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All Journal Al Ishlah Jurnal Pendidikan Jurnal Penelitian Pendidikan IPA (JPPIPA) Journal of Innovative and Creativity
Arwizet Arwizet
Universitas Negeri Padang
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Education Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Physics Social Sciences Other

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Prediksi Beban Turbin Uap Menggunakan Machine Learning Berbasis Random Forest untuk Optimalisasi Efisiensi Turbin Uap Pada PLTU Ombilin Diki Alfarizi; Arwizet Arwizet; Refdinal Refdinal; Primawati Primawati
Journal of Innovative and Creativity Vol. 5 No. 2 (2025)
Publisher : Fakultas Ilmu Pendidikan Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/joecy.v5i2.2879

Abstract

Penelitian ini mengembangkan model prediktif untuk estimasi beban turbin uap dengan mengimplementasikan teknik machine learning Random Forest dalam rangka optimasi efisiensi operasional turbin uap pada fasilitas PLTU Ombilin. Proses penelitian melibatkan akuisisi data operasional historis yang kemudian melalui tahap preprocessing menggunakan normalisasi Min-Max Scaler dan pembagian dataset dengan rasio 80:20 untuk training dan testing. Optimasi model Random Forest dilakukan melalui hyperparameter tuning untuk memperoleh konfigurasi optimal, menghasilkan performa dengan MAE 1.024, RMSE 0.987, dan R-squared 0.893 pada evaluasi testing. Hasil evaluasi mengindikasikan kemampuan model dalam menghasilkan prediksi akurat dengan generalisasi yang memadai terhadap data baru. Implementasi model ini diproyeksikan dapat berfungsi sebagai tool decision support dalam manajemen operasional pembangkit, meningkatkan thermal efficiency turbin uap, dan mendukung reduksi konsumsi fuel serta emisi greenhouse gas. Riset ini merekomendasikan eksplorasi algoritma komplementer seperti Gradient Boosting, XGBoost, dan Artificial Neural Network untuk benchmarking performa dan validasi model yang lebih komprehensif.
Prediksi Beban Turbin Uap Menggunakan Machine Learning Berbasis Random Forest untuk Optimalisasi Efisiensi Turbin Uap Pada PLTU Ombilin Diki Alfarizi; Arwizet Arwizet; Refdinal Refdinal; Primawati Primawati
Journal of Innovative and Creativity Vol. 5 No. 3 (2025)
Publisher : Fakultas Ilmu Pendidikan Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/joecy.v5i3.2880

Abstract

Penelitian ini mengembangkan model prediktif untuk estimasi beban turbin uap dengan mengimplementasikan teknik machine learning Random Forest dalam rangka optimasi efisiensi operasional turbin uap pada fasilitas PLTU Ombilin. Proses penelitian melibatkan akuisisi data operasional historis yang kemudian melalui tahap preprocessing menggunakan normalisasi Min-Max Scaler dan pembagian dataset dengan rasio 80:20 untuk training dan testing. Optimasi model Random Forest dilakukan melalui hyperparameter tuning untuk memperoleh konfigurasi optimal, menghasilkan performa dengan MAE 1.024, RMSE 0.987, dan R-squared 0.893 pada evaluasi testing. Hasil evaluasi mengindikasikan kemampuan model dalam menghasilkan prediksi akurat dengan generalisasi yang memadai terhadap data baru. Implementasi model ini diproyeksikan dapat berfungsi sebagai tool decision support dalam manajemen operasional pembangkit, meningkatkan thermal efficiency turbin uap, dan mendukung reduksi konsumsi fuel serta emisi greenhouse gas. Riset ini merekomendasikan eksplorasi algoritma komplementer seperti Gradient Boosting, XGBoost, dan Artificial Neural Network untuk benchmarking performa dan validasi model yang lebih komprehensif.
Analisis Kinerja Boiler Pada PLTU Ombilin Berdasarkan Variabel Operasional Menggunakan Model Machine Learning Random Forest Devky Meinaldi Fernanda; Arwizet Arwizet; Yolli Fernanda; Andre Kurniawan
Journal of Innovative and Creativity Vol. 5 No. 2 (2025)
Publisher : Fakultas Ilmu Pendidikan Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/joecy.v5i2.2911

