiyono; Fendi Achmad; Dwi Aqidah; Afrizal Luthfi Eka Arnatha; Muhammad Nawwarudin Nawwarudin
Journal of Current Studies in SDGs Vol. 2 No. 4 (2026): December
Publisher : Sekolah Tinggi Agama Islam Sabilul Muttaqin Mojokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63230/jocsis.2.4.235

Abstract

Objective: To development of a comprehensive robotics learning device package for vocational engineering education based on Contextual Teaching and Learning (CTL) principles. This research contributes to Sustainable Development Goal 4 (SDG 4) by promoting quality education through innovative, contextual, and competency-based learning approaches that support technical skill development and problem-solving abilities. Method: The research employed a Research and Development (R&D) methodology guided by the 4-D model (Define, Design, Develop, Disseminate) and the ADDIE framework. The developed learning package consists of six components: Mechanical Robot Module, Electronics Robot Module, Software Robot Module, Student Worksheets (LKPD), Learning Evaluation Instruments, and Robotics Practicum Guide. Validation was conducted by experts, followed by initial student trials to evaluate feasibility and learning responses. Results:  Expert validation across seven aspects resulted in an average feasibility score of 86.6%, categorized as Very Feasible. Initial student trials demonstrated positive responses, showing improvements in learning engagement, module attractiveness, instructional interactivity, and robotics skills through contextual project-based problem-solving activities. The developed learning package achieved an overall research achievement rate of 90.6%. Novelty: The study provides a novel contribution by integrating CTL principles into a comprehensive robotics learning device package that connects theoretical knowledge with real-world industrial applications. The findings highlight how contextual technology-based learning supports SDG 4 by strengthening quality vocational education, enhancing student competencies, and preparing learners for industry-oriented challenges.
Performance Evaluation of Sensor Data Filtering Methods for Signal Processing in TVET Learning Applications Farid Baskoro; Hisham A. Shehadeh; Hewa Majeed Zangana; Tri Wrahatnolo; Puput Wanarti Rusimamto; Fendi Achmad; Aristyawan Putra Nurdiansyah
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 4 (2026): August
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i4.16883

Abstract

Technical and Vocational Education and Training (TVET) learning requires sensor measurement data that are stable, accurate, and easy to interpret. Raw LiDAR sensor data often contain fluctuations that may interfere with the readability results. This study employed an experimental-comparative design by comparing Moving Average, Median Filter, Savitzky-Golay, Butterworth, and Simple Kalman Filter. The data acquisition system used a VL53L0X LiDAR sensor and ESP32 microcontroller. Data processing was conducted in MATLAB on 10,500 samples at a sampling frequency of 50 Hz. The evaluation was carried out based on error metrics, signal stability, noise reduction, and filter responsiveness. The raw data had a standard deviation of 111.26 and still showed fluctuations that required reduction. A Greenhouse–Geisser-corrected repeated-measures ANOVA showed a significant effect of filtering method on segment-level residual RMSE, F(1.10,44.92)=26.23, p<0.001, partial η2=0.390. Bonferroni-adjusted comparisons showed that Savitzky–Golay produced significantly lower residual RMSE than the other methods, indicating stronger preservation of the raw-signal pattern. The results showed that Savitzky–Golay achieved the best overall trade-off, with the lowest residual deviation, the highest estimated SNR of 32.154 dB, and good pattern preservation without excessive smoothing. Butterworth and Simple Kalman provided stronger fluctuation reduction, although Kalman introduced greater deviation and a 39-sample delay. Moving Average offered simple smoothing, whereas the Median Filter was more suitable for impulsive noise and outliers. This study contributes a comparative evaluation of filtering methods from both signal-processing and TVET pedagogical perspectives, supporting filter selection based on smoothness, readability, noise reduction, and responsiveness in signal processing.
Co-Authors Afrizal Luthfi Eka Arnatha Agus Wiyono Aristyawan Putra Nurdiansyah Dwi Aqidah Farid Baskoro Hewa Majeed Zangana Hisham A. Shehadeh Muhamad Zuhrie Muhammad Nawwarudin Nawwarudin Puput Wanarti Rusimamto Tri Wrahatnolo