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All Journal ILKOM Jurnal Ilmiah Advance Sustainable Science, Engineering and Technology (ASSET)
Satriawan Rasyid Purnama
Universitas Diponegoro
Chemistry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Separable Convolutional Hierarchical Decomposition for Lightweight Residential Load Forecasting in Smart Grids Satriawan Rasyid Purnama; Henri Tantyoko; Adi Wibowo; Yesaya Rudolf Susanto Widyanto
Advance Sustainable Science Engineering and Technology Vol. 8 No. 3 (2026): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i3.2869

Abstract

Residential load forecasting is essential for maintaining grid stability and energy management in smart grids. However, achieving accurate real-time forecasting under resource constraints remains challenging because Transformer and LSTM models can be computationally demanding, while lightweight linear models such as DLinear have limited modeling flexibility. This study investigates whether a hierarchical separable convolutional framework can provide accurate and efficient residential load forecasting. To address this, SeparableCLF, a lightweight hierarchical decomposition model using depthwise separable convolution, is proposed and evaluated on hourly OpenEI residential load data from 20 U.S. states (2012) at forecast horizons of 6, 12, 24, 48, and 96 h. Relative to DLinear, SeparableCLF reduced MAPE by 0.93, 1.54, and 0.79 percentage points at 24, 48, and 96 h, respectively while requiring substantially fewer parameters than Transformer and LSTM models.SeparableCLF achieved the lowest MAPE at 12, 24, and 48 h. DLinear achieved the lowest errors at 6 h, whereas LSTM achieved the lowest MAPE at 96 h; at 96 h, SeparableCLF retained the lowest MAE, MSE, and RMSE among the compared models, indicating suitability for real-time forecasting on smart meters and edge-based smart grid devices.
Multi-Horizon Short-Term Residential Load Forecasting Using Decomposition-Based Linear Neural Network Henri Tantyoko; Satriawan Rasyid Purnama; Etna Vianita
Advance Sustainable Science Engineering and Technology Vol. 7 No. 3 (2025): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v7i3.2033

Abstract

Short-Term Load Forecasting is crucial for grid stability and real-time energy management, particularly in residential settings where consumption is highly volatile and influenced by behavioral and external factors. Traditional models struggle to capture complex, non-linear patterns. This study proposes a forecasting framework based on the DLinear model, which decomposes time series data into trend and seasonal components using a simple linear neural network architecture. Designed for multi-horizon forecasting, the model predicts electricity demand across several future time points simultaneously. Experimental results show that DLinear performs best at a 24-hour prediction length, achieving the lowest MSE of 41.58 and MAE of 5.11, indicating improved accuracy with longer horizons. These results confirm DLinear’s robustness and efficiency in modeling dynamic residential electricity consumption patterns.
Feature Importance–Driven Multimodal Learning for Medical Diagnosis Classification Using Clinical and Symptom Text Data Indra Waspada; Satriawan Rasyid Purnama; Alfonso Clement Sutantio; Alwey Hakim
ILKOM Jurnal Ilmiah Vol 18, No 2 (2026)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v18i2.3289.255-267

Abstract

The integration of structured clinical measurements and unstructured textual symptom descriptions poses persistent challenges for automated medical diagnosis, particularly due to feature heterogeneity and class imbalance in real-world outpatient data. This study proposes a feature importance–driven multimodal machine learning framework for multi-class medical diagnosis classification that jointly models numerical clinical attributes and free-text symptom narratives within a unified pipeline. Beyond overall performance comparison, the proposed approach systematically examines the interaction between model architecture and feature importance through three controlled configurations: base, strong-feature, and weak-feature settings. Six supervised learning algorithms are evaluated using stratified five-fold cross-validation and imbalance-aware metrics. The results show that feature importance–driven modeling yields strongly model-dependent performance characteristics. Bagging-based tree ensembles benefit most from strong-feature selection, with the Extra Trees classifier achieving the best overall performance, reaching a macro-averaged F1-score of 0.821, compared to 0.811 in the base configuration and 0.642 when only weak features are retained. Conversely, margin-based classifiers rely on distributed feature representations. The kernel-based Support Vector Machine performs poorly under strong-feature selection (F1-score 0.450) but achieves a substantially higher F1-score of 0.795 and a macro-averaged recall of 0.808 under the weak-feature configuration. Linear SVM demonstrates stable behavior across configurations, maintaining a macro-averaged F1-score between 0.798 and 0.810, while attaining the highest overall recall of 0.819. These findings indicate that feature importance should be treated as a model-aware analytical tool for aligning feature selection strategies with the inductive bias of the learning algorithm, supporting robust and clinically meaningful diagnostic classification.
Co-Authors Adi Wibowo Alfonso Clement Sutantio Alwey Hakim Etna Vianita Henri Tantyoko Indra Waspada Yesaya Rudolf Susanto Widyanto