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All Journal Jurnal Masyarakat Informatika TEKNOLOGIA Jurnal Teknologi Informasi dan Multimedia Idealis : Indonesia Journal Information System JUSTIN (Jurnal Sistem dan Teknologi Informasi) IJCOSIN : Indonesian Journal of Community Service and Innovation Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) Buffer Informatika Advance Sustainable Science, Engineering and Technology (ASSET)
Henri Tantyoko
Department Of Informatics, Universitas Diponegoro |Universitas Diponegoro
Agriculture, Biological Sciences & Forestry Automotive Engineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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A Comparative Study of Machine Learning Models for Short-Term Load Forecasting Etna Vianita; Henri Tantyoko
Jurnal Masyarakat Informatika Vol 16, No 1 (2025): May 2025
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.16.1.73130

Abstract

Short-Term Load Forecasting (STLF) was a critical task in power system operations, enabling efficient energy management and planning. This study presented a comparative analysis of five machine learning models namely XGBoost, Random Forest, Multi-Layer Perceptron (MLP), Support Vector Regression (SVR), and LightGBM using real-world electricity demand data collected over a four-month period. Two modeling approaches were explored: one using only time-based features (hour, day of the week, month), and another incorporating historical lag features (lag_1, lag_2, lag_3) to capture temporal patterns. The results showed that MLP with lag features achieved the best performance (RMSE: 57.63, MAE: 34.54, MAPE: 0.22), highlighting its ability to model nonlinear and sequential dependencies. In contrast, SVR and LightGBM experienced performance degradation when lag features were added, suggesting sensitivity to feature dimensionality and data volume. These findings emphasized the importance of model-feature alignment and temporal context in improving forecasting accuracy. Future work could explore the integration of external variables such as weather and holidays, as well as the application of advanced deep learning architectures like LSTM or hybrid models to further enhance robustness and generalizability.
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.
Klasifikasi Produktivitas Pekerja Garmen Menggunakan Algoritma Random Forest: Classification of Garment Worker Productivity Using Random Forest Algorithm Luthfi Rakan Nabila; Fiqki Haidar Amrulloh; Ghilman Farhani Putra Aji; Rio Ghaniy Septiansyah; Vincentius Sagi Alban Anindyajati; Henri Tantyoko
Buffer Informatika Vol. 10 No. 1 (2024): Buffer Informatika
Publisher : Department of Informatics Engineering, Faculty of Computer Science, University of Kuningan, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/buffer.v10i1.111

Abstract

Penelitian ini membahas mengenai penerapan algoritma machine learning dalam melakukan klasifikasi produktivitas pekerja garmen. Penelitian ini menggunakan dataset produktivitas garmen yang didapatkan dari situs UC Irvine Machine Learning Repository dengan rentang waktu dari tanggal 1 januari 2015 sampai 11 maret 2015 dengan total data sebanyak 1197 baris. Algoritma yang diterapkan pada penelitian ini adalah random forest dengan hyperparameter tuning untuk melakukan klasifikasi produktivitas pekerja garmen. Metodologi penelitian ini melibatkan pengolahan data seperti pemilihan fitur yang relevan, transformasi data, dan normalisasi guna mendapatkan hasil evaluasi terbaik. pada penelitian ini juga dilakukan percobaan dengan decision tree dan algoritma svm sebagai pembandingnya. Algoritma random forest mengungguli algoritma lain dengan akurasi sebesar 94.36% di mana akurasi tersebut sudah cukup bagus dalam untuk mengklasifikasi produktivitas
Co-Authors Adi Wibowo Aditya Dwi Putro Adiwijaya Andreas Rony Wijaya Anggi Zafia Aris Sugiharto Aulia Desy Nur Utomo Ayuningtyas, Puji Bita Parga Zen Dade Nurjanah Dian Kartika Sari, Dian Kartika Dimas Fanny Hebrasianto Permadi Etna Vianita Etna Vianita Etna Vianita Fiqki Haidar Amrulloh Ghilman Farhani Putra Aji Latief, Muhammad Abdul Luthfi Rakan Nabila Ma'rufatullah, Saihun Miftakhurrahman, Wildan Muhamad Nur Baihaqi Muhammad Abdul Latief Nabila, Luthfi Rakan Rio Ghaniy Septiansyah Saihun Ma'rufatullah Satriawan Rasyid Purnama Untari Novia Wisesty Vincentius Sagi Alban Anindyajati Wildan Miftakhurrahman Yanti Rusmawati Yesaya Rudolf Susanto Widyanto Yoso Adi Setyoko