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All Journal JURNAL NASIONAL TEKNIK ELEKTRO Jurnal Teknologi Industrial Research Workshop and National Seminar Jurnal Teknik Kimia dan Lingkungan Journal Serambi Engineering (JSE)
Ferawati, Yohana Fransiska
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Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry 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 Materials Science & Nanotechnology Mechanical Engineering Other

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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.
Studi Potensi Pengolahan Sampah Anorganik Menjadi Refuse Derived Fuel (RDF) Taufiqurohim, Teguh; Gustin Mustika Krista; Gita Nur Sajida; Hermin Kartika Sari; Yohana Fransiska Ferawati
Prosiding Industrial Research Workshop and National Seminar Vol. 16 No. 1 (2025): Vol. 16 No. 1 (2025): Prosiding 16th Industrial Research Workshop and National
Publisher : Politeknik Negeri Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35313/irwns.v16i1.6677

Abstract

Peningkatan volume sampah di Indonesia, khususnya sampah anorganik, menimbulkan dampak lingkungan serius dan menuntut solusi berkelanjutan. Salah satu pengolahan sampah anorganik yang bisa dilakukan adalah dengan mengolahnya menjadi Refuse Derived Fuel (RDF). Pengolahan ini menawarkan solusi ganda dengan mengurangi timbunan sampah di TPA sekaligus menghasilkan sumber energi terbarukan. Karakteristik sampah anorganik seperti plastik, kertas, kain, kayu, dan karet, diidentifikasi sebagai bahan baku potensial RDF berdasarkan sifat fisik dan kimia yang menjanjikan. Penelitian ini bertujuan untuk mengkaji potensi pengolahan sampah anorganik menjadi RDF melalui pendekatan studi pustaka komprehensif. Proses pengolahan RDF meliputi pemisahan, pencacahan, pengeringan, dan pemadatan, yang bertujuan untuk meningkatkan nilai kalor dan densitas bahan bakar. Sampah anorganik seperti plastik memiliki nilai kalor yang relatif tinggi yaitu sekitar 46,5 MJ/kg dan kadar air rendah yaitu sekitar 1,9%. Potensi kuantitas RDF sangat bergantung pada komposisi sampah dan efisiensi pemilahan, dimana porsi sampah anorganik berpotensi mencapai 30-60% dari total timbunan sampah, dengan efisiensi konversi menjadi RDF siap pakai berkisar 50-80%. Secara teknis, teknologi RDF sudah matang, namun tantangan utamanya terletak pada konsistensi pasokan bahan baku dan kualitas pemilahan di sumber. Faktor pendukung meliputi ketersediaan bahan baku dan kebutuhan energi alternatif, sementara faktor penghambat utama adalah rendahnya tingkat pemilahan sampah di masyarakat dan biaya investasi awal yang tinggi. Penelitian ini diharapkan dapat memberikan rekomendasi strategis bagi pemangku kepentingan untuk pengelolaan sampah yang lebih efisien dan berkelanjutan.
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.
Predictive Modeling of Carbon Monoxide with MOS Sensors and Machine Learning: A Potential Tool for Process Safety Improvement Sari, Hermin Kartika; Pratama, Thomas Oka; Ferawati, Yohana Fransiska; Sajida, Gita Nur; Krista, Gustin Mustika; Taufiqurohim, Teguh; Shoerya Shoelarta
JURNAL NASIONAL TEKNIK ELEKTRO Vol 15, No 1: March 2026
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v15n1.1390.2026

Abstract

Carbon monoxide (CO) is a toxic, odorless gas commonly present in industrial processes and poses serious risks to occupational safety and health. This study proposes an optimized machine-learning-based approach to predict CO concentration using metal-oxide semiconductor (MOS) sensor arrays. The model was trained and evaluated on a public dataset comprising 650 time-series measurements from 14 thermally modulated MOS sensors, tested across CO concentrations ranging from 0 to 8.9 ppm under dynamic relative humidity (15%–75%). To optimize computational efficiency and mitigate multicollinearity, a multi-method feature selection strategy that combines Random Forest importance, Recursive Feature Elimination (RFE), and Mutual Information (MI) was implemented, successfully isolating sensors R10, R11, and R13 as the most robust predictors. A Random Forest Regression model, optimized via grid search and validated through five-fold cross-validation, was subsequently developed. The proposed framework demonstrated high predictive accuracy, achieving an R² of 0.884, Root Mean Square Error (RMSE) of 2.189 ppm, Mean Absolute Error (MAE) of 1.215 ppm, and Symmetric Mean Absolute Percentage Error (SMAPE) of 34.27%. These results highlight the potential of combining low-cost, feature-optimized MOS sensor arrays with ensemble machine learning for accurate, real-time gas monitoring. The framework provides a computationally efficient decision-support tool for the early detection of hazardous CO levels, contributing to safer process environments.
Optimization of Agitation Speed and Aeration Rate for Fungal Protein Production from Tofu Whey Using Aspergillus oryzae in a Stirred Tank Bioreactor Keryanti, Keryanti; Manfaati, Rintis; Fauzan, Rizky; Ramadhani, Fauziah; Krista, Gustin Mustika; Ferawati, Yohana Fransiska; Santoso, Budi
Jurnal Teknik Kimia dan Lingkungan Vol. 10 No. 1 (2026): April 2026
Publisher : Politeknik Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jtkl.v10i1.7813

Abstract

Fungal protein, derived from microbial biomass, offers a sustainable protein source and can be produced through fermentation. However, the utilization of tofu whey, an abundant agro-industrial by-product in Indonesia, as a substrate for fungal protein remains underexplored. This study optimizing both agitation speed and aeration rate for Aspergillus oryzae fermentation in a stirred-tank bioreactor. Fermentation was conducted in a 5 L stirred-tank bioreactor with a 3 L working volume for 48 hours at an initial pH of 5 and a temperature of 35℃. Agitation speeds of 150, 200, 250, and 300 rpm were tested at a constant aeration rate of 1.0 vvm to determine the optimum mixing condition. The agitation speed that yielded the highest dry cell weight was then used as the basis for further aeration experiments (0, 0.5, 1.0, and 1.5 vvm). The optimum conditions were obtained at 150 rpm and 1.0 vvm, resulting in a dry cell weight of 7.1 g/L and a protein content of 6.83% (w/w). These findings demonstrate the potential of valorizing tofu whey into fungal protein while highlighting the need for further multi-parameter optimization to enhance protein levels toward single-cell protein standards.
Co-Authors Dwi Budi Santoso Fauzan, Rizky Gita Nur Sajida Keryanti, Keryanti Krista, Gustin Mustika Manfaati, Rintis Pratama, Thomas Oka Ramadhani, Fauziah Sajida, Gita Nur Sari, Hermin Kartika Shoelarta, Shoerya Sihombing, Rony Pasonang Teguh Taufiqurohim