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All Journal FLUIDA Jurnal Teknologi
Shoelarta, Shoerya
Jurusan Teknik Kimia, Politeknik Negeri Bandung
Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Energy Engineering Environmental Science Materials Science & Nanotechnology Mechanical Engineering

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Audit Energi unit Kondenser Turbin di PLTP Perspektif Teknik Kimia menggunakan ANFIS Shoerya Shoelarta; Anissa Feni Haryati; Kholiq Hermawan
Fluida Vol 14 No 1 (2021): FLUIDA
Publisher : Jurusan Teknik Kimia, Politeknik Negeri Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35313/fluida.v14i1.3475

Abstract

Audit energy merupakan salah satu metoda yang diwajibkan oleh pemerintah RI dalam peningkatan program konservasi energy diindustri sekaligus sebagai peningkatan produksi atau pengurangan biaya konsumsi energi produksi. Program ini dikenal dikenal dengan nama Proper yang dikelola oleh kementrian lingkungan hidup dan kehutanan. PT Geodipa Energi sebagai pemilik Pembangkit Listrik Tenaga Panas Bumi Patuha yang berkapasitas 60 MW (100%) berusaha mengikuti program ini yang bila lolos akan mendapatkan manfaat berupa insentif. Pengoptimalan kinerja pendinginan baik dikondenser dan cooling tower dengan beberapa parameter yang direkam di DCS digunakan sebaga data input meningkatkan vakum di luaran turbin. Vakum yang dihasilkan akan memperbesar torsi yang dihasilkan sehingga generator terrotasi lebih kuat dan output daya yang dihasilkan meneningkat. Penggunaan artificial intellegence (AI) dalam hal ini ANFIS (Adaptive Neuro Fuzzy Inference System) dikombinasikan dengan pengetahuan praktis teknologi proses kimia dapat memperkirakan optimasi yang akan dicapai. Torsi tambahan ini lebih lanjut diharapkan meningkatkan produksi energi listrik sampai 2 – 3 % dari rata rata 88% kapasits sekarang, tanpa menambah daya pemakaian sendiri (own used power).
In Situ Katalitik Basa pada Proses Pembuatan Biodiesel dari Mikroalga Nannochloropsis sp Shoerya Shoelarta; Ghusrina Prihandini; Mariatha Martha; Rusdiyana Nur Putri Salimi; Fitria Yulistiani
Fluida Vol 15 No 2 (2022): FLUIDA
Publisher : Department of Chemical Engineering, Politeknik Negeri Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35313/fluida.v15i2.4395

Abstract

Biodiesel merupakan salah satu bahan bakar alternatif yang dapat digunakan sebagai pengganti bahan bakar fosil. Mikroalga Nannochloropsis sp merupakan bahan baku terbarukan yang mampu menghasilkan lipid hingga 68% dan berpotensi untuk dijadikan bahan baku pembuatan biodiesel. Penelitian ini bertujuan untuk mengetahui pengaruh variasi konsentrasi katalis NaOH dan suhu yang digunakan terhadap biodiesel yang dihasilkan dari biomassa Nannochloropsis sp yang digunakan sebagai bahan baku, serta menguji densitas dan viskositas dari crude biodiesel yang dihasilkan. Metode yang digunakan dalam pembuatan biodiesel ini yaitu in situ transesterifikasi menggunakan katalis NaOH 1% dan 2%, pada temperatur 50 dan 60 , dan pada tekanan atmosfer. Hasil penelitian menunjukan bahwa penambahan konsentrasi katalis NaOH 2% meningkatkan yield crude biodiesel menjadi 71,03%, densitas dan viskositas crude biodiesel yang mendekati standar SNI adalah 833,75 kg/m3 dan 1,3579 cSt dengan kondisi operasi pada suhu 50oC dan konsentrasi katalis NaOH 2%.
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.
The Effect of Plasma Electrolysis for Biodiesel Synthesis Using Waste Cooking Oil as a Raw Material and KOH Catalyst Shoelarta, Shoerya; Saripudin; Fedianto, Rahmat Dani Dwi; Darojat , Moch Adli; Abdulloh, Sudrajat Harris; Hidayatulloh, Irwan; elizabeth, lidya
Fluida Vol. 16 No. 2 (2023): FLUIDA
Publisher : Department of Chemical Engineering, Politeknik Negeri Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35313/fluida.v16i2.3987

Abstract

The high consumption of cooking oil in Indonesia is directly proportional to the production of used cooking oil waste that can pollute the environment. To overcome this problem, used cooking oil is used as a biodiesel feedstock using the plasma electrolysis method. Plasma electrolysis is a synthesis process by applying a high voltage to the process. This study aims to determine the effect of the plasma electrolysis method with variations in voltage (400 - 500 volts) and the type of reagent (methanol and ethanol) on % yield and characteristics of biodiesel. The process is carried out by reacting used cooking oil with a reagent that is given a voltage until an electric discharge occurs and two layers are formed and the calculation of the specific energy requirements for the synthesis process is carried out. The results show that at a voltage of 500 volts with methanol reagent produces a yield of 85.73% with characteristics according to the SNI 7128:2015 standard such as density 888.36 Kg/m3, viscosity 5.79 Cst, acid number 0.39 mg - KOH/g, content ester 97.56% and flash point 117oC with the exception of water content 0.373%, with a specific energy requirement of 1758,031 J/mL.
Co-Authors Anissa Feni Haryati Darojat , Moch Adli Elizabeth, Lidya Fedianto, Rahmat Dani Dwi Ferawati, Yohana Fransiska Hidayatulloh, Irwan Kholiq Hermawan Krista, Gustin Mustika Mariatha Martha Pratama, Thomas Oka Prihandini, Ghusrina Rusdiyana Nur Putri Salimi Sajida, Gita Nur Sari, Hermin Kartika Saripudin Saripudin Sudrajat Harris Abdulloh Taufiqurohim, Teguh Yulistiani, Fitria