Abstract

Penelitian ini mengembangkan model prediksi beban PLTU menggunakan algoritma Random Forest dengan proses tuning hyperparameter melalui grid search untuk memperoleh konfigurasi optimal, yaitu n_estimators 200, max_depth 30, min_samples_split 10, dan min_samples_leaf 2. Model dilatih dan diuji menggunakan data historis dan data eksternal sebanyak 4.149 sampel, menghasilkan performa prediksi yang sangat baik dengan nilai MAE sebesar 0,6517, RMSE 1,4305, dan R-squared 0,9650. Hasil visualisasi scatter plot menunjukkan prediksi yang akurat dengan sebagian besar titik dekat garis ideal. Model ini membuktikan kemampuan generalisasi dan reliabilitas yang tinggi dalam memprediksi beban PLTU, sehingga dapat digunakan efektif dalam monitoring dan pengelolaan operasional pembangkit listrik.
Effect of Project-Based Learning Models on Motivation, Creative Thinking, Critical Thinking, and Learning Outcomes of Students in the Concrete Work Practical Course UNP Muhammad Akbar; Nurhasan syah; Muhammad Giatman; Arwizet Arwizet
Jurnal Penelitian Pendidikan IPA Vol 11 No 9 (2025): September
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i9.12326

Abstract

The study aims to analyze the effect of Project Based Learning on learning motivation, creativity, critical thinking, and learning outcomes of students in the concrete work practice course at UNP. The research method used a Quasi-Experiment (Semi-Experiment) with a pretest-posttest control group design. The results of the study obtained a p-value of 0.00 < 0.05 or tcount > ttable with a value of (9.29 > 1.70) for learning motivation, a p-value of 0.00 < 0.05 or tcount > ttable with a value of (5.36 > 1.70) on student creativity, p = 0.00 < 0.05 or tcount > ttable with a value of (6.49 > 1.70) on critical thinking, and p-value = 0.00 < 0.05 or tcount > ttable with a value of (5.06 > 1.70) and (6.44 > 1.70) on cognitive and psychomotor learning outcomes. Thus, it is concluded that there is an effect of the project-based learning model on learning motivation, creativity, critical thinking, and learning outcomes of students in the PTB class at the Faculty of Engineering, Padang State University, Academic Year 2024/2025.
Assessing the Implementation of the Pancasila Student Profile (P5) Project on Local Wisdom in Vocational High Schools: A CIPP Model Evaluation Ariska Dwi Putri; Muhammad Adri; Arwizet Arwizet; Muhammad Giatman
AL-ISHLAH: Jurnal Pendidikan Vol 18, No 1 (2026): MARCH 2026
Publisher : STAI Hubbulwathan Duri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35445/alishlah.v18i1.8466

Abstract

The Pancasila Student Profile Strengthening Project (P5), a core component of Indonesia’s Merdeka Curriculum, aims to integrate character education with contextual learning. This study evaluates the implementation of a local wisdom–themed P5 project in a Vocational High School (SMK) in West Sumatra, focusing on its effectiveness in aligning cultural values with vocational competencies.A sequential explanatory mixed-methods design was employed using the CIPP (Context, Input, Process, Product) evaluation model. Data were collected from 155 participants (principals, teachers, and students) through questionnaires, interviews, observations, and document analysis. Quantitative data were analyzed descriptively, followed by qualitative interpretation to explain key findings.The context evaluation demonstrated strong alignment with national education policies. Input analysis revealed a disparity between adequate facilities (82%) and limited teacher understanding of program management (63%). During implementation, student participation was moderate (73%), but inconsistent pedagogical facilitation hindered effectiveness. Product evaluation indicated a positive yet suboptimal impact on student character development (74%).The findings highlight a critical gap between structural readiness and human resource capacity. Although institutional support and infrastructure are sufficient, limitations in teachers’ pedagogical and managerial competencies constrain program outcomes.The effectiveness of P5 implementation depends less on infrastructure and more on strengthening teacher capacity. Policy efforts should prioritize professional development, practical mentorship, and clear technical guidelines to bridge the gap between curriculum design and classroom practice, thereby optimizing the integration of local wisdom in vocational education.
Co-Authors - Primawati Andre Kurniawan Ariska Dwi Putri Devky Meinaldi Fernanda Diki Alfarizi Muhammad Adri Muhammad Akbar Nurhasan Syah Refdinal, Refdinal Yolli Fernanda