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All Journal Jurnal Informatika dan Teknik Elektro Terapan Jurnal Andalas: Rekayasa dan Penerapan Teknologi
Refki Budiman
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Computer Science & IT Education Electrical & Electronics Engineering Health Professions Industrial & Manufacturing Engineering Other

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Rekayasa Ulang Pendinginan Transformator Daya Tua dengan ONAF dan Evaluasi Berbasis PDCA untuk Efisiensi dan Keandalan Refki Budiman; Budi, Baik; Faizal, Jhonny
Jurnal Andalas: Rekayasa dan Penerapan Teknologi Vol. 5 No. 2 (2025): Desember 2025
Publisher : Electrical Engineering Department Faculty of Engineering Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jarpet.v5i2.118

Abstract

Transformator daya merupakan aset kritis dalam sistem kelistrikan industri, di mana kegagalannya dapat menyebabkan kerugian produksi yang signifikan. Studi kasus ini menginvestigasi serangkaian kegagalan berulang pada tiga unit transformator daya tua berkapasitas 30 MVA, 150/6,3 kV di PT Semen Padang, yang telah mengakibatkan terhentinya operasional pabrik. Kegagalan-kegagalan ini, yang termanifestasi sebagai kebocoran minyak, aktivasi Pressure Relief Device (PRD), dan trip pemutus daya, secara konsisten berkorelasi dengan suhu operasi yang tinggi. Melalui pendekatan metodologis yang terstruktur, termasuk analisis akar penyebab menggunakan diagram Ishikawa dan Nominal Group Technique (NGT), pemanasan berlebih (overheating) diidentifikasi sebagai penyebab dominan. Sebagai solusi, sebuah sistem pendingin paksa Oil Natural Air Forced (ONAF) otomatis diimplementasikan dengan biaya rendah
COMPARISON OF GAUSSIAN NAIVE BAYES AND RANDOM FOREST FOR ANEMIA CLASSIFICATION USING HEMATOLOGICAL PARAMETERS baik budi; Refki Budiman; Queen Hesti Ramadhamy
Jurnal Informatika dan Teknik Elektro Terapan Vol. 14 No. 3 (2026)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v14i3.11019

Abstract

Anemia is a global health problem affecting approximately 1.92 billion people, or 24% of the population, according to the WHO. Accurate early detection is crucial for data-driven healthcare. This study evaluates two machine learning algorithms, Gaussian Naive Bayes (GNB) and Random Forest (RF). The classification is based on four key hematological parameters: Hemoglobin (Hb), Mean Corpuscular Volume (MCV), Mean Corpuscular Hemoglobin (MCH), and Mean Corpuscular Hemoglobin Concentration (MCHC). GNB relies on Bayes' Theorem with Gaussian distribution assumptions, whereas RF is a decision-tree-based ensemble method capable of capturing non-linear patterns without specific distributional assumptions. Evaluated using 5-fold cross-validation and standard metrics (accuracy, precision, recall, F1-score), results showed that RF outperformed GNB. RF achieved 94.2% accuracy (CV 94.9% ± 1.1%), compared to GNB's 90.5% (CV 90.1% ± 1.3%). RF feature importance confirmed Hb as the dominant predictor (score 0.562), aligning with its strong correlation to anemia (r = −0.80). Although not surpassing larger-scale studies, these results remain highly competitive. Ultimately, this research provides evidence to support the development of automated, data-driven clinical decision support systems for anemia detection.
Co-Authors Baik Budi Budi, Baik Faizal, Jhonny Ramadhamy, Queen Hesti