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All Journal JURIKOM (Jurnal Riset Komputer) Jurnal E-Komtek
Elindra Ambar Pambudi
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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An Analysis and Forecasting of Electricity Demand Using the Triple Exponential Smoothing Method Aulia Nur Aini; Hakim, Dimara Kusuma; Feri Wibowo; Elindra Ambar Pambudi
Jurnal E-Komtek (Elektro-Komputer-Teknik) Vol 9 No 1 (2025)
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/e-komtek.v9i1.2483

Abstract

Electricity is a basic necessity required in daily life, supporting various activities, including economic development. The growing demand for electricity requires reliable and efficient planning and management of the power system. Electricity demand forecasting is essential due to its fluctuating nature and seasonal patterns. This study aims to forecast electricity demand using the Triple Exponential Smoothing method with data from the Australian Energy Market Operator (AEMO) for the New South Wales region, Australia, covering the period from January 2015 to February 2025. This method is chosen because it effectively handles time series data patterns consisting of level, trend, and seasonal components. The forecasting results show that this method is capable of closely following the actual data patterns and produces a Mean Absolute Percentage Error (MAPE) of 2.89%, indicating a very good performance. This model is expected to serve as a basis for decision-making in anticipating future fluctuations in electricity demand.
Forecasting Water Pollution in Cengklik Reservoir Using Triple Exponential Smoothing Method Nooriza Modistira Sakti; Hakim, Dimara Kusuma; Elindra Ambar Pambudi; Maulida Ayu Fitriani
Jurnal E-Komtek (Elektro-Komputer-Teknik) Vol 9 No 1 (2025)
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/e-komtek.v9i1.2414

Abstract

Water quality is a crucial element for the sustainability of ecosystems and human life, yet it is often threatened by pollution resulting from human activities. Cengklik Reservoir in Boyolali Regency has shown increasing levels of pollution influenced by domestic waste, agricultural fertilizers, and residual fish feed from Floating Net Cages (KJA). This study aims to predict water pollution levels to support more effective management efforts by applying the Triple Exponential Smoothing (TES) method to pollution index data from 2016 to 2023. The forecasting results reveal a clear seasonal pattern, with a Mean Absolute Percentage Error (MAPE) of 34.36%, indicating a moderately good level of accuracy. These findings suggest that TES is capable of identifying general pollution patterns, although further approaches are needed to fully capture the dynamics of water pollution. As a follow-up, the study recommends optimizing the number and placement of KJA units, improving waste management, and implementing community education programs to preserve water quality and ensure the sustainability of the reservoir ecosystem.
Penggunaan Algoritma Stacking Classifier Pada Sistem Deteksi Risiko Kardiovaskular Imam Bari Setiawan; Maulida Ayu Fitriani; Elindra Ambar Pambudi; Muhammad Hamka
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.v12i6.9402

Abstract

Cardiovascular disease is a leading cause of global death. However, the complexity of medical data often makes conventional models fail to capture hidden patterns, resulting in suboptimal predictive performance. This study evaluates the effectiveness of a hybrid model that integrates K-Modes Clustering with the Stacking Classifier algorithm and tests whether the model's complexity can provide significant performance improvements compared to a single model. The methodology involves data preprocessing including outlier handling, clinical feature engineering, and cluster feature extraction using K-Modes (K=2). The Stacking Classifier architecture is built using five optimized heterogeneous base-learners (CatBoost, Decision Tree, MLP, SVC, Logistic Regression) and XGBoost as a meta-learner, validated through Stratified 5-Fold Cross-Validation. The results showed that although K-Modes effectively mapped clinically valid risk categories, the Stacking Classifier model (87.99% accuracy and 95.89% ROC-AUC) was not able to surpass the performance of the best single model, namely CatBoost (88.03% accuracy and 95.90% ROC-AUC). The most significant finding lies in the computational time efficiency, where the Stacking Classifier algorithm required 560 times longer computational time (7587.7686 seconds) than CatBoost (13.4635 seconds) without providing a commensurate performance improvement. This indicates that Boosting-based algorithms are able to capture complex patterns without requiring additional ensemble layers, so that an optimized single model is more recommended for real-world implementations by providing the best balance between prediction accuracy and computational time efficiency.
Co-Authors Aulia Nur Aini Dimara Kusuma Hakim Feri Wibowo Imam Bari Setiawan Maulida Ayu Fitriani Muhammad Hamka Nooriza Modistira Sakti