Article Per Year (5 Year)
p-Index From 2020 - 2025
0.702
P-Index
This Author published in this journals
All Journal Jurnal Teknik Industri Terintegrasi (JUTIN) Jurnal Teknologi Serambi Engineering
Sajida, Gita Nur
Unknown Affiliation
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Mechanical Engineering

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
Efektivitas Ekstrak Bunga Telang (Clitoria ternate L.) sebagai Inhibitor Korosi Ramah Lingkungan dalam Media Asam Ferawati, Yohana Fransiska; Sajida, Gita Nur; Krista, Gustin Mustika; Sari, Hermin Kartika; Taufiqurohim, Teguh; Sihombing, Rony Pasonang
Jurnal Serambi Engineering Vol. 10 No. 3 (2025): Juli 2025
Publisher : Faculty of Engineering, Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Corrosion is an indicator of metal quality degradation. One method to prevent corrosion is by using corrosion inhibitors. Corrosion inhibitors are compounds added to corrosive media to reduce the rate of corrosion. The use of environmentally friendly natural inhibitors such as butterfly pea flower extract can serve as an alternative to hazardous chemical inhibitors. This study aims to evaluate the performance of natural inhibitors derived from butterfly pea flower extract in preventing corrosion of low-carbon steel plates. The experiment was conducted in 0.25 N and 0.5 N HCl media with inhibitor concentrations ranging from 100 to 1000 ppm. The steel plates were immersed for 7 days. The parameters studied were corrosion rate and inhibitor efficiency. The results showed that higher inhibitor concentrations led to lower corrosion rates in both acidic media. The lowest corrosion rate was obtained at 1000 ppm inhibitor concentration, with a value of 228.70 mdd and an efficiency of 32.12% in 0.5 N HCl solution, while in 0.25 N HCl solution, the lowest corrosion rate was 156.69 mdd with an efficiency of 30.66%. This study indicates that butterfly pea flower extract has potential as an effective corrosion inhibitor for low-carbon steel plates in an acid medium. This finding supports its application as a sustainable alternative for corrosion control.
Pengembangan Katalis Water-Gas Shift: Perspektif dari Sistem Medium Temperature Shift (MTS) Liska, Salma; Sajida, Gita Nur; Legawati, Lisa; Suhendri, Suhendri; Meldha, Zuqni; Yolanda, Yogi; Hendri, Yola Bertilsya; Rusmana, Marcela Anandita
Jurnal Teknik Industri Terintegrasi (JUTIN) Vol. 8 No. 3 (2025): July
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jutin.v8i3.48117

Abstract

The water-gas shift (WGS) reaction is a crucial process for increasing hydrogen (H₂) production by minimizing carbon monoxide (CO) content. It is widely applied in the petrochemical industry to reduce CO levels in syngas produced from hydrocarbon reforming. Conventionally, this reaction is conducted in two stages: high-temperature shift (HTS) and low-temperature shift (LTS). However, this approach presents both technical and economic limitations. To address these issues, catalysts capable of operating optimally at intermediate temperatures—referred to as medium-temperature shift (MTS)—have been developed. The scope of discussion in this article consists of catalyst development challenges such as sintering, deactivation, and side reactions. This review is intended to serve as a reference for the development of efficient WGS catalysts under MTS conditions, along with modification strategies aimed at enhancing their performance based on current research findings.
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 Hendri, Yola Bertilsya Krista, Gustin Mustika Lisa Legawati Pratama, Thomas Oka Rusmana, Marcela Anandita Salma Liska Sari, Hermin Kartika Shoelarta, Shoerya Sihombing, Rony Pasonang Suhendri Suhendri Taufiqurohim, Teguh Yolanda, Yogi Zuqni Meldha