Article Per Year (5 Year)
p-Index From 2020 - 2025
0.961
P-Index
This Author published in this journals
All Journal Jurnal Teknologi Chemical Engineering Journal Storage (CEJS) CHEMTAG Journal of Chemical Engineering Industrial Research Workshop and National Seminar Serambi Engineering
Taufiqurohim, Teguh
Unknown Affiliation
Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Mechanical Engineering Other

Published : 6 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Jurnal Teknologi

 1 
Potensi Ekstrak Kunyit sebagai Inhibitor Korosi Ramah Lingkungan untuk Baja Karbon Rendah Sajida, Gita Nur; Krista, Gustin Mustika; Sari, Hermin Kartika; Taufiqurohim, Teguh; Ferawati, Yohana Fransiska; Sihombing, Rony Pasonang
Jurnal Teknologi Vol 25, No 2 (2025): Agustus 2025
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/teknologi.v25i2.7483

Abstract

Corrosion is a significant metal degradation problem causing substantial economic losses, particularly in the oil and gas industry. Traditional chromate-based inhibitors are toxic, spurring the search for eco-friendly alternatives. This article explores the potential of Curcuma longa (turmeric) extract as a natural corrosion inhibitor for low-carbon steel plates in acidic and basic media. This study tests turmeric extract on low-carbon steel plates in HCl and NaOH media using immersion test (weight loss). The active compound curcumin in turmeric extract effectively inhibits corrosion. Its effectiveness is highly dependent on the solvent type and concentration; 0.25% NaOH yields up to ~87% effectiveness at 1000 ppm turmeric concentration, with a corrosion rate of 0.697 mdd, significantly outperforming 0.25% HCl which only reaches ~22% at similar concentrations with a corrosion rate of 133.99 mdd. Increasing NaOH concentration to 0.50% drastically enhances initial effectiveness, reaching ~63% at 100 ppm, and 90% at 400 ppm, with the corrosion rate dropping to 0.668 mdd. 
Machine Learning-Based Prediction of Sleep Disorders from Lifestyle and Physiological Data: A Cross-Occupational Study Sari, Hermin Kartika; Shoelarta, Shoerya; Pratama, Thomas Oka; Sajida, Gita Nur; Krista, Gustin Mustika; Ferawati, Yohana Fransiska; Taufiqurrahim, Teguh
Jurnal Teknologi Vol 25, No 2 (2025): Agustus 2025
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/teknologi.v25i2.7507

Abstract

Sleep disorders are increasingly recognized as critical public health concerns, particularly among working populations where occupational stress, lifestyle factors, and physiological imbalances intersect. This study explores the predictive capacity of machine learning models, including Random Forest, Support Vector Machine (SVM), and XGBoost to identify sleep disorders (None, Insomnia, and Sleep Apnea) using a dataset comprising demographic, occupational, lifestyle, and physiological variables. The dataset, drawn from 400 individuals, was preprocessed through normalization, one-hot encoding, and SMOTE to address class imbalance. Feature selection was conducted using correlation analysis, RFE, and Random Forest importance scores. Models were trained with stratified sampling and optimized using 5-fold cross-validation. XGBoost outperformed the others with an accuracy of 0.90 and an F1-score of 0.88, followed by Random Forest (0.875, 0.86), while SVM lagged (0.825, 0.71). Confusion matrix analysis revealed consistent misclassification between Insomnia and Sleep Apnea, reflecting overlapping symptomatology and low feature correlation. Occupational analysis showed that manual laborers exhibited higher stress levels and shorter sleep durations, particularly those with insomnia. These findings highlight the value of integrating occupational and physiological data into predictive modeling and underscore the potential of ensemble learning methods in health informatics. This study supports the development of early detection systems for sleep disorders tailored to occupational risk profiles.
Co-Authors Ferawati, Yohana Fransiska Gita Nur Sajida Gustin Mustika Krista Herawati Budiastuti Krista, Gustin Mustika Pratama, Thomas Oka Sajida, Gita Nur Sari, Hermin Kartika Shoelarta, Shoerya Sihombing, Rony Pasonang Yasinta, Fatima Zahra, Salma Amara