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All Journal Jurnal ULTIMATICS JURPIKAT (Jurnal Pengabdian Kepada Masyarakat)
Krisna Aditya
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Computer Science & IT Control & Systems Engineering Dentistry Education Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Public Health Social Sciences

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Pelatihan Strategi Komunikasi dalam Pengamalan Nilai-Nilai Kebangsaan melalui Komunikasi Keluarga Dini Nur Fadhillah; Suhaeri; Fadli Tri Hartono; Diny Fitriawati; Cevi Mochamad Taufik; Didin Sabarudin; Ira Lusiawati; Gita Eka Sila; Rizqi Ghassani; Krisna Aditya
JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Vol. 6 No. 2 (2025)
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/jurpikat.v6i2.2458

Abstract

The ever-evolving digital media has had an impact on the dynamics of national awareness among the younger generation today, such as a weakening understanding and practice of Pancasila and low community resilience to the influence of globalisation. Therefore, a strategy is needed to practise national values. This community service activity aims to conduct family communication training as a strategy to practise national values. This activity employs a Community-Based Participatory Research (CBPR) approach. The results of this community service activity indicate that family communication in promoting national values can be developed through a combination of conformity orientation and conversation orientation. Thus, the family communication skills developed can be balanced with actions that foster mutual benefit among families and the community.
Deep Learning-Based Energy Consumption Optimization in Smart Home Systems An LSTM Approach with SHAP Interpretability Mhd. Basri; Krisna Aditya; Andi Zulherry
ULTIMATICS Vol 18 No 1 (2026): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v18i1.4691

Abstract

Accurate and easy-to-understand energy consumption forecasting is a prerequisite for effective demand-side management in smart home systems. This study proposes an integrated framework that combines a stacked Long Short-Term Memory (LSTM) network with SHapley Additive exPlanations (SHAP) for hourly residential energy forecasting and the extraction of optimization insights. The model was evaluated on the UCI Individual Household Electric Power Consumption (IHEPC) dataset—34,589 hourly observations resampled from over two million sensor records per minute—and tested against Stacked GRU, Vanilla RNN, and ARIMA(2,1,2). All deep learning models significantly outperformed ARIMA (R² = −0.1258, MAPE = 126.92%). The proposed LSTM model achieved MAE = 0.3208 kW, RMSE = 0.4568 kW, and R² = 0.5749, while Stacked GRU recorded the best aggregate metrics (MAE = 0.3090 kW, R² = 0.5801). SHAP analysis identified Global Intensity as the dominant predictor (mean |SHAP| = 0.00350), based on the electrophysical relationship P = V × I, followed by the 3-hour Rolling Mean and seasonal encoding. Further temporal attribution analysis revealed that predictive value is concentrated in the most recent input time steps, confirming that real-time current measurements are the most valuable input for HEMS implementation.
Co-Authors Andi Zulherry Didin Sabarudin Diny Fitriawati, Diny Fadhillah, Dini Nur Fadli Tri Hartono Gita Eka Sila Lusiawati, Ira Mhd. Basri Rizqi Ghassani Suhaeri Taufik, Cevi Mochamad