Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal Journal of Mechanical Engineering and Applied Technology
Kadavi, Fadhil Muhammad
Unknown Affiliation
Automotive Engineering Control & Systems Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Rancang Bangun Alat Pengukur Jarak Berbasis Arduino Uno dengan Sensor Ultrasonik Arum, Adilla Safna; Kadavi, Fadhil Muhammad; Pratama, Yhoga Putra; Fitrianti, Friska Ayu
Journal of Mechanical Engineering and Applied Technology Vol. 2 No. 1 (2024): VOLUME 2 ISSUE 1 YEAR 2024 (MARCH 2024)
Publisher : Politeknik Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32497/jmeat.v2i1.5464

Abstract

Instrument equipment is part of the equipment installed on the tool intended to find out and get the desired data from an activity. One of the important instrument equipment in various fields is the measuring instrument. A jaral measuring instrument is basically a tool to find out how much distance or how long the distance is from an object. The need for measuring instruments used and having high accuracy encourages the creation of more measuring instruments. The purpose of this study is the development of a prototype distance measuring instrument based on Arduino uno microcontroller using ultrasonic sensor HC-SR04. This ultrasonic sensor uses the speed of sound to measure the distance with a magnitude of 340 m/s, thus knowing the time between sending the signal and receiving the signal so that the distance between the transmitter and the receiving object can be calculated. The distance measurement data will then be displayed on a 16 x 2 liquid crystal display for easy reading. This design is controlled through the Arduino Uno R3 kit based on ATMEGA 328 P which is implemented in an acrylic plastic plate as a place for the circuit. The test results from the distance measurement instrument research have a measurement error value range of 0% - 0.067%, so it can be said that the distance measuring instrument from this study has a high accuracy value.
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.
Co-Authors Arum, Adilla Safna Fitrianti, Friska Ayu Margana Margana Muryanto Pasmanasari, Elta Diah Pratama, Yhoga Putra Prawibowo, Hartanto Purwati, Wiwik Putri, Farika Tono Rifky Ismail Supriyo