Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Jambura Journal of Electrical and Electronics Engineering Jurnal Teknik Elektro dan Komputasi (ELKOM)
Edris Shamsulhaq
Nusa Putra University
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Unsupervised Hybrid Deep Learning for Unknown Bearing Fault Diagnosis and Severity Assessment Edris Shamsulhaq; Fikri Arif Wicaksana
Jambura Journal of Electrical and Electronics Engineering Vol 8, No 2 (2026): Juli - Desember 2026
Publisher : Electrical Engineering Department Faculty of Engineering State University of Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjeee.v8i2.39120

Abstract

This paper presents an unsupervised hybrid deep learning framework for unknown bearing fault diagnosis and severity assessment using vibration signals. The proposed framework combines Continuous Wavelet Transform (CWT), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) autoencoders. Trained exclusively on 3,779 healthy data segments, the model detects anomalies via reconstruction error analysis evaluated across a total of 7,585 test segments. Experiments on the CWRU dataset show that the proposed hybrid model achieves competitive performance (AUC 0.990, accuracy 90.42%, and F1-score 89.57%) compared to spectral baselines, while uniquely preserving temporal dynamics—a critical advantage for non-stationary industrial environments. However, outer race faults were not reliably detected under the global threshold, which we report as a key limitation. A severity assessment and Health Index are also introduced for interpretable predictive maintenance.
Household Energy Consumption Prediction Using Ensemble Machine Learning Models: A Comparative Analysis Edris Shamsulhaq; Fikri Arif Wicaksana
Jurnal Teknik Elektro dan Komputasi (ELKOM) Vol. 8 No. 2 (2026): Jurnal Teknik Elektro dan Komputasi (ELKOM)
Publisher : Universitas Muhammadiyah Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32528/elkom.v8i2.6418

Abstract

Accurate prediction of household energy consumption plays a vital role in smart grid management, demand-side response, and energy efficiency planning. This study presents a comparative analysis of four ensemble machine learning models—Random Forest, XGBoost, LightGBM, and CatBoost—for predicting household active power consumption. The publicly available Household Electric Power Consumption dataset from the UCI repository, comprising over two million measurements, was used for experimentation. Three feature scenarios were designed to evaluate model performance under different input conditions: using all features, excluding current intensity, and excluding both intensity and reactive power. Results show that Random Forest achieved the highest predictive accuracy with an R² of 0.913 in the reduced-feature scenario, outperforming gradient boosting alternatives. Statistical validation using the Wilcoxon signed-rank test was used to assess the effect of feature exclusion across the three scenarios. The study demonstrates that reliable energy prediction remains achievable using sub-metering, voltage, and temporal features, offering a practical baseline for smart meter applications.
Co-Authors Arif Fikri Wicaksana