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All Journal Jurnal E-Komtek JURPIKAT (Jurnal Pengabdian Kepada Masyarakat)
Indrianto
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Computer Science & IT Dentistry Education Electrical & Electronics Engineering Law, Crime, Criminology & Criminal Justice Public Health Social Sciences

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Journal : Jurnal E-Komtek

 1 
Application of Villamil-Molina in Virtual Reality Environmental Information on The ITPLN Campus Herman Bedi; Abdurrasyid; Meilia Nur Indah Susanti; Indrianto; M Yoga Distra Sudirman; Rahma Farah Ningrum; Yessy Asri; Joey Andrew Fransisco Sihombing; Azizah Arip Rambe
Jurnal E-Komtek (Elektro-Komputer-Teknik) Vol 8 No 1 (2024)
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/e-komtek.v8i1.1700

Abstract

This study aims to apply Virtual Reality (VR) as an information medium in the ITPLN campus environment, focusing on the Villamil-Molina analysis method. The 3DVista app will be used as a VR content development platform. The research will evaluate accessibility, educational effectiveness, user acceptance, content availability, financial aspects, security, privacy, and environmental impact. The study results are expected to provide an in-depth understanding of the potential of VR, identify obstacles, and present recommendations to improve its implementation in the ITPLN campus environment by utilizing 3DVista applications. Villamil-Molina follows structured steps, uses the right creativity, and provides a satisfying experience for users.
Support Vector Regression–Based Model for Multizone Electricity Consumption Forecasting Nabila Farida; Indrianto
Jurnal E-Komtek (Elektro-Komputer-Teknik) Vol 9 No 2 (2025)
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/e-komtek.v9i2.3028

Abstract

This study aims to develop and evaluate a multi-zone short-term electricity consumption prediction model based on weather factors using the Support Vector Regression (SVR) method to support more efficient and adaptive power system planning in response to climate variability. Ten-minute resolution electricity consumption data from three zones was combined with variables of air temperature, relative humidity, wind speed, and solar radiation. The research process included data preprocessing, temporal feature engineering, time-based data partitioning, and SVR hyperparameter optimization with RBF kernel. Model performance was evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R²) metrics, and compared with reference models such as linear regression, Random Forest Regression, and Artificial Neural Network. The results of the experiment show that SVR provides the best accuracy at high temporal resolution. At 10-minute aggregation, the MAPE values obtained were 4.78% (Zone 1), 4.11% (Zone 2), and 9.25% (Zone 3). Model performance declines as the level of time aggregation increases, indicating that the effectiveness of SVR is influenced by the temporal scale and load characteristics of each zone. These findings show that SVR works effectively for weather-based electricity load forecasting in various zones at high temporal resolution, although its performance declines at larger aggregation scales. Keywords: Multizone electric power consumption; Short-term load forecasting; Weather/meteorological factors; Support Vector Regression (SVR); Intelligent energy management systems.
Co-Authors Abdurrasyid Azizah Arip Rambe Dewi Arianti Wulandari Herman Bedi Herman Bedi Agtriadi, Herman Bedi Joey Andrew Fransisco Sihombing M Yoga Distra Sudirman Meilia Nur Indah Susanti Nabila Farida Ningrum, Rahma Farah Rahmad Evan Yessy Asri