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All Journal Journal of Artificial Intelligence and Engineering Applications (JAIEA) Journal of Technology Informatics and Engineering
Panjaitan, Haposan Daniel
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Automotive Engineering Computer Science & IT Control & Systems Engineering

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Optimization of Smart Home Energy Consumption Using Machine Learning-Based Load Forecasting Rudiyanto, Arif Rifan; Satria, Bagas Panji ; Panjaitan, Haposan Daniel
Journal of Technology Informatics and Engineering Vol. 4 No. 2 (2025): AUGUST | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v4i2.437

Abstract

The growing demand for energy efficiency in smart homes necessitates accurate short-term load forecasting to enable adaptive scheduling and optimal resource allocation. Traditional forecasting models, such as Random Forest, have demonstrated limited capability in capturing sequential dependencies, especially under fluctuating consumption behaviors typical of residential environments. This study aims to compare the forecasting performance of RF and Long Short-Term Memory (LSTM) models in predicting household energy consumption, to identify the most suitable approach for intelligent energy management systems. A quantitative experimental design was adopted using a publicly available dataset, which underwent preprocessing including time normalization and unit conversion. Both models were evaluated using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) to assess forecasting accuracy. The LSTM model achieved a lower MAE of 3.2 and RMSE of 4.1, significantly outperforming the RF model, which recorded an MAE of 6.5 and RMSE of 8.4. Additionally, during peak load conditions, LSTM achieved 89.7% accuracy, compared to 72.4% for RF, further emphasizing its superior adaptability to time-sensitive variations. The results confirm that LSTM is more effective in modeling temporal patterns and handling volatility in household energy usage. This research contributes to the field by reinforcing the applicability of deep learning for real-time energy forecasting, offering valuable insights for the development of smart home systems. Future studies may expand this work by integrating hybrid optimization techniques and exploring multi-household scenarios for broader scalability.
Application of the ANFIS Method to Predict Satisfaction with Facilities and Infrastructure Turnip, Mardi; Priambodo, Ganang Reza; Sihaloho, Theresia Delima; Ndruru, Jonathan Haris P.; Sigalingging, Josepta; Salsabillah; Panjaitan, Haposan Daniel
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 3 No. 1 (2023): October 2023
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v3i1.283

Abstract

Facilities and infrastructure are all movable or immovable objects or objects that are used to support every aspect of human life. Students, lecturers and office workers at least spend about half of their active hours at work. Therefore it is very important to pay attention to the high level of comfort, security, completeness in a building. There fore we need a way to predict satisfaction with facilities and infrastructure. To provide solutions to existing problems, the authors create applications that can predict the satisfaction of facilities and infrastructure. In this article, a satisfaction prediction approach based on a data-driven technique, representing system behavior using the Takagi-Sugeno model is developed. The Adaptive Neuro Fuzzy Inference System method is used to build a predictive model. The research was conducted by interview, observation and literature study. Data were taken from 92 respondents consisting of lecturers, students, and staff/employees in the research area. The test results using this method showed satisfactory results, indicating a success rate with an accuracy of 97.2%.
Co-Authors ARIF RIFAN RUDIYANTO, ARIF RIFAN Mardi Turnip, Mardi Ndruru, Jonathan Haris P. Priambodo, Ganang Reza Salsabillah Satria, Bagas Panji  Sigalingging, Josepta Sihaloho, Theresia Delima