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All Journal JURIKOM (Jurnal Riset Komputer) Indonesian Journal of Data and Science
Hendro Sutomo Ginting
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Mathematics

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Use of Machine Learning in Power Consumption Optimization of Computing Devices Rivalri Kristianto Hondro; Hendro Sutomo Ginting; Peter Jaya Negara Simanjuntak; Hanna Tresia Silalahi; Sarwandi
Indonesian Journal of Data and Science Vol. 6 No. 1 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i1.231

Abstract

Introduction: The high-power consumption of computing devices poses both economic and environmental challenges in the digital era. This study aims to optimize power usage using machine learning to maintain device performance while reducing energy costs and carbon emissions. Methods: The Random Forest algorithm was selected for its robustness in handling non-linear interactions among features. A dataset containing historical power consumption, workload metrics, environmental conditions, and hardware configurations was collected from sensors and logs. Data pre-processing included cleaning, normalization, and feature selection. The model was trained and evaluated using accuracy, precision, recall, F1-score, MAE, and RMSE metrics. Hyperparameter tuning via grid search, random search, and Bayesian optimization was applied to enhance model performance. The model was deployed on real devices to test energy optimization under varied workloads. Results: The Random Forest model achieved 92% accuracy and an RMSE of 0.15. Tuning reduced RMSE by 10% and improved F1-score from 0.875 to 0.905. Implementation on computing devices led to average power savings of 15–20% across workload scenarios without notable performance degradation (<5%). The model also projected annual carbon emission reductions of up to 5 tons of CO₂ and operational savings of $50,000 when scaled to 1,000 servers. Conclusions: Machine learning, particularly Random Forest, proves effective in optimizing power consumption on computing devices. The proposed approach not only ensures computational efficiency but also promotes environmental sustainability. These findings support further exploration of ML-based solutions for green technology initiatives in IT infrastructure.
Prediksi Harga Mobil Global Menggunakan Machine Learning dengan Algoritma Naive Bayes Hts, Dedek Indra Gunawan; Firman Edi; Ratna Sri Hayati; Hendro Sutomo Ginting
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 6 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i6.9320

Abstract

Determining car prices is one of the major challenges in the global automotive industry because it is influenced by various factors such as technical specifications, vehicle condition, and market dynamics. This issue becomes more complex as the volume of available data increases, requiring methods capable of performing fast and accurate analysis. This study aims to predict car price levels based on vehicle specifications using a Machine Learning approach, with the Naive Bayes algorithm selected as a solution to simplify the price classification process on large-scale data. The dataset used is the Global Car Sales Analysis from the Kaggle platform, which includes attributes such as Manufacturer, Model, Engine size, Fuel type, Year of manufacture, Mileage, and Price. The research methodology consists of data preprocessing, label encoding for categorical attributes, splitting the dataset into training and testing sets, and applying the Naive Bayes algorithm to classify car prices into three categories: Low, Medium, and High. The results indicate that Naive Bayes is capable of predicting car prices with very strong performance, achieving an accuracy of 96%, precision of 0.97, recall of 0.96, and an F1-score of 0.96. The model performs best on the Low category with an F1-score of 0.98, although performance decreases for the Medium and High categories due to imbalanced class distribution. Further analysis also reveals that Engine size, Year of manufacture, and Mileage are the most influential attributes in determining price. Overall, this study demonstrates that Naive Bayes is an effective method for predicting car prices using global automotive data.
Co-Authors Firman Edi Hanna Tresia Silalahi Hts, Dedek Indra Gunawan Peter Jaya Negara Simanjuntak Ratna Sri Hayati Rivalri Kristianto Hondro Sarwandi