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Jurnal E-Komtek
Published by Politeknik Piksi Ganesha Indonesia
ISSN : 25803719     EISSN : 26223066     DOI : https://doi.org/10.37339/e-komtek.v4i2.269
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Jurnal E-Komtek (Elektro-Komputer-Teknik) is a Journal that contains scientific articles in the form of research results, analytical studies, application of theory, and discussion of various problems relating to Electrical, Computer, and Automotive Mechanical Engineering.
Arjuna Subject : Ilmu Komputer (semua kategori) - Visi Komputer dan Pengenalan Pola
Articles 32 Documents
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Search results for , issue "Vol 9 No 2 (2025)" : 32 Documents clear
Causal Modeling of Factors Causing Toddler Stunting Using the Peter-Clark Algorithm Tou, Nurhaeka; Endraswari, Putri Mentari; Iftizam, Syafiranur
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.v9i2.2692

Abstract

Stunting is a growth disorder in toddlers that can result in a height that is not proportionate to their age. Between 2016 and 2024, many studies have discussed factors related to stunting. However, these studies generally only use correlation analysis, which indicates the level of closeness of the relationship between variables. Correlation analysis can indeed describe the existence of an association, but cannot explain the causal relationship. Therefore, the causal mechanisms underlying these factors have not been fully revealed. This study aims to model the causal relationship of the factors that cause stunting. This study uses the Peter-Clark algorithm to obtain the direction of the causal relationship. The results of this study show a relationship between Height(TB.U)/Weight(BB.U), Integrated Health Post Visits/Height(TB.U), Height(TB.U)/Mother's Education, Age of Marriage/Mother's Education, Immunization/Family Members Smoking in the Home, and Exclusive Breastfeeding/ Integrated Health Post Visits. The Peter-Clark algorithm in this study successfully identified a causal relationship based on a comparison of performance using directional and causal density of 67%. These results are quite informative, but 34% of the relationships remain undirected. Therefore, additional data, domain assumptions, or advanced algorithms, such as FCI or GES, are needed to determine their direction.
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.

